From 662053d2d003d491e68ecd6b02d48ed27cbcabed Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Fri, 26 Dec 2025 21:18:42 -0800
Subject: [PATCH 01/24] Add LangGraph Hello World sample

- Add langgraph_samples/basic/hello_world/ demonstrating basic
  Temporal + LangGraph integration with a single-node graph
- Add langgraph dependency group to pyproject.toml
- Update langchain dependency group to 0.3.x for compatibility
  with langgraph (requires langchain-core>=0.2.27)
---
 langgraph_samples/README.md                   |   24 +
 langgraph_samples/SAMPLES_PROPOSAL.md         |  349 ++
 langgraph_samples/__init__.py                 |    1 +
 langgraph_samples/basic/README.md             |   18 +
 langgraph_samples/basic/__init__.py           |    1 +
 langgraph_samples/basic/hello_world/README.md |   36 +
 .../basic/hello_world/__init__.py             |    1 +
 langgraph_samples/basic/hello_world/graph.py  |   60 +
 .../basic/hello_world/run_worker.py           |   39 +
 .../basic/hello_world/run_workflow.py         |   30 +
 .../basic/hello_world/workflow.py             |   43 +
 pyproject.toml                                |   13 +-
 uv.lock                                       | 4584 +++++++++--------
 13 files changed, 3020 insertions(+), 2179 deletions(-)
 create mode 100644 langgraph_samples/README.md
 create mode 100644 langgraph_samples/SAMPLES_PROPOSAL.md
 create mode 100644 langgraph_samples/__init__.py
 create mode 100644 langgraph_samples/basic/README.md
 create mode 100644 langgraph_samples/basic/__init__.py
 create mode 100644 langgraph_samples/basic/hello_world/README.md
 create mode 100644 langgraph_samples/basic/hello_world/__init__.py
 create mode 100644 langgraph_samples/basic/hello_world/graph.py
 create mode 100644 langgraph_samples/basic/hello_world/run_worker.py
 create mode 100644 langgraph_samples/basic/hello_world/run_workflow.py
 create mode 100644 langgraph_samples/basic/hello_world/workflow.py

diff --git a/langgraph_samples/README.md b/langgraph_samples/README.md
new file mode 100644
index 00000000..69ad2d33
--- /dev/null
+++ b/langgraph_samples/README.md
@@ -0,0 +1,24 @@
+# Temporal LangGraph Samples
+
+These samples demonstrate the [Temporal LangGraph integration](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/langgraph/README.md) - combining LangGraph's agent framework with Temporal's durable execution.
+
+See the [module documentation](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/langgraph/README.md) for more information.
+
+## Overview
+
+The integration combines:
+- **Temporal workflows** for orchestrating agent control flow and state management
+- **LangGraph** for defining agent graphs with conditional logic, cycles, and state
+
+This approach ensures that AI agent workflows are durable, observable, and can handle failures gracefully.
+
+## Prerequisites
+
+- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
+- Required dependencies installed via `uv sync --group langgraph`
+
+## Examples
+
+Each directory contains complete examples with their own README for detailed instructions:
+
+- **[Basic Examples](./basic/README.md)** - Simple examples including a hello world agent demonstrating basic plugin setup and graph registration.
diff --git a/langgraph_samples/SAMPLES_PROPOSAL.md b/langgraph_samples/SAMPLES_PROPOSAL.md
new file mode 100644
index 00000000..27351a1e
--- /dev/null
+++ b/langgraph_samples/SAMPLES_PROPOSAL.md
@@ -0,0 +1,349 @@
+# Temporal LangGraph Samples Proposal
+
+This document proposes sample applications demonstrating the [Temporal LangGraph integration](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/langgraph/README.md) - combining LangGraph's agent framework with Temporal's durable execution.
+
+## Design Principles
+
+Each sample should demonstrate:
+1. **Durability** - Agents that survive failures and can resume from checkpoints
+2. **Production Readiness** - Proper timeout/retry configuration, error handling
+3. **Temporal Features** - Leveraging signals, queries, child workflows where appropriate
+
+Samples are organized by complexity and use case, inspired by [OpenAI Agents samples](../openai_agents/README.md) structure.
+
+---
+
+## Proposed Samples
+
+### 1. Basic Examples (`basic/`)
+
+#### 1.1 Hello World Agent
+**Description:** Minimal LangGraph agent with Temporal integration. Single-node graph that processes a query.
+
+**Demonstrates:**
+- Basic plugin setup and graph registration
+- Simple workflow invocation
+- Activity-based node execution
+
+**Original Reference:** N/A (custom introductory example)
+
+---
+
+#### 1.2 ReAct Agent with Tools
+**Description:** Classic ReAct (Reasoning + Acting) agent that can use tools to answer questions. Implements the think-act-observe loop.
+
+**Demonstrates:**
+- `temporal_model()` for durable LLM calls
+- `temporal_tool()` for durable tool execution
+- `create_react_agent` integration
+- Automatic retries on LLM/tool failures
+
+**Original Reference:** [LangGraph ReAct Agent Template](https://github.com/langchain-ai/react-agent)
+
+---
+
+### 2. Human-in-the-Loop (`human_in_loop/`)
+
+#### 2.1 Approval Workflow
+**Description:** Agent that pauses for human approval before taking actions. Uses LangGraph's `interrupt()` with Temporal signals.
+
+**Demonstrates:**
+- `interrupt()` for pausing execution
+- Temporal signals for receiving human input
+- Workflow queries for checking pending approvals
+- Timeout handling for approval deadlines
+
+**Original Reference:** [LangGraph Human-in-the-Loop Guide](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/)
+
+---
+
+#### 2.2 Document Review Agent
+**Description:** Agent that drafts documents and requests human review/edits before finalizing. Supports iterative refinement cycles.
+
+**Demonstrates:**
+- Multi-step interrupt/resume cycles
+- State preservation across interrupts
+- Combining AI drafting with human editing
+
+**Original Reference:** [LangGraph Human Feedback Discussion](https://github.com/langchain-ai/langgraph/discussions/1137)
+
+---
+
+### 3. Multi-Agent Systems (`multi_agent/`)
+
+#### 3.1 Supervisor Agent
+**Description:** Supervisor that coordinates specialized worker agents (researcher, writer, critic). The supervisor routes tasks to appropriate agents and aggregates results.
+
+**Demonstrates:**
+- Multi-agent orchestration patterns
+- Supervisor decision-making logic
+- Agent handoffs and communication
+- Parallel agent execution with `Send`
+
+**Original Reference:** [LangGraph Supervisor Library](https://github.com/langchain-ai/langgraph-supervisor-py) | [Multi-Agent Blog Post](https://blog.langchain.com/langgraph-multi-agent-workflows/)
+
+---
+
+#### 3.2 Hierarchical Agent Teams
+**Description:** Two-level hierarchy with a top supervisor managing team supervisors (research team + writing team), each with their own worker agents.
+
+**Demonstrates:**
+- Child workflows for team isolation
+- Hierarchical agent coordination
+- Complex routing logic
+- Cross-team communication
+
+**Original Reference:** [LangGraph Hierarchical Agent Teams Tutorial](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/)
+
+---
+
+### 4. RAG and Research (`rag/`)
+
+#### 4.1 Agentic RAG
+**Description:** RAG agent that decides when to retrieve documents vs. respond directly. Includes document relevance grading and query rewriting.
+
+**Demonstrates:**
+- Conditional retrieval decisions
+- Document grading nodes
+- Query rewriting for better retrieval
+- Combining retrieval with generation
+
+**Original Reference:** [LangGraph Agentic RAG Tutorial](https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_agentic_rag.ipynb) | [Agentic RAG Docs](https://docs.langchain.com/oss/python/langgraph/agentic-rag)
+
+---
+
+#### 4.2 Corrective RAG
+**Description:** Self-correcting RAG that validates retrieved documents and falls back to web search when needed. Includes hallucination detection.
+
+**Demonstrates:**
+- Document relevance validation
+- Fallback to web search
+- Self-correction loops
+- Multiple retrieval strategies
+
+**Original Reference:** [Corrective RAG Guide](https://www.analyticsvidhya.com/blog/2024/07/building-agentic-rag-systems-with-langgraph/)
+
+---
+
+#### 4.3 Deep Research Agent
+**Description:** Multi-step research agent that performs iterative web searches, evaluates results, and synthesizes findings into a comprehensive report.
+
+**Demonstrates:**
+- Long-running research workflows (minutes to hours)
+- Parallel search execution
+- Result evaluation and iteration
+- Continue-as-new for large histories
+- Report generation
+
+**Original Reference:** [LangGraph Deep Research Agent](https://towardsdatascience.com/langgraph-101-lets-build-a-deep-research-agent/) | [Exa Research System](https://blog.langchain.com/exa/)
+
+---
+
+### 5. Customer Service (`customer_service/`)
+
+#### 5.1 Customer Support Agent
+**Description:** Interactive customer service agent that handles inquiries, accesses knowledge bases, and escalates to humans when needed.
+
+**Demonstrates:**
+- Conversational state management
+- Knowledge base integration
+- Escalation workflows with `interrupt()`
+- Session continuity across interactions
+
+**Original Reference:** [LangGraph Customer Support Tutorial](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/customer-support/customer-support.ipynb)
+
+---
+
+#### 5.2 Email Triage Agent
+**Description:** Agent that processes incoming emails, categorizes them, drafts responses using RAG for policy questions, and routes to appropriate handlers.
+
+**Demonstrates:**
+- Email classification
+- RAG for policy/knowledge lookup
+- Conditional routing based on email type
+- Draft review before sending
+
+**Original Reference:** [Agentic RAG Customer Support](https://blog.lancedb.com/agentic-rag-using-langgraph-building-a-simple-customer-support-autonomous-agent/)
+
+---
+
+### 6. Planning and Execution (`planning/`)
+
+#### 6.1 Plan-and-Execute Agent
+**Description:** Agent that first creates a plan, then executes steps sequentially, with the ability to replan based on results.
+
+**Demonstrates:**
+- Separation of planning and execution
+- Dynamic replanning
+- Step-by-step execution with checkpointing
+- Plan validation
+
+**Original Reference:** [LangGraph Plan-and-Execute Tutorial](https://github.com/langchain-ai/langgraph/blob/main/examples/plan-and-execute/plan-and-execute.ipynb)
+
+---
+
+#### 6.2 Travel Planning Agent
+**Description:** Agent that plans trips by coordinating flight searches, hotel bookings, and itinerary creation with user preferences.
+
+**Demonstrates:**
+- Multi-tool coordination
+- User preference handling
+- Complex planning with constraints
+- Real API integration patterns
+
+**Original Reference:** [LangGraph Travel Planning Tutorial](https://www.projectpro.io/article/langgraph/1109)
+
+---
+
+### 7. Code and Development (`code/`)
+
+#### 7.1 Code Assistant
+**Description:** Agent that helps with code-related tasks: explaining code, generating code, debugging, and answering programming questions.
+
+**Demonstrates:**
+- Code-aware prompting
+- Multi-turn coding conversations
+- Tool use for code execution/testing
+- Context management for large codebases
+
+**Original Reference:** [LangGraph Code Assistant Tutorial](https://github.com/langchain-ai/langgraph/blob/main/examples/code_assistant/langgraph_code_assistant.ipynb)
+
+---
+
+### 8. Advanced Patterns (`advanced/`)
+
+#### 8.1 Reflection Agent
+**Description:** Agent that generates output, reflects on it, and iteratively improves until quality criteria are met.
+
+**Demonstrates:**
+- Self-critique and improvement loops
+- Quality evaluation nodes
+- Iterative refinement patterns
+- Loop termination conditions
+
+**Original Reference:** [LangGraph Reflection Tutorial](https://github.com/langchain-ai/langgraph/tree/main/docs/docs/tutorials/reflection)
+
+---
+
+#### 8.2 Tree of Thoughts
+**Description:** Agent that explores multiple reasoning paths in parallel and selects the best solution.
+
+**Demonstrates:**
+- Parallel exploration with `Send`
+- Solution evaluation and ranking
+- Branch pruning logic
+- Complex graph patterns
+
+**Original Reference:** [LangGraph Tree of Thoughts Tutorial](https://github.com/langchain-ai/langgraph/tree/main/docs/docs/tutorials/tot)
+
+---
+
+#### 8.3 Long-Running Workflow with Checkpointing
+**Description:** Demonstrates continue-as-new for workflows that may run for extended periods and generate large event histories.
+
+**Demonstrates:**
+- `should_continue` callback for checkpointing
+- State serialization and restoration
+- Continue-as-new pattern
+- Store API for cross-workflow data
+
+**Original Reference:** N/A (Temporal-specific pattern)
+
+---
+
+## Implementation Priority
+
+### Phase 1 - Core Examples (Recommended First)
+1. **Basic: Hello World Agent** - Entry point for new users
+2. **Basic: ReAct Agent with Tools** - Most common agent pattern
+3. **Human-in-the-Loop: Approval Workflow** - Key differentiator for Temporal
+4. **RAG: Agentic RAG** - Popular production use case
+
+### Phase 2 - Multi-Agent and Research
+5. **Multi-Agent: Supervisor Agent** - Popular advanced pattern
+6. **RAG: Deep Research Agent** - Showcases long-running durability
+7. **Customer Service: Support Agent** - Business-relevant example
+
+### Phase 3 - Advanced Patterns
+8. **Planning: Plan-and-Execute** - Structured agent execution
+9. **Advanced: Reflection Agent** - Self-improvement pattern
+10. **Multi-Agent: Hierarchical Teams** - Complex orchestration
+
+---
+
+## Directory Structure
+
+```
+langgraph/
+├── README.md                    # Overview and getting started
+├── __init__.py
+├── basic/
+│   ├── README.md
+│   ├── hello_world/
+│   │   ├── README.md
+│   │   ├── run_worker.py
+│   │   └── run_workflow.py
+│   └── react_agent/
+│       ├── README.md
+│       ├── agent.py
+│       ├── tools.py
+│       ├── run_worker.py
+│       └── run_workflow.py
+├── human_in_loop/
+│   ├── README.md
+│   ├── approval_workflow/
+│   └── document_review/
+├── multi_agent/
+│   ├── README.md
+│   ├── supervisor/
+│   └── hierarchical_teams/
+├── rag/
+│   ├── README.md
+│   ├── agentic_rag/
+│   ├── corrective_rag/
+│   └── deep_research/
+├── customer_service/
+│   ├── README.md
+│   ├── support_agent/
+│   └── email_triage/
+├── planning/
+│   ├── README.md
+│   ├── plan_and_execute/
+│   └── travel_planner/
+├── code/
+│   ├── README.md
+│   └── code_assistant/
+└── advanced/
+    ├── README.md
+    ├── reflection/
+    ├── tree_of_thoughts/
+    └── long_running_workflow/
+```
+
+---
+
+## References
+
+### Official LangGraph Resources
+- [LangGraph Documentation](https://docs.langchain.com/oss/python/langgraph/overview)
+- [LangGraph GitHub Repository](https://github.com/langchain-ai/langgraph)
+- [LangGraph Tutorials](https://github.com/langchain-ai/langgraph/tree/main/docs/docs/tutorials)
+- [LangChain Academy - Intro to LangGraph](https://academy.langchain.com/courses/intro-to-langgraph)
+
+### Multi-Agent Resources
+- [LangGraph Multi-Agent Concepts](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/concepts/multi_agent.md)
+- [LangGraph Supervisor Library](https://github.com/langchain-ai/langgraph-supervisor-py)
+- [Hierarchical Agent Teams Tutorial](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/)
+
+### RAG and Research
+- [Agentic RAG Documentation](https://docs.langchain.com/oss/python/langgraph/agentic-rag)
+- [RAG Research Agent Template](https://github.com/langchain-ai/rag-research-agent-template)
+- [Deep Research Agent Article](https://towardsdatascience.com/langgraph-101-lets-build-a-deep-research-agent/)
+
+### Human-in-the-Loop
+- [Human-in-the-Loop How-To](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/)
+- [Human Feedback Discussion](https://github.com/langchain-ai/langgraph/discussions/1137)
+
+### Courses and Tutorials
+- [DeepLearning.AI - AI Agents in LangGraph](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/)
+- [Real Python - LangGraph Tutorial](https://realpython.com/langgraph-python/)
diff --git a/langgraph_samples/__init__.py b/langgraph_samples/__init__.py
new file mode 100644
index 00000000..c30a7488
--- /dev/null
+++ b/langgraph_samples/__init__.py
@@ -0,0 +1 @@
+# Temporal LangGraph Samples
diff --git a/langgraph_samples/basic/README.md b/langgraph_samples/basic/README.md
new file mode 100644
index 00000000..d88057d1
--- /dev/null
+++ b/langgraph_samples/basic/README.md
@@ -0,0 +1,18 @@
+# Basic LangGraph Examples
+
+Simple examples to get started with LangGraph integrated with Temporal workflows.
+
+Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md).
+
+## Examples
+
+### Hello World Agent
+
+Minimal LangGraph agent with Temporal integration. A single-node graph that processes a query.
+
+**Demonstrates:**
+- Basic plugin setup and graph registration
+- Simple workflow invocation
+- Activity-based node execution
+
+See [hello_world/README.md](./hello_world/README.md) for details.
diff --git a/langgraph_samples/basic/__init__.py b/langgraph_samples/basic/__init__.py
new file mode 100644
index 00000000..98e74479
--- /dev/null
+++ b/langgraph_samples/basic/__init__.py
@@ -0,0 +1 @@
+# Basic LangGraph Examples
diff --git a/langgraph_samples/basic/hello_world/README.md b/langgraph_samples/basic/hello_world/README.md
new file mode 100644
index 00000000..f06885df
--- /dev/null
+++ b/langgraph_samples/basic/hello_world/README.md
@@ -0,0 +1,36 @@
+# Hello World LangGraph Agent
+
+Minimal LangGraph agent with Temporal integration. A single-node graph that processes a query.
+
+## What This Sample Demonstrates
+
+- **Basic plugin setup**: Registering a LangGraph graph with `LangGraphPlugin`
+- **Graph registration**: Creating a graph builder function and registering it by name
+- **Simple workflow invocation**: Using `compile()` to get a graph runner within a workflow
+- **Activity-based node execution**: Each graph node runs as a Temporal activity
+
+## How It Works
+
+1. **State Definition**: A simple `TypedDict` defines the workflow state with `query` and `result` fields
+2. **Node Function**: A single node processes the query and returns a result
+3. **Graph Builder**: Creates and compiles a minimal StateGraph with one node
+4. **Workflow**: Uses `compile("graph_name")` to get a runner and `ainvoke()` to execute
+5. **Plugin**: Registers the graph builder so it's available to workflows
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run langgraph_samples/basic/hello_world/run_worker.py
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run langgraph_samples/basic/hello_world/run_workflow.py
+```
+
+## Expected Output
+
+```
+Result: {'query': 'Hello, Temporal!', 'result': 'Processed: Hello, Temporal!'}
+```
diff --git a/langgraph_samples/basic/hello_world/__init__.py b/langgraph_samples/basic/hello_world/__init__.py
new file mode 100644
index 00000000..988bb5c8
--- /dev/null
+++ b/langgraph_samples/basic/hello_world/__init__.py
@@ -0,0 +1 @@
+# Hello World LangGraph Example
diff --git a/langgraph_samples/basic/hello_world/graph.py b/langgraph_samples/basic/hello_world/graph.py
new file mode 100644
index 00000000..b374245e
--- /dev/null
+++ b/langgraph_samples/basic/hello_world/graph.py
@@ -0,0 +1,60 @@
+"""Hello World LangGraph Graph Definition.
+
+This module defines the graph structure and node functions.
+It is imported only by the worker (not by the workflow).
+"""
+
+from typing import Any
+
+from langgraph.graph import END, START, StateGraph
+from typing_extensions import TypedDict
+
+
+# =============================================================================
+# State Definition
+# =============================================================================
+
+
+class HelloState(TypedDict, total=False):
+    """Simple state for the hello world agent."""
+
+    query: str
+    result: str
+
+
+# =============================================================================
+# Node Functions
+# =============================================================================
+
+
+def process_query(state: HelloState) -> HelloState:
+    """Process the query and return a result.
+
+    In a real application, this could call an LLM, database, or external API.
+    Each node function runs as a Temporal activity with automatic retries.
+    """
+    query = state.get("query", "")
+    return {"result": f"Processed: {query}"}
+
+
+# =============================================================================
+# Graph Builder
+# =============================================================================
+
+
+def build_hello_graph() -> Any:
+    """Build a minimal single-node graph.
+
+    This function is registered with LangGraphPlugin and called to create
+    the compiled graph when needed.
+    """
+    graph = StateGraph(HelloState)
+
+    # Add a single processing node
+    graph.add_node("process", process_query)
+
+    # Define edges: START -> process -> END
+    graph.add_edge(START, "process")
+    graph.add_edge("process", END)
+
+    return graph.compile()
diff --git a/langgraph_samples/basic/hello_world/run_worker.py b/langgraph_samples/basic/hello_world/run_worker.py
new file mode 100644
index 00000000..c423cacd
--- /dev/null
+++ b/langgraph_samples/basic/hello_world/run_worker.py
@@ -0,0 +1,39 @@
+"""Worker for the Hello World LangGraph example.
+
+Starts a Temporal worker that can execute HelloWorldWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.worker import Worker
+
+from langgraph_samples.basic.hello_world.graph import build_hello_graph
+from langgraph_samples.basic.hello_world.workflow import HelloWorldWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with our graph registered by name
+    plugin = LangGraphPlugin(
+        graphs={"hello_graph": build_hello_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    client = await Client.connect("localhost:7233", plugins=[plugin])
+
+    # Create and run the worker
+    # Note: Activities are automatically registered by the plugin
+    worker = Worker(
+        client,
+        task_queue="langgraph-hello-world",
+        workflows=[HelloWorldWorkflow],
+    )
+
+    print("Worker started. Ctrl+C to exit.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/basic/hello_world/run_workflow.py b/langgraph_samples/basic/hello_world/run_workflow.py
new file mode 100644
index 00000000..80a37440
--- /dev/null
+++ b/langgraph_samples/basic/hello_world/run_workflow.py
@@ -0,0 +1,30 @@
+"""Execute the Hello World LangGraph workflow.
+
+Connects to Temporal and starts the HelloWorldWorkflow.
+Note: The graph is only needed by the worker, not by the workflow starter.
+"""
+
+import asyncio
+
+from temporalio.client import Client
+
+from langgraph_samples.basic.hello_world.workflow import HelloWorldWorkflow
+
+
+async def main() -> None:
+    # Connect to Temporal
+    client = await Client.connect("localhost:7233")
+
+    # Execute the workflow
+    result = await client.execute_workflow(
+        HelloWorldWorkflow.run,
+        "Hello, Temporal!",
+        id="hello-world-workflow",
+        task_queue="langgraph-hello-world",
+    )
+
+    print(f"Result: {result}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/basic/hello_world/workflow.py b/langgraph_samples/basic/hello_world/workflow.py
new file mode 100644
index 00000000..cc8b1902
--- /dev/null
+++ b/langgraph_samples/basic/hello_world/workflow.py
@@ -0,0 +1,43 @@
+"""Hello World LangGraph Workflow.
+
+Minimal example demonstrating LangGraph with Temporal integration.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because langgraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class HelloWorldWorkflow:
+    """Temporal workflow that executes the hello world LangGraph agent.
+
+    This workflow demonstrates:
+    - Using compile() to get a graph runner by name
+    - Executing the graph with ainvoke()
+    - Each node runs as a Temporal activity with durability guarantees
+    """
+
+    @workflow.run
+    async def run(self, query: str) -> dict[str, Any]:
+        """Run the hello world agent.
+
+        Args:
+            query: The input query to process.
+
+        Returns:
+            The final state containing the processed result.
+        """
+        # Get the compiled graph runner by name
+        app = compile("hello_graph")
+
+        # Execute the graph - the "process" node runs as a Temporal activity
+        result = await app.ainvoke({"query": query})
+
+        return result
diff --git a/pyproject.toml b/pyproject.toml
index bf97ef04..563ee884 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,13 +31,13 @@ dsl = ["pyyaml>=6.0.1,<7", "types-pyyaml>=6.0.12,<7", "dacite>=1.8.1,<2"]
 encryption = ["cryptography>=38.0.1,<39", "aiohttp>=3.8.1,<4"]
 gevent = ["gevent>=25.4.2 ; python_version >= '3.8'"]
 langchain = [
-    "langchain>=0.1.7,<0.2 ; python_version >= '3.8.1' and python_version < '4.0'",
-    "langchain-openai>=0.0.6,<0.0.7 ; python_version >= '3.8.1' and python_version < '4.0'",
-    "langsmith>=0.1.22,<0.2 ; python_version >= '3.8.1' and python_version < '4.0'",
+    "langchain>=0.3.0",
+    "langchain-openai>=0.2.0",
+    "langsmith>=0.1.22",
     "openai>=1.4.0,<2",
     "fastapi>=0.115.12",
     "tqdm>=4.62.0,<5",
-    "uvicorn[standard]>=0.24.0.post1,<0.25",
+    "uvicorn[standard]>=0.24.0",
 ]
 nexus = ["nexus-rpc>=1.1.0,<2"]
 open-telemetry = [
@@ -58,6 +58,10 @@ cloud-export-to-parquet = [
     "boto3>=1.34.89,<2",
     "pyarrow>=19.0.1",
 ]
+langgraph = [
+    "langgraph>=0.2.0",
+    "langchain-core>=0.1.0",
+]
 
 [tool.hatch.metadata]
 allow-direct-references = true
@@ -80,6 +84,7 @@ packages = [
     "gevent_async",
     "hello",
     "langchain",
+    "langgraph_samples",
     "message_passing",
     "nexus",
     "open_telemetry",
diff --git a/uv.lock b/uv.lock
index 81feead4..843a1994 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,4 @@
 version = 1
-revision = 3
 requires-python = ">=3.10"
 resolution-markers = [
     "python_full_version >= '3.13'",
@@ -12,16 +11,16 @@ resolution-markers = [
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 version = "2.6.1"
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+source = { registry = "https://pypi.org/simple" }
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 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
 ]
 
 [[package]]
 name = "aiohttp"
 version = "3.12.14"
-source = { registry = "https://pypi.org/simple/" }
+source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
     { name = "aiosignal" },
@@ -32,790 +31,777 @@ dependencies = [
     { name = "propcache" },
     { name = "yarl" },
 ]
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From 18b995a3d4e88c400ac1f7799eb3231abb0d8030 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 09:50:21 -0800
Subject: [PATCH 02/24] Add README for react_agent sample

Document the ReAct agent sample showing durable tool execution
with temporal_tool() and the think-act-observe pattern.
---
 langgraph_samples/basic/react_agent/README.md | 74 +++++++++++++++++++
 1 file changed, 74 insertions(+)
 create mode 100644 langgraph_samples/basic/react_agent/README.md

diff --git a/langgraph_samples/basic/react_agent/README.md b/langgraph_samples/basic/react_agent/README.md
new file mode 100644
index 00000000..ecfb94b9
--- /dev/null
+++ b/langgraph_samples/basic/react_agent/README.md
@@ -0,0 +1,74 @@
+# ReAct Agent with Tools
+
+A ReAct (Reasoning + Acting) agent using LangGraph's `create_react_agent` with Temporal-wrapped tools for durable execution.
+
+## What This Sample Demonstrates
+
+- **ReAct pattern**: The think-act-observe loop where the LLM decides actions and observes results
+- **Durable tool execution**: Using `temporal_tool()` to wrap LangChain tools as Temporal activities
+- **Automatic retries**: Each tool invocation has its own timeout and retry policy
+- **Cyclic graph execution**: The agent loops between thinking and acting until it has an answer
+
+## How It Works
+
+1. **Tools**: Three LangChain tools (`get_weather`, `calculate`, `search_knowledge`) simulate external APIs
+2. **Temporal wrapping**: Each tool is wrapped with `temporal_tool()` for durable execution
+3. **ReAct agent**: `create_react_agent()` builds a cyclic graph that alternates between LLM calls and tool execution
+4. **Workflow**: Invokes the agent and returns the final conversation state
+
+The ReAct pattern:
+```
+User Query → [Think] → [Act (tool)] → [Observe] → [Think] → ... → Final Answer
+```
+
+## Prerequisites
+
+- Temporal server running locally (`temporal server start-dev`)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run langgraph_samples/basic/react_agent/run_worker.py
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run langgraph_samples/basic/react_agent/run_workflow.py
+```
+
+## Example Queries
+
+The sample runs three queries demonstrating different tool usage:
+
+1. **Weather tool**: "What's the weather like in Tokyo?"
+2. **Calculator tool**: "What is 25 * 4 + 10?"
+3. **Knowledge search**: "Tell me about Temporal and LangGraph"
+
+## Expected Output
+
+```
+============================================================
+Query: What's the weather like in Tokyo?
+============================================================
+
+Agent Response:
+The current weather in Tokyo is 68°F with clear skies.
+
+============================================================
+Query: What is 25 * 4 + 10?
+============================================================
+
+Agent Response:
+The result of 25 * 4 + 10 is 110.
+
+============================================================
+Query: Tell me about Temporal and LangGraph
+============================================================
+
+Agent Response:
+Temporal is a durable execution platform that helps developers build reliable
+applications. LangGraph is a library for building stateful, multi-actor
+applications with LLMs...
+```

From f8f51a0fb290e5cafcaa9c9fba3d9b853ff45dff Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 10:54:14 -0800
Subject: [PATCH 03/24] LangGraph: Use ClientConfig for client initialization
 in all samples

Update all run_worker.py and run_workflow.py files to use the
ClientConfig.load_client_connect_config() pattern for environment-based
configuration support. Also simplify react_agent run_workflow.py to
run a single query with clean output.

Add CLAUDE.md documenting the client initialization pattern.
---
 CLAUDE.md                                     |  16 +++
 .../basic/hello_world/run_worker.py           |   5 +-
 .../basic/hello_world/run_workflow.py         |   6 +-
 .../basic/react_agent/run_worker.py           |  47 +++++++
 .../basic/react_agent/run_workflow.py         |  36 ++++++
 .../approval_workflow/run_worker.py           |  50 ++++++++
 .../approval_workflow/run_workflow.py         | 116 ++++++++++++++++++
 7 files changed, 273 insertions(+), 3 deletions(-)
 create mode 100644 CLAUDE.md
 create mode 100644 langgraph_samples/basic/react_agent/run_worker.py
 create mode 100644 langgraph_samples/basic/react_agent/run_workflow.py
 create mode 100644 langgraph_samples/human_in_loop/approval_workflow/run_worker.py
 create mode 100644 langgraph_samples/human_in_loop/approval_workflow/run_workflow.py

diff --git a/CLAUDE.md b/CLAUDE.md
new file mode 100644
index 00000000..76a799e1
--- /dev/null
+++ b/CLAUDE.md
@@ -0,0 +1,16 @@
+# Temporal Python Samples
+
+## Client Initialization Pattern
+
+Use the `ClientConfig` pattern for client initialization to support environment-based configuration:
+
+```python
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+config = ClientConfig.load_client_connect_config()
+config.setdefault("target_host", "localhost:7233")
+client = await Client.connect(**config)
+```
+
+This pattern allows configuration via environment variables while providing sensible defaults.
diff --git a/langgraph_samples/basic/hello_world/run_worker.py b/langgraph_samples/basic/hello_world/run_worker.py
index c423cacd..8734513d 100644
--- a/langgraph_samples/basic/hello_world/run_worker.py
+++ b/langgraph_samples/basic/hello_world/run_worker.py
@@ -8,6 +8,7 @@
 
 from temporalio.client import Client
 from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
 from langgraph_samples.basic.hello_world.graph import build_hello_graph
@@ -21,7 +22,9 @@ async def main() -> None:
     )
 
     # Connect to Temporal with the plugin
-    client = await Client.connect("localhost:7233", plugins=[plugin])
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
 
     # Create and run the worker
     # Note: Activities are automatically registered by the plugin
diff --git a/langgraph_samples/basic/hello_world/run_workflow.py b/langgraph_samples/basic/hello_world/run_workflow.py
index 80a37440..c5c4f475 100644
--- a/langgraph_samples/basic/hello_world/run_workflow.py
+++ b/langgraph_samples/basic/hello_world/run_workflow.py
@@ -7,13 +7,15 @@
 import asyncio
 
 from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
 
 from langgraph_samples.basic.hello_world.workflow import HelloWorldWorkflow
 
 
 async def main() -> None:
-    # Connect to Temporal
-    client = await Client.connect("localhost:7233")
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
 
     # Execute the workflow
     result = await client.execute_workflow(
diff --git a/langgraph_samples/basic/react_agent/run_worker.py b/langgraph_samples/basic/react_agent/run_worker.py
new file mode 100644
index 00000000..ee2f6474
--- /dev/null
+++ b/langgraph_samples/basic/react_agent/run_worker.py
@@ -0,0 +1,47 @@
+"""Worker for the ReAct Agent sample.
+
+Starts a Temporal worker that can execute ReActAgentWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_samples.basic.react_agent.graph import build_react_agent
+from langgraph_samples.basic.react_agent.workflow import ReActAgentWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the ReAct agent graph registered
+    plugin = LangGraphPlugin(
+        graphs={"react_agent": build_react_agent},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: Activities (LLM calls, tool executions) are automatically registered
+    worker = Worker(
+        client,
+        task_queue="langgraph-react-agent",
+        workflows=[ReActAgentWorkflow],
+    )
+
+    print("ReAct Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/basic/react_agent/run_workflow.py b/langgraph_samples/basic/react_agent/run_workflow.py
new file mode 100644
index 00000000..4ab4229f
--- /dev/null
+++ b/langgraph_samples/basic/react_agent/run_workflow.py
@@ -0,0 +1,36 @@
+"""Execute the ReAct Agent workflow.
+
+Usage:
+    # First, in another terminal, start the worker:
+    python -m langgraph_samples.basic.react_agent.run_worker
+
+    # Then run this script:
+    python -m langgraph_samples.basic.react_agent.run_workflow
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_samples.basic.react_agent.workflow import ReActAgentWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    result = await client.execute_workflow(
+        ReActAgentWorkflow.run,
+        "What's the weather like in Tokyo?",
+        id="react-agent-workflow",
+        task_queue="langgraph-react-agent",
+    )
+
+    # Print only the text response
+    print(result["messages"][-1]["content"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_worker.py b/langgraph_samples/human_in_loop/approval_workflow/run_worker.py
new file mode 100644
index 00000000..09529d03
--- /dev/null
+++ b/langgraph_samples/human_in_loop/approval_workflow/run_worker.py
@@ -0,0 +1,50 @@
+"""Run the Approval Workflow worker.
+
+Starts a Temporal worker that can execute the approval workflow.
+
+Usage:
+    python -m langgraph_samples.human_in_loop.approval_workflow.run_worker
+"""
+
+import asyncio
+from datetime import timedelta
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_samples.human_in_loop.approval_workflow.graph import build_approval_graph
+from langgraph_samples.human_in_loop.approval_workflow.workflow import ApprovalWorkflow
+
+TASK_QUEUE = "langgraph-approval"
+
+
+async def main() -> None:
+    # Create the LangGraph plugin with the approval graph
+    plugin = LangGraphPlugin(
+        graphs={"approval_workflow": build_approval_graph},
+        default_activity_timeout=timedelta(seconds=30),
+    )
+
+    # Connect to Temporal
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run worker
+    worker = Worker(
+        client,
+        task_queue=TASK_QUEUE,
+        workflows=[ApprovalWorkflow],
+        # Activities are registered by the plugin
+    )
+
+    print(f"Starting approval workflow worker on task queue: {TASK_QUEUE}")
+    print("Press Ctrl+C to stop")
+
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py b/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
new file mode 100644
index 00000000..40a8ff46
--- /dev/null
+++ b/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
@@ -0,0 +1,116 @@
+"""Execute the Approval Workflow.
+
+Demonstrates the human-in-the-loop approval pattern:
+1. Start a workflow that pauses for approval
+2. Query the pending approval details
+3. Send an approval signal
+4. Get the final result
+
+Usage:
+    # First, start the worker in another terminal:
+    python -m langgraph_samples.human_in_loop.approval_workflow.run_worker
+
+    # Then run this script:
+    python -m langgraph_samples.human_in_loop.approval_workflow.run_workflow
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_samples.human_in_loop.approval_workflow.workflow import (
+    ApprovalRequest,
+    ApprovalWorkflow,
+)
+
+TASK_QUEUE = "langgraph-approval"
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Example 1: Approve a request
+    print("\n" + "=" * 60)
+    print("Example 1: Approving a purchase request")
+    print("=" * 60)
+
+    handle = await client.start_workflow(
+        ApprovalWorkflow.run,
+        ApprovalRequest(
+            request_type="purchase",
+            amount=500.00,
+            request_data={"item": "Office supplies", "vendor": "Acme Corp"},
+        ),
+        id="approval-workflow-approve",
+        task_queue=TASK_QUEUE,
+    )
+
+    # Wait for the workflow to reach the interrupt
+    await asyncio.sleep(1)
+
+    # Query the pending approval
+    pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+    print(f"\nPending approval: {pending}")
+
+    status = await handle.query(ApprovalWorkflow.get_status)
+    print(f"Workflow status: {status}")
+
+    # Send approval signal
+    print("\nSending approval signal...")
+    await handle.signal(
+        ApprovalWorkflow.provide_approval,
+        {
+            "approved": True,
+            "reason": "Within budget",
+            "approver": "manager@example.com",
+        },
+    )
+
+    # Wait for result
+    result = await handle.result()
+    print(f"\nResult: {result.get('result')}")
+    print(f"Executed: {result.get('executed')}")
+
+    # Example 2: Reject a request
+    print("\n" + "=" * 60)
+    print("Example 2: Rejecting a high-risk request")
+    print("=" * 60)
+
+    handle2 = await client.start_workflow(
+        ApprovalWorkflow.run,
+        ApprovalRequest(
+            request_type="transfer",
+            amount=50000.00,
+            request_data={"destination": "External account"},
+        ),
+        id="approval-workflow-reject",
+        task_queue=TASK_QUEUE,
+    )
+
+    await asyncio.sleep(1)
+
+    # Query pending approval
+    pending2 = await handle2.query(ApprovalWorkflow.get_pending_approval)
+    print(f"\nPending approval: {pending2}")
+
+    # Reject it
+    print("\nSending rejection signal...")
+    await handle2.signal(
+        ApprovalWorkflow.provide_approval,
+        {
+            "approved": False,
+            "reason": "Amount exceeds single-approval limit",
+            "approver": "compliance@example.com",
+        },
+    )
+
+    result2 = await handle2.result()
+    print(f"\nResult: {result2.get('result')}")
+    print(f"Executed: {result2.get('executed')}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

From 6b96e09604e506cbbb29eb507ae147034df39e51 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 10:56:40 -0800
Subject: [PATCH 04/24] LangGraph: Add react_agent and approval_workflow sample
 files

Add remaining sample files:
- react_agent: graph, tools, workflow definitions
- approval_workflow: graph, workflow with human-in-the-loop pattern
---
 .../basic/react_agent/__init__.py             |   1 +
 langgraph_samples/basic/react_agent/graph.py  |  56 +++++++
 langgraph_samples/basic/react_agent/tools.py  |  75 +++++++++
 .../basic/react_agent/workflow.py             |  54 +++++++
 langgraph_samples/human_in_loop/__init__.py   |   1 +
 .../approval_workflow/__init__.py             |  11 ++
 .../human_in_loop/approval_workflow/graph.py  | 128 ++++++++++++++++
 .../approval_workflow/workflow.py             | 145 ++++++++++++++++++
 8 files changed, 471 insertions(+)
 create mode 100644 langgraph_samples/basic/react_agent/__init__.py
 create mode 100644 langgraph_samples/basic/react_agent/graph.py
 create mode 100644 langgraph_samples/basic/react_agent/tools.py
 create mode 100644 langgraph_samples/basic/react_agent/workflow.py
 create mode 100644 langgraph_samples/human_in_loop/__init__.py
 create mode 100644 langgraph_samples/human_in_loop/approval_workflow/__init__.py
 create mode 100644 langgraph_samples/human_in_loop/approval_workflow/graph.py
 create mode 100644 langgraph_samples/human_in_loop/approval_workflow/workflow.py

diff --git a/langgraph_samples/basic/react_agent/__init__.py b/langgraph_samples/basic/react_agent/__init__.py
new file mode 100644
index 00000000..1e145783
--- /dev/null
+++ b/langgraph_samples/basic/react_agent/__init__.py
@@ -0,0 +1 @@
+"""ReAct Agent with Tools sample."""
diff --git a/langgraph_samples/basic/react_agent/graph.py b/langgraph_samples/basic/react_agent/graph.py
new file mode 100644
index 00000000..c60b6f00
--- /dev/null
+++ b/langgraph_samples/basic/react_agent/graph.py
@@ -0,0 +1,56 @@
+"""ReAct Agent Graph Definition.
+
+This module builds a ReAct agent using LangGraph's create_react_agent
+with Temporal-wrapped tools for durable execution.
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from datetime import timedelta
+from typing import Any
+
+from langchain_openai import ChatOpenAI
+from langgraph.prebuilt import create_react_agent
+from temporalio.contrib.langgraph import temporal_tool
+
+from langgraph_samples.basic.react_agent.tools import (
+    calculate,
+    get_weather,
+    search_knowledge,
+)
+
+
+def build_react_agent() -> Any:
+    """Build a ReAct agent with durable tool execution.
+
+    This function creates a ReAct agent where tool invocations execute as
+    Temporal activities with durability and automatic retries.
+
+    The agent follows the ReAct pattern:
+    1. Think: LLM decides what action to take
+    2. Act: Execute the chosen tool
+    3. Observe: Feed tool results back to LLM
+    4. Repeat until done
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    # Create the model
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Wrap tools for durable execution as Temporal activities
+    # Each tool call becomes a Temporal activity with its own timeout and retries
+    tools = [
+        temporal_tool(get_weather, start_to_close_timeout=timedelta(seconds=30)),
+        temporal_tool(calculate, start_to_close_timeout=timedelta(seconds=10)),
+        temporal_tool(search_knowledge, start_to_close_timeout=timedelta(seconds=30)),
+    ]
+
+    # Create the ReAct agent using LangGraph's prebuilt function
+    # This creates a cyclic graph: agent -> tools -> agent (repeat until done)
+    return create_react_agent(model, tools)
diff --git a/langgraph_samples/basic/react_agent/tools.py b/langgraph_samples/basic/react_agent/tools.py
new file mode 100644
index 00000000..c2fe7098
--- /dev/null
+++ b/langgraph_samples/basic/react_agent/tools.py
@@ -0,0 +1,75 @@
+"""Tool definitions for the ReAct Agent.
+
+These tools demonstrate how to wrap LangChain tools for durable execution
+using temporal_tool().
+"""
+
+from langchain_core.tools import tool
+
+
+@tool
+def get_weather(city: str) -> str:
+    """Get the current weather for a city.
+
+    Args:
+        city: The city name to get weather for.
+
+    Returns:
+        A string describing the current weather conditions.
+    """
+    # Simulated weather data - in production, call a real weather API
+    weather_data = {
+        "new york": "72°F, Partly cloudy with light winds",
+        "london": "58°F, Overcast with chance of rain",
+        "tokyo": "68°F, Clear skies",
+        "paris": "65°F, Sunny with occasional clouds",
+        "sydney": "77°F, Warm and humid",
+    }
+    city_lower = city.lower()
+    return weather_data.get(city_lower, f"Weather data not available for {city}")
+
+
+@tool
+def calculate(expression: str) -> str:
+    """Perform a mathematical calculation.
+
+    Args:
+        expression: A mathematical expression like "2 + 2" or "10 * 5".
+
+    Returns:
+        The result of the calculation as a string.
+    """
+    try:
+        # Safely evaluate mathematical expressions
+        # Only allow basic math operations
+        allowed_chars = set("0123456789+-*/(). ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Only basic math operations are allowed"
+        result = eval(expression)  # noqa: S307
+        return str(result)
+    except Exception as e:
+        return f"Error calculating: {e}"
+
+
+@tool
+def search_knowledge(query: str) -> str:
+    """Search a knowledge base for information.
+
+    Args:
+        query: The search query.
+
+    Returns:
+        Relevant information from the knowledge base.
+    """
+    # Simulated knowledge base - in production, use a real search/RAG system
+    knowledge = {
+        "temporal": "Temporal is a durable execution platform that helps developers build reliable applications. It provides fault tolerance, state management, and workflow orchestration.",
+        "langgraph": "LangGraph is a library for building stateful, multi-actor applications with LLMs. It enables creating agent workflows with cycles, conditionals, and state management.",
+        "react": "ReAct (Reasoning + Acting) is an agent pattern where the AI alternates between thinking about what to do and taking actions. It improves reliability by making the reasoning process explicit.",
+        "python": "Python is a high-level programming language known for its readability and versatility. It's widely used in AI/ML, web development, and automation.",
+    }
+    query_lower = query.lower()
+    for key, value in knowledge.items():
+        if key in query_lower:
+            return value
+    return f"No specific information found for '{query}'. Try searching for: temporal, langgraph, react, or python."
diff --git a/langgraph_samples/basic/react_agent/workflow.py b/langgraph_samples/basic/react_agent/workflow.py
new file mode 100644
index 00000000..c142d4e2
--- /dev/null
+++ b/langgraph_samples/basic/react_agent/workflow.py
@@ -0,0 +1,54 @@
+"""ReAct Agent Workflow.
+
+Temporal workflow that executes the ReAct agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class ReActAgentWorkflow:
+    """Temporal workflow that executes a ReAct agent.
+
+    This workflow demonstrates:
+    - Using create_react_agent with temporal_model() for durable LLM calls
+    - Using temporal_tool() for durable tool execution
+    - The think-act-observe loop pattern
+
+    Each LLM call and tool invocation runs as a Temporal activity,
+    providing automatic retries and failure recovery.
+    """
+
+    @workflow.run
+    async def run(self, query: str) -> dict[str, Any]:
+        """Run the ReAct agent to answer a query.
+
+        The agent will:
+        1. Think about what information is needed
+        2. Use tools to gather information
+        3. Synthesize a final answer
+
+        Args:
+            query: The user's question or request.
+
+        Returns:
+            The final state containing the conversation messages and result.
+        """
+        # Get the compiled graph runner by name
+        app = compile("react_agent")
+
+        # Execute the agent
+        # The input format for create_react_agent is a dict with "messages"
+        result = await app.ainvoke(
+            {"messages": [{"role": "user", "content": query}]}
+        )
+
+        return result
diff --git a/langgraph_samples/human_in_loop/__init__.py b/langgraph_samples/human_in_loop/__init__.py
new file mode 100644
index 00000000..a41e9687
--- /dev/null
+++ b/langgraph_samples/human_in_loop/__init__.py
@@ -0,0 +1 @@
+"""Human-in-the-Loop samples demonstrating interrupt and approval patterns."""
diff --git a/langgraph_samples/human_in_loop/approval_workflow/__init__.py b/langgraph_samples/human_in_loop/approval_workflow/__init__.py
new file mode 100644
index 00000000..17c0c106
--- /dev/null
+++ b/langgraph_samples/human_in_loop/approval_workflow/__init__.py
@@ -0,0 +1,11 @@
+"""Approval Workflow Sample.
+
+Demonstrates human-in-the-loop approval pattern using LangGraph's interrupt()
+with Temporal signals for receiving human input.
+
+Components:
+- graph.py: LangGraph graph with approval node using interrupt()
+- workflow.py: Temporal workflow that handles interrupts and signals
+- run_worker.py: Worker setup with LangGraphPlugin
+- run_workflow.py: Client that starts workflow and sends approval signal
+"""
diff --git a/langgraph_samples/human_in_loop/approval_workflow/graph.py b/langgraph_samples/human_in_loop/approval_workflow/graph.py
new file mode 100644
index 00000000..06de4127
--- /dev/null
+++ b/langgraph_samples/human_in_loop/approval_workflow/graph.py
@@ -0,0 +1,128 @@
+"""Approval Workflow Graph Definition.
+
+This module builds a graph that demonstrates human-in-the-loop approval
+using LangGraph's interrupt() function.
+
+The graph flow:
+1. process_request: Validates and prepares the request
+2. request_approval: Calls interrupt() to pause for human approval
+3. execute_action: Processes the approved request (or rejects it)
+"""
+
+from typing import Any
+
+from langgraph.graph import END, START, StateGraph
+from langgraph.types import interrupt
+from typing_extensions import TypedDict
+
+
+class ApprovalState(TypedDict, total=False):
+    """State for the approval workflow."""
+
+    # Input
+    request_type: str
+    request_data: dict[str, Any]
+    amount: float
+
+    # Processing state
+    validated: bool
+    risk_level: str
+
+    # Approval state
+    approved: bool
+    approval_reason: str
+    approver: str
+
+    # Output
+    result: str
+    executed: bool
+
+
+def process_request(state: ApprovalState) -> ApprovalState:
+    """Validate and assess the request before approval.
+
+    This node analyzes the request and determines risk level.
+    """
+    amount = state.get("amount", 0)
+
+    # Determine risk level based on amount
+    if amount < 100:
+        risk_level = "low"
+    elif amount < 1000:
+        risk_level = "medium"
+    else:
+        risk_level = "high"
+
+    return {
+        "validated": True,
+        "risk_level": risk_level,
+    }
+
+
+def request_approval(state: ApprovalState) -> ApprovalState:
+    """Request human approval via interrupt.
+
+    This node pauses execution and waits for human input.
+    The interrupt() call returns the human's response when resumed.
+    """
+    # Create the approval request with context for the human
+    approval_request = {
+        "request_type": state.get("request_type", "unknown"),
+        "amount": state.get("amount", 0),
+        "risk_level": state.get("risk_level", "unknown"),
+        "request_data": state.get("request_data", {}),
+        "message": f"Please approve {state.get('request_type', 'request')} "
+        f"for ${state.get('amount', 0):.2f} (Risk: {state.get('risk_level', 'unknown')})",
+    }
+
+    # This pauses the graph and returns control to the workflow.
+    # The workflow will wait for a signal with the human's response.
+    # When resumed with Command(resume=response), interrupt() returns that response.
+    approval_response = interrupt(approval_request)
+
+    return {
+        "approved": approval_response.get("approved", False),
+        "approval_reason": approval_response.get("reason", ""),
+        "approver": approval_response.get("approver", "unknown"),
+    }
+
+
+def execute_action(state: ApprovalState) -> ApprovalState:
+    """Execute or reject the action based on approval status."""
+    if state.get("approved"):
+        return {
+            "executed": True,
+            "result": f"Successfully processed {state.get('request_type', 'request')} "
+            f"for ${state.get('amount', 0):.2f}. "
+            f"Approved by {state.get('approver', 'unknown')}: {state.get('approval_reason', '')}",
+        }
+    else:
+        return {
+            "executed": False,
+            "result": f"Request rejected by {state.get('approver', 'unknown')}: "
+            f"{state.get('approval_reason', 'No reason provided')}",
+        }
+
+
+def build_approval_graph() -> Any:
+    """Build the approval workflow graph.
+
+    Flow:
+    START -> process_request -> request_approval -> execute_action -> END
+
+    The request_approval node uses interrupt() to pause for human input.
+    """
+    graph = StateGraph(ApprovalState)
+
+    # Add nodes
+    graph.add_node("process_request", process_request)
+    graph.add_node("request_approval", request_approval)
+    graph.add_node("execute_action", execute_action)
+
+    # Define edges
+    graph.add_edge(START, "process_request")
+    graph.add_edge("process_request", "request_approval")
+    graph.add_edge("request_approval", "execute_action")
+    graph.add_edge("execute_action", END)
+
+    return graph.compile()
diff --git a/langgraph_samples/human_in_loop/approval_workflow/workflow.py b/langgraph_samples/human_in_loop/approval_workflow/workflow.py
new file mode 100644
index 00000000..5b7fee06
--- /dev/null
+++ b/langgraph_samples/human_in_loop/approval_workflow/workflow.py
@@ -0,0 +1,145 @@
+"""Approval Workflow Definition.
+
+This workflow demonstrates human-in-the-loop approval using:
+- LangGraph's interrupt() for pausing execution
+- Temporal signals for receiving human input
+- Temporal queries for checking pending approvals
+- Timeout handling for approval deadlines
+"""
+
+from dataclasses import dataclass
+from datetime import timedelta
+from typing import Any
+
+from temporalio import workflow
+
+with workflow.unsafe.imports_passed_through():
+    from langgraph.types import Command
+
+    from temporalio.contrib.langgraph import compile as lg_compile
+
+
+@dataclass
+class ApprovalRequest:
+    """Input for the approval workflow."""
+
+    request_type: str
+    amount: float
+    request_data: dict[str, Any] | None = None
+
+
+@workflow.defn
+class ApprovalWorkflow:
+    """Workflow that pauses for human approval before executing actions.
+
+    This demonstrates the full interrupt flow:
+    1. Graph runs until interrupt() is called in request_approval node
+    2. Workflow receives __interrupt__ in result with approval request details
+    3. Workflow waits for signal with human input (with optional timeout)
+    4. Workflow resumes graph with Command(resume=response)
+    5. Graph completes with execute_action node
+    """
+
+    def __init__(self) -> None:
+        self._approval_response: dict[str, Any] | None = None
+        self._interrupt_value: dict[str, Any] | None = None
+
+    @workflow.signal
+    def provide_approval(self, response: dict[str, Any]) -> None:
+        """Signal to provide approval response.
+
+        Args:
+            response: Dict with 'approved' (bool), 'reason' (str), 'approver' (str)
+        """
+        self._approval_response = response
+
+    @workflow.query
+    def get_pending_approval(self) -> dict[str, Any] | None:
+        """Query to get the current pending approval request.
+
+        Returns:
+            The interrupt value containing approval request details, or None.
+        """
+        return self._interrupt_value
+
+    @workflow.query
+    def get_status(self) -> str:
+        """Query to get the current workflow status."""
+        if self._interrupt_value is None:
+            return "processing"
+        elif self._approval_response is None:
+            return "waiting_for_approval"
+        else:
+            return "approved" if self._approval_response.get("approved") else "rejected"
+
+    @workflow.run
+    async def run(
+        self,
+        request: ApprovalRequest,
+        approval_timeout: timedelta | None = None,
+    ) -> dict[str, Any]:
+        """Run the approval workflow.
+
+        Args:
+            request: The approval request details.
+            approval_timeout: Optional timeout for waiting for approval.
+                             If None, waits indefinitely.
+
+        Returns:
+            The final state containing result and executed status.
+        """
+        app = lg_compile("approval_workflow")
+
+        # Handle both dataclass and dict input (Temporal deserializes to dict)
+        if isinstance(request, dict):
+            request_type = request.get("request_type", "unknown")
+            amount = request.get("amount", 0.0)
+            request_data = request.get("request_data") or {}
+        else:
+            request_type = request.request_type
+            amount = request.amount
+            request_data = request.request_data or {}
+
+        # Prepare initial state
+        initial_state = {
+            "request_type": request_type,
+            "amount": amount,
+            "request_data": request_data,
+        }
+
+        # First invocation - should hit interrupt at request_approval node
+        result = await app.ainvoke(initial_state)
+
+        # Check for interrupt
+        if "__interrupt__" in result:
+            # Store the interrupt value for queries
+            self._interrupt_value = result["__interrupt__"][0].value
+
+            workflow.logger.info(
+                "Workflow paused for approval: %s", self._interrupt_value.get("message")
+            )
+
+            # Wait for approval signal (with optional timeout)
+            try:
+                await workflow.wait_condition(
+                    lambda: self._approval_response is not None,
+                    timeout=approval_timeout,
+                )
+            except TimeoutError:
+                # Timeout - auto-reject
+                workflow.logger.warning("Approval timeout - auto-rejecting")
+                self._approval_response = {
+                    "approved": False,
+                    "reason": "Approval timeout exceeded",
+                    "approver": "system",
+                }
+
+            workflow.logger.info(
+                "Received approval response: approved=%s",
+                self._approval_response.get("approved") if self._approval_response else None,
+            )
+
+            # Resume with the approval response
+            result = await app.ainvoke(Command(resume=self._approval_response))
+
+        return result

From 2e5c82e5b33d95235710237d887246f1ff90c59b Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 10:58:31 -0800
Subject: [PATCH 05/24] LangGraph: Add README for approval_workflow and update
 react_agent README

- Add comprehensive README for human_in_loop/approval_workflow sample
- Update react_agent README to reflect simplified single-query output
---
 langgraph_samples/basic/react_agent/README.md | 36 ++-------
 .../human_in_loop/approval_workflow/README.md | 77 +++++++++++++++++++
 2 files changed, 83 insertions(+), 30 deletions(-)
 create mode 100644 langgraph_samples/human_in_loop/approval_workflow/README.md

diff --git a/langgraph_samples/basic/react_agent/README.md b/langgraph_samples/basic/react_agent/README.md
index ecfb94b9..0861ad71 100644
--- a/langgraph_samples/basic/react_agent/README.md
+++ b/langgraph_samples/basic/react_agent/README.md
@@ -38,37 +38,13 @@ Then, in a separate terminal, run the workflow:
 uv run langgraph_samples/basic/react_agent/run_workflow.py
 ```
 
-## Example Queries
-
-The sample runs three queries demonstrating different tool usage:
-
-1. **Weather tool**: "What's the weather like in Tokyo?"
-2. **Calculator tool**: "What is 25 * 4 + 10?"
-3. **Knowledge search**: "Tell me about Temporal and LangGraph"
-
 ## Expected Output
 
 ```
-============================================================
-Query: What's the weather like in Tokyo?
-============================================================
-
-Agent Response:
-The current weather in Tokyo is 68°F with clear skies.
-
-============================================================
-Query: What is 25 * 4 + 10?
-============================================================
-
-Agent Response:
-The result of 25 * 4 + 10 is 110.
-
-============================================================
-Query: Tell me about Temporal and LangGraph
-============================================================
-
-Agent Response:
-Temporal is a durable execution platform that helps developers build reliable
-applications. LangGraph is a library for building stateful, multi-actor
-applications with LLMs...
+The weather in Tokyo is currently 68°F with clear skies.
 ```
+
+You can modify the query in `run_workflow.py` to test different tools:
+- **Weather tool**: "What's the weather like in Tokyo?"
+- **Calculator tool**: "What is 25 * 4 + 10?"
+- **Knowledge search**: "Tell me about Temporal and LangGraph"
diff --git a/langgraph_samples/human_in_loop/approval_workflow/README.md b/langgraph_samples/human_in_loop/approval_workflow/README.md
new file mode 100644
index 00000000..afd0b37c
--- /dev/null
+++ b/langgraph_samples/human_in_loop/approval_workflow/README.md
@@ -0,0 +1,77 @@
+# Human-in-the-Loop Approval Workflow
+
+A workflow demonstrating human-in-the-loop approval using LangGraph's `interrupt()` function with Temporal signals and queries.
+
+## What This Sample Demonstrates
+
+- **LangGraph interrupt**: Using `interrupt()` to pause graph execution for human input
+- **Temporal signals**: Receiving approval/rejection decisions from external systems
+- **Temporal queries**: Checking pending approval status and workflow state
+- **Timeout handling**: Optional approval deadlines with auto-rejection
+
+## How It Works
+
+1. **process_request**: Validates the request and determines risk level based on amount
+2. **request_approval**: Calls `interrupt()` to pause execution and wait for human input
+3. **execute_action**: Processes the approved request or handles rejection
+
+The workflow flow:
+```
+Request → [Process & Assess Risk] → [Interrupt for Approval] → [Execute or Reject] → Result
+```
+
+When the graph hits `interrupt()`:
+1. Workflow receives `__interrupt__` in the result with approval request details
+2. Workflow waits for a signal with human input (with optional timeout)
+3. Workflow resumes the graph with `Command(resume=response)`
+4. Graph completes with the execute_action node
+
+## Prerequisites
+
+- Temporal server running locally (`temporal server start-dev`)
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_worker
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_workflow
+```
+
+## Expected Output
+
+```
+============================================================
+Example 1: Approving a purchase request
+============================================================
+
+Pending approval: {'request_type': 'purchase', 'amount': 500.0, 'risk_level': 'medium', ...}
+Workflow status: waiting_for_approval
+
+Sending approval signal...
+
+Result: Successfully processed purchase for $500.00. Approved by manager@example.com: Within budget
+Executed: True
+
+============================================================
+Example 2: Rejecting a high-risk request
+============================================================
+
+Pending approval: {'request_type': 'transfer', 'amount': 50000.0, 'risk_level': 'high', ...}
+
+Sending rejection signal...
+
+Result: Request rejected by compliance@example.com: Amount exceeds single-approval limit
+Executed: False
+```
+
+## Key APIs Used
+
+- `interrupt(value)`: Pauses graph execution and returns `value` in `__interrupt__`
+- `Command(resume=response)`: Resumes graph execution with the given response
+- `@workflow.signal`: Receives external input (approval response)
+- `@workflow.query`: Exposes workflow state (pending approval, status)

From 0432967770841e08cadc79873be493d3c6d47243 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 11:26:42 -0800
Subject: [PATCH 06/24] LangGraph: Add notification activity and CLI response
 tool to approval_workflow

- Add notify_approver activity that prints approval instructions with workflow ID
- Add run_respond.py CLI tool for approvers to approve/reject workflows
- Update workflow to call notification activity when hitting interrupt
- Simplify run_workflow.py to start one workflow and wait for result
- Update README with 3-terminal workflow instructions
---
 .../human_in_loop/approval_workflow/README.md | 78 ++++++++++------
 .../approval_workflow/activities.py           | 43 +++++++++
 .../approval_workflow/run_respond.py          | 86 ++++++++++++++++++
 .../approval_workflow/run_worker.py           |  3 +-
 .../approval_workflow/run_workflow.py         | 88 ++++---------------
 .../approval_workflow/workflow.py             | 11 +++
 6 files changed, 211 insertions(+), 98 deletions(-)
 create mode 100644 langgraph_samples/human_in_loop/approval_workflow/activities.py
 create mode 100644 langgraph_samples/human_in_loop/approval_workflow/run_respond.py

diff --git a/langgraph_samples/human_in_loop/approval_workflow/README.md b/langgraph_samples/human_in_loop/approval_workflow/README.md
index afd0b37c..a179a1cc 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/README.md
+++ b/langgraph_samples/human_in_loop/approval_workflow/README.md
@@ -1,77 +1,101 @@
 # Human-in-the-Loop Approval Workflow
 
-A workflow demonstrating human-in-the-loop approval using LangGraph's `interrupt()` function with Temporal signals and queries.
+A workflow demonstrating human-in-the-loop approval using LangGraph's `interrupt()` function with Temporal signals, queries, and notification activities.
 
 ## What This Sample Demonstrates
 
 - **LangGraph interrupt**: Using `interrupt()` to pause graph execution for human input
+- **Notification activity**: Alerting approvers when approval is needed
 - **Temporal signals**: Receiving approval/rejection decisions from external systems
 - **Temporal queries**: Checking pending approval status and workflow state
+- **CLI response tool**: Separate script for approvers to respond to requests
 - **Timeout handling**: Optional approval deadlines with auto-rejection
 
 ## How It Works
 
 1. **process_request**: Validates the request and determines risk level based on amount
 2. **request_approval**: Calls `interrupt()` to pause execution and wait for human input
-3. **execute_action**: Processes the approved request or handles rejection
+3. **notify_approver** (activity): Sends notification with instructions on how to respond
+4. **execute_action**: Processes the approved request or handles rejection
 
 The workflow flow:
 ```
-Request → [Process & Assess Risk] → [Interrupt for Approval] → [Execute or Reject] → Result
+Request → [Process & Assess Risk] → [Interrupt] → [Notify Approver] → [Wait for Signal] → [Execute or Reject] → Result
 ```
 
-When the graph hits `interrupt()`:
-1. Workflow receives `__interrupt__` in the result with approval request details
-2. Workflow waits for a signal with human input (with optional timeout)
-3. Workflow resumes the graph with `Command(resume=response)`
-4. Graph completes with the execute_action node
-
 ## Prerequisites
 
 - Temporal server running locally (`temporal server start-dev`)
 
 ## Running the Example
 
-First, start the worker:
+**Terminal 1 - Start the worker:**
 ```bash
 uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_worker
 ```
 
-Then, in a separate terminal, run the workflow:
+**Terminal 2 - Start a workflow:**
 ```bash
 uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_workflow
 ```
 
-## Expected Output
+The worker will print notification instructions like:
+```
+*** APPROVAL NEEDED ***
+Workflow ID: approval-abc12345
+Request: Please approve purchase for $500.00 (Risk: medium)
 
+To respond, run:
+  Approve: uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --approve --reason 'Your reason'
+  Reject:  uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --reject --reason 'Your reason'
 ```
-============================================================
-Example 1: Approving a purchase request
-============================================================
 
-Pending approval: {'request_type': 'purchase', 'amount': 500.0, 'risk_level': 'medium', ...}
-Workflow status: waiting_for_approval
+**Terminal 3 - Respond to the approval request:**
+```bash
+# Check status
+uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --status
 
-Sending approval signal...
+# Approve
+uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --approve --reason "Within budget"
 
-Result: Successfully processed purchase for $500.00. Approved by manager@example.com: Within budget
-Executed: True
+# Or reject
+uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --reject --reason "Needs manager approval"
+```
 
-============================================================
-Example 2: Rejecting a high-risk request
-============================================================
+## Response Script Options
 
-Pending approval: {'request_type': 'transfer', 'amount': 50000.0, 'risk_level': 'high', ...}
+```
+usage: run_respond.py [-h] [--approve] [--reject] [--status] [--reason REASON] [--approver APPROVER] workflow_id
+
+positional arguments:
+  workflow_id          The workflow ID to respond to
+
+options:
+  --approve            Approve the request
+  --reject             Reject the request
+  --status             Check workflow status
+  --reason REASON      Reason for approval/rejection
+  --approver APPROVER  Approver identifier (default: cli-user)
+```
+
+## Expected Output
+
+**Workflow starter (Terminal 2):**
+```
+Starting approval workflow: approval-abc12345
+Workflow started. Waiting for result...
 
-Sending rejection signal...
+To approve/reject, use the run_respond script (see worker output for commands)
 
-Result: Request rejected by compliance@example.com: Amount exceeds single-approval limit
-Executed: False
+============================================================
+Result: Successfully processed purchase for $500.00. Approved by cli-user: Within budget
+Executed: True
 ```
 
 ## Key APIs Used
 
 - `interrupt(value)`: Pauses graph execution and returns `value` in `__interrupt__`
 - `Command(resume=response)`: Resumes graph execution with the given response
+- `@activity.defn`: Notification activity to alert approvers
 - `@workflow.signal`: Receives external input (approval response)
 - `@workflow.query`: Exposes workflow state (pending approval, status)
diff --git a/langgraph_samples/human_in_loop/approval_workflow/activities.py b/langgraph_samples/human_in_loop/approval_workflow/activities.py
new file mode 100644
index 00000000..cc2c9ea0
--- /dev/null
+++ b/langgraph_samples/human_in_loop/approval_workflow/activities.py
@@ -0,0 +1,43 @@
+"""Activities for the approval workflow."""
+
+from temporalio import activity
+
+
+@activity.defn
+async def notify_approver(request_info: dict) -> str:
+    """Notify the approver about a pending approval request.
+
+    In a real implementation, this could:
+    - Send an email
+    - Post to Slack
+    - Create a ticket in a ticketing system
+    - Send a push notification
+
+    Args:
+        request_info: Information about the approval request.
+
+    Returns:
+        Confirmation message.
+    """
+    workflow_id = activity.info().workflow_id
+    message = request_info.get("message", "Approval needed")
+
+    # Log notification (simulating sending notification)
+    activity.logger.info(
+        f"NOTIFICATION: {message}\n"
+        f"  Workflow ID: {workflow_id}\n"
+        f"  To respond, run:\n"
+        f"    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond {workflow_id} --approve --reason 'Approved'\n"
+        f"    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond {workflow_id} --reject --reason 'Rejected'"
+    )
+
+    # In production, you would send actual notification here
+    print(f"\n*** APPROVAL NEEDED ***")
+    print(f"Workflow ID: {workflow_id}")
+    print(f"Request: {message}")
+    print(f"\nTo respond, run:")
+    print(f"  Approve: uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond {workflow_id} --approve --reason 'Your reason'")
+    print(f"  Reject:  uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond {workflow_id} --reject --reason 'Your reason'")
+    print()
+
+    return f"Notification sent for workflow {workflow_id}"
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_respond.py b/langgraph_samples/human_in_loop/approval_workflow/run_respond.py
new file mode 100644
index 00000000..b4417910
--- /dev/null
+++ b/langgraph_samples/human_in_loop/approval_workflow/run_respond.py
@@ -0,0 +1,86 @@
+"""Respond to an approval request.
+
+This script allows an approver to approve or reject a pending approval workflow.
+
+Usage:
+    # Approve a request:
+    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond <workflow-id> --approve --reason "Approved by manager"
+
+    # Reject a request:
+    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond <workflow-id> --reject --reason "Budget exceeded"
+
+    # Check status first:
+    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond <workflow-id> --status
+"""
+
+import argparse
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_samples.human_in_loop.approval_workflow.workflow import ApprovalWorkflow
+
+
+async def main() -> None:
+    parser = argparse.ArgumentParser(description="Respond to an approval workflow")
+    parser.add_argument("workflow_id", help="The workflow ID to respond to")
+    parser.add_argument("--approve", action="store_true", help="Approve the request")
+    parser.add_argument("--reject", action="store_true", help="Reject the request")
+    parser.add_argument("--status", action="store_true", help="Check workflow status")
+    parser.add_argument("--reason", default="", help="Reason for approval/rejection")
+    parser.add_argument(
+        "--approver", default="cli-user", help="Approver identifier (default: cli-user)"
+    )
+
+    args = parser.parse_args()
+
+    # Validate arguments
+    if not args.status and not args.approve and not args.reject:
+        parser.error("Must specify --approve, --reject, or --status")
+    if args.approve and args.reject:
+        parser.error("Cannot specify both --approve and --reject")
+
+    # Connect to Temporal
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Get workflow handle
+    handle = client.get_workflow_handle(args.workflow_id)
+
+    if args.status:
+        # Query the workflow status
+        try:
+            status = await handle.query(ApprovalWorkflow.get_status)
+            pending = await handle.query(ApprovalWorkflow.get_pending_approval)
+            print(f"Workflow ID: {args.workflow_id}")
+            print(f"Status: {status}")
+            if pending:
+                print(f"Pending approval: {pending.get('message', 'No message')}")
+                print(f"  Request type: {pending.get('request_type')}")
+                print(f"  Amount: ${pending.get('amount', 0):.2f}")
+                print(f"  Risk level: {pending.get('risk_level')}")
+        except Exception as e:
+            print(f"Error querying workflow: {e}")
+    else:
+        # Send approval/rejection signal
+        approved = args.approve
+        response = {
+            "approved": approved,
+            "reason": args.reason,
+            "approver": args.approver,
+        }
+
+        try:
+            await handle.signal(ApprovalWorkflow.provide_approval, response)
+            action = "Approved" if approved else "Rejected"
+            print(f"{action} workflow {args.workflow_id}")
+            if args.reason:
+                print(f"Reason: {args.reason}")
+        except Exception as e:
+            print(f"Error sending signal: {e}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_worker.py b/langgraph_samples/human_in_loop/approval_workflow/run_worker.py
index 09529d03..ac1d04ac 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/run_worker.py
+++ b/langgraph_samples/human_in_loop/approval_workflow/run_worker.py
@@ -14,6 +14,7 @@
 from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
+from langgraph_samples.human_in_loop.approval_workflow.activities import notify_approver
 from langgraph_samples.human_in_loop.approval_workflow.graph import build_approval_graph
 from langgraph_samples.human_in_loop.approval_workflow.workflow import ApprovalWorkflow
 
@@ -37,7 +38,7 @@ async def main() -> None:
         client,
         task_queue=TASK_QUEUE,
         workflows=[ApprovalWorkflow],
-        # Activities are registered by the plugin
+        activities=[notify_approver],
     )
 
     print(f"Starting approval workflow worker on task queue: {TASK_QUEUE}")
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py b/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
index 40a8ff46..2b1bfb72 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
+++ b/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
@@ -1,20 +1,21 @@
 """Execute the Approval Workflow.
 
-Demonstrates the human-in-the-loop approval pattern:
-1. Start a workflow that pauses for approval
-2. Query the pending approval details
-3. Send an approval signal
-4. Get the final result
+Starts an approval workflow that pauses for human approval.
+The worker will print instructions for how to approve/reject.
 
 Usage:
     # First, start the worker in another terminal:
     python -m langgraph_samples.human_in_loop.approval_workflow.run_worker
 
-    # Then run this script:
+    # Then run this script to start a workflow:
     python -m langgraph_samples.human_in_loop.approval_workflow.run_workflow
+
+    # Use the respond script to approve/reject:
+    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond <workflow-id> --approve --reason "OK"
 """
 
 import asyncio
+import uuid
 
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
@@ -32,10 +33,10 @@ async def main() -> None:
     config.setdefault("target_host", "localhost:7233")
     client = await Client.connect(**config)
 
-    # Example 1: Approve a request
-    print("\n" + "=" * 60)
-    print("Example 1: Approving a purchase request")
-    print("=" * 60)
+    # Generate a unique workflow ID
+    workflow_id = f"approval-{uuid.uuid4().hex[:8]}"
+
+    print(f"Starting approval workflow: {workflow_id}")
 
     handle = await client.start_workflow(
         ApprovalWorkflow.run,
@@ -44,72 +45,19 @@ async def main() -> None:
             amount=500.00,
             request_data={"item": "Office supplies", "vendor": "Acme Corp"},
         ),
-        id="approval-workflow-approve",
+        id=workflow_id,
         task_queue=TASK_QUEUE,
     )
 
-    # Wait for the workflow to reach the interrupt
-    await asyncio.sleep(1)
-
-    # Query the pending approval
-    pending = await handle.query(ApprovalWorkflow.get_pending_approval)
-    print(f"\nPending approval: {pending}")
-
-    status = await handle.query(ApprovalWorkflow.get_status)
-    print(f"Workflow status: {status}")
-
-    # Send approval signal
-    print("\nSending approval signal...")
-    await handle.signal(
-        ApprovalWorkflow.provide_approval,
-        {
-            "approved": True,
-            "reason": "Within budget",
-            "approver": "manager@example.com",
-        },
-    )
+    print(f"Workflow started. Waiting for result...")
+    print(f"\nTo approve/reject, use the run_respond script (see worker output for commands)")
 
-    # Wait for result
+    # Wait for the workflow to complete
     result = await handle.result()
-    print(f"\nResult: {result.get('result')}")
-    print(f"Executed: {result.get('executed')}")
-
-    # Example 2: Reject a request
-    print("\n" + "=" * 60)
-    print("Example 2: Rejecting a high-risk request")
-    print("=" * 60)
-
-    handle2 = await client.start_workflow(
-        ApprovalWorkflow.run,
-        ApprovalRequest(
-            request_type="transfer",
-            amount=50000.00,
-            request_data={"destination": "External account"},
-        ),
-        id="approval-workflow-reject",
-        task_queue=TASK_QUEUE,
-    )
 
-    await asyncio.sleep(1)
-
-    # Query pending approval
-    pending2 = await handle2.query(ApprovalWorkflow.get_pending_approval)
-    print(f"\nPending approval: {pending2}")
-
-    # Reject it
-    print("\nSending rejection signal...")
-    await handle2.signal(
-        ApprovalWorkflow.provide_approval,
-        {
-            "approved": False,
-            "reason": "Amount exceeds single-approval limit",
-            "approver": "compliance@example.com",
-        },
-    )
-
-    result2 = await handle2.result()
-    print(f"\nResult: {result2.get('result')}")
-    print(f"Executed: {result2.get('executed')}")
+    print(f"\n{'='*60}")
+    print(f"Result: {result.get('result')}")
+    print(f"Executed: {result.get('executed')}")
 
 
 if __name__ == "__main__":
diff --git a/langgraph_samples/human_in_loop/approval_workflow/workflow.py b/langgraph_samples/human_in_loop/approval_workflow/workflow.py
index 5b7fee06..bd8c94eb 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/workflow.py
+++ b/langgraph_samples/human_in_loop/approval_workflow/workflow.py
@@ -4,6 +4,7 @@
 - LangGraph's interrupt() for pausing execution
 - Temporal signals for receiving human input
 - Temporal queries for checking pending approvals
+- Notification activity to alert approvers
 - Timeout handling for approval deadlines
 """
 
@@ -16,6 +17,9 @@
 with workflow.unsafe.imports_passed_through():
     from langgraph.types import Command
 
+    from langgraph_samples.human_in_loop.approval_workflow.activities import (
+        notify_approver,
+    )
     from temporalio.contrib.langgraph import compile as lg_compile
 
 
@@ -119,6 +123,13 @@ async def run(
                 "Workflow paused for approval: %s", self._interrupt_value.get("message")
             )
 
+            # Notify the approver via activity
+            await workflow.execute_activity(
+                notify_approver,
+                self._interrupt_value,
+                start_to_close_timeout=timedelta(seconds=30),
+            )
+
             # Wait for approval signal (with optional timeout)
             try:
                 await workflow.wait_condition(

From ca793bf591171b54b94d064995c4b06009294b08 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 11:30:09 -0800
Subject: [PATCH 07/24] LangGraph: Wrap model with temporal_model for durable
 LLM calls in react_agent

The react_agent sample was using ChatOpenAI directly without wrapping
it with temporal_model(), meaning LLM calls were not durable. Now both
LLM calls and tool invocations run as Temporal activities.
---
 langgraph_samples/basic/react_agent/graph.py  | 28 +++++++++++--------
 .../basic/react_agent/workflow.py             | 10 +++----
 2 files changed, 22 insertions(+), 16 deletions(-)

diff --git a/langgraph_samples/basic/react_agent/graph.py b/langgraph_samples/basic/react_agent/graph.py
index c60b6f00..daa890a1 100644
--- a/langgraph_samples/basic/react_agent/graph.py
+++ b/langgraph_samples/basic/react_agent/graph.py
@@ -1,7 +1,7 @@
 """ReAct Agent Graph Definition.
 
 This module builds a ReAct agent using LangGraph's create_react_agent
-with Temporal-wrapped tools for durable execution.
+with Temporal-wrapped model and tools for fully durable execution.
 
 Note: This module is only imported by the worker (not by the workflow).
 LangGraph cannot be imported in the workflow sandbox.
@@ -13,7 +13,8 @@
 
 from langchain_openai import ChatOpenAI
 from langgraph.prebuilt import create_react_agent
-from temporalio.contrib.langgraph import temporal_tool
+
+from temporalio.contrib.langgraph import temporal_model, temporal_tool
 
 from langgraph_samples.basic.react_agent.tools import (
     calculate,
@@ -23,24 +24,29 @@
 
 
 def build_react_agent() -> Any:
-    """Build a ReAct agent with durable tool execution.
+    """Build a ReAct agent with fully durable execution.
 
-    This function creates a ReAct agent where tool invocations execute as
-    Temporal activities with durability and automatic retries.
+    This function creates a ReAct agent where both LLM calls and tool
+    invocations execute as Temporal activities with durability and
+    automatic retries.
 
     The agent follows the ReAct pattern:
-    1. Think: LLM decides what action to take
-    2. Act: Execute the chosen tool
+    1. Think: LLM decides what action to take (durable activity)
+    2. Act: Execute the chosen tool (durable activity)
     3. Observe: Feed tool results back to LLM
     4. Repeat until done
 
     Returns:
         A compiled LangGraph that can be executed with ainvoke().
     """
-    # Create the model
-    model = ChatOpenAI(
-        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
-        temperature=0,
+    # Create and wrap the model for durable LLM execution
+    # Each LLM call becomes a Temporal activity with its own timeout and retries
+    model = temporal_model(
+        ChatOpenAI(
+            model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+            temperature=0,
+        ),
+        start_to_close_timeout=timedelta(minutes=2),
     )
 
     # Wrap tools for durable execution as Temporal activities
diff --git a/langgraph_samples/basic/react_agent/workflow.py b/langgraph_samples/basic/react_agent/workflow.py
index c142d4e2..28ec9938 100644
--- a/langgraph_samples/basic/react_agent/workflow.py
+++ b/langgraph_samples/basic/react_agent/workflow.py
@@ -16,15 +16,15 @@
 
 @workflow.defn
 class ReActAgentWorkflow:
-    """Temporal workflow that executes a ReAct agent.
+    """Temporal workflow that executes a ReAct agent with fully durable execution.
 
     This workflow demonstrates:
-    - Using create_react_agent with temporal_model() for durable LLM calls
+    - Using temporal_model() to wrap the LLM for durable calls
     - Using temporal_tool() for durable tool execution
-    - The think-act-observe loop pattern
+    - The think-act-observe loop pattern (ReAct)
 
-    Each LLM call and tool invocation runs as a Temporal activity,
-    providing automatic retries and failure recovery.
+    Both LLM calls and tool invocations run as Temporal activities,
+    providing automatic retries, failure recovery, and replay safety.
     """
 
     @workflow.run

From 5adef7d7ffb14394ba522a7119354366051125ec Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 11:47:25 -0800
Subject: [PATCH 08/24] LangGraph: Update react_agent sample to use
 create_durable_react_agent

Use the new Temporal-provided create_durable_react_agent which:
- Automatically wraps model and tools for durable execution
- Runs agent nodes inline in workflow with model/tool calls as activities
---
 langgraph_samples/basic/react_agent/graph.py  | 52 +++++++++----------
 .../basic/react_agent/workflow.py             | 12 ++---
 2 files changed, 32 insertions(+), 32 deletions(-)

diff --git a/langgraph_samples/basic/react_agent/graph.py b/langgraph_samples/basic/react_agent/graph.py
index daa890a1..04a95a1b 100644
--- a/langgraph_samples/basic/react_agent/graph.py
+++ b/langgraph_samples/basic/react_agent/graph.py
@@ -1,7 +1,7 @@
 """ReAct Agent Graph Definition.
 
-This module builds a ReAct agent using LangGraph's create_react_agent
-with Temporal-wrapped model and tools for fully durable execution.
+This module builds a ReAct agent using Temporal's create_durable_react_agent
+for fully durable execution of both LLM calls and tool invocations.
 
 Note: This module is only imported by the worker (not by the workflow).
 LangGraph cannot be imported in the workflow sandbox.
@@ -12,9 +12,8 @@
 from typing import Any
 
 from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import create_react_agent
 
-from temporalio.contrib.langgraph import temporal_model, temporal_tool
+from temporalio.contrib.langgraph import create_durable_react_agent
 
 from langgraph_samples.basic.react_agent.tools import (
     calculate,
@@ -26,9 +25,13 @@
 def build_react_agent() -> Any:
     """Build a ReAct agent with fully durable execution.
 
-    This function creates a ReAct agent where both LLM calls and tool
-    invocations execute as Temporal activities with durability and
-    automatic retries.
+    Uses Temporal's create_durable_react_agent which automatically wraps:
+    - The model with temporal_model() for durable LLM calls
+    - The tools with temporal_tool() for durable tool execution
+
+    The agent nodes run inline in the workflow while model and tool
+    calls execute as separate Temporal activities with their own
+    timeouts and retry policies.
 
     The agent follows the ReAct pattern:
     1. Think: LLM decides what action to take (durable activity)
@@ -39,24 +42,21 @@ def build_react_agent() -> Any:
     Returns:
         A compiled LangGraph that can be executed with ainvoke().
     """
-    # Create and wrap the model for durable LLM execution
-    # Each LLM call becomes a Temporal activity with its own timeout and retries
-    model = temporal_model(
-        ChatOpenAI(
-            model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
-            temperature=0,
-        ),
-        start_to_close_timeout=timedelta(minutes=2),
+    # Create the model - will be automatically wrapped for durable execution
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
     )
 
-    # Wrap tools for durable execution as Temporal activities
-    # Each tool call becomes a Temporal activity with its own timeout and retries
-    tools = [
-        temporal_tool(get_weather, start_to_close_timeout=timedelta(seconds=30)),
-        temporal_tool(calculate, start_to_close_timeout=timedelta(seconds=10)),
-        temporal_tool(search_knowledge, start_to_close_timeout=timedelta(seconds=30)),
-    ]
-
-    # Create the ReAct agent using LangGraph's prebuilt function
-    # This creates a cyclic graph: agent -> tools -> agent (repeat until done)
-    return create_react_agent(model, tools)
+    # Tools - will be automatically wrapped for durable execution
+    tools = [get_weather, calculate, search_knowledge]
+
+    # Create the ReAct agent using Temporal's durable version
+    # This automatically wraps model and tools, and marks nodes to run
+    # inline in the workflow (with model/tool calls as activities)
+    return create_durable_react_agent(
+        model,
+        tools,
+        model_start_to_close_timeout=timedelta(minutes=2),
+        tool_start_to_close_timeout=timedelta(seconds=30),
+    )
diff --git a/langgraph_samples/basic/react_agent/workflow.py b/langgraph_samples/basic/react_agent/workflow.py
index 28ec9938..a124658d 100644
--- a/langgraph_samples/basic/react_agent/workflow.py
+++ b/langgraph_samples/basic/react_agent/workflow.py
@@ -18,13 +18,13 @@
 class ReActAgentWorkflow:
     """Temporal workflow that executes a ReAct agent with fully durable execution.
 
-    This workflow demonstrates:
-    - Using temporal_model() to wrap the LLM for durable calls
-    - Using temporal_tool() for durable tool execution
-    - The think-act-observe loop pattern (ReAct)
+    This workflow demonstrates using Temporal's create_durable_react_agent which:
+    - Runs agent nodes inline in the workflow (deterministic orchestration)
+    - Executes LLM calls as Temporal activities (durable, retryable)
+    - Executes tool calls as Temporal activities (durable, retryable)
 
-    Both LLM calls and tool invocations run as Temporal activities,
-    providing automatic retries, failure recovery, and replay safety.
+    This provides fine-grained durability where each LLM call and tool
+    invocation is individually recoverable on failure.
     """
 
     @workflow.run

From 3b62683b1b8b7aa54e5dd2f2d31222e9cc088ba4 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 12:01:25 -0800
Subject: [PATCH 09/24] LangGraph: Update react_agent to use new
 activity_options API

Use the new model_activity_options and tool_activity_options parameters
with activity_options() instead of the old individual timeout parameters.
---
 langgraph_samples/basic/react_agent/graph.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/langgraph_samples/basic/react_agent/graph.py b/langgraph_samples/basic/react_agent/graph.py
index 04a95a1b..86d1c8d8 100644
--- a/langgraph_samples/basic/react_agent/graph.py
+++ b/langgraph_samples/basic/react_agent/graph.py
@@ -13,7 +13,7 @@
 
 from langchain_openai import ChatOpenAI
 
-from temporalio.contrib.langgraph import create_durable_react_agent
+from temporalio.contrib.langgraph import activity_options, create_durable_react_agent
 
 from langgraph_samples.basic.react_agent.tools import (
     calculate,
@@ -57,6 +57,10 @@ def build_react_agent() -> Any:
     return create_durable_react_agent(
         model,
         tools,
-        model_start_to_close_timeout=timedelta(minutes=2),
-        tool_start_to_close_timeout=timedelta(seconds=30),
+        model_activity_options=activity_options(
+            start_to_close_timeout=timedelta(minutes=2),
+        ),
+        tool_activity_options=activity_options(
+            start_to_close_timeout=timedelta(seconds=30),
+        ),
     )

From 1fb468226d1827bd1df71612977806dadfd4f3c3 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 15:17:04 -0800
Subject: [PATCH 10/24] LangGraph: Use native create_react_agent instead of
 wrappers

Update react_agent sample to use LangGraph's native create_react_agent
instead of the removed create_durable_react_agent wrapper.

The Temporal integration now works directly with native LangGraph APIs -
each node runs as a Temporal activity, providing durability without
special wrappers.
---
 langgraph_samples/basic/react_agent/README.md | 17 ++++---
 langgraph_samples/basic/react_agent/graph.py  | 48 ++++++++-----------
 langgraph_samples/basic/react_agent/tools.py  |  5 +-
 .../basic/react_agent/workflow.py             | 15 +++---
 4 files changed, 41 insertions(+), 44 deletions(-)

diff --git a/langgraph_samples/basic/react_agent/README.md b/langgraph_samples/basic/react_agent/README.md
index 0861ad71..b2e2368e 100644
--- a/langgraph_samples/basic/react_agent/README.md
+++ b/langgraph_samples/basic/react_agent/README.md
@@ -1,26 +1,31 @@
 # ReAct Agent with Tools
 
-A ReAct (Reasoning + Acting) agent using LangGraph's `create_react_agent` with Temporal-wrapped tools for durable execution.
+A ReAct (Reasoning + Acting) agent using LangGraph's `create_react_agent` with Temporal for durable execution.
 
 ## What This Sample Demonstrates
 
 - **ReAct pattern**: The think-act-observe loop where the LLM decides actions and observes results
-- **Durable tool execution**: Using `temporal_tool()` to wrap LangChain tools as Temporal activities
-- **Automatic retries**: Each tool invocation has its own timeout and retry policy
+- **Durable execution**: Each graph node runs as a Temporal activity with automatic retries
+- **Crash recovery**: If the worker fails, execution resumes from the last completed node
 - **Cyclic graph execution**: The agent loops between thinking and acting until it has an answer
 
 ## How It Works
 
 1. **Tools**: Three LangChain tools (`get_weather`, `calculate`, `search_knowledge`) simulate external APIs
-2. **Temporal wrapping**: Each tool is wrapped with `temporal_tool()` for durable execution
-3. **ReAct agent**: `create_react_agent()` builds a cyclic graph that alternates between LLM calls and tool execution
+2. **ReAct agent**: `create_react_agent()` builds a cyclic graph with "agent" and "tools" nodes
+3. **Temporal integration**: Each node runs as a separate activity, providing durability
 4. **Workflow**: Invokes the agent and returns the final conversation state
 
 The ReAct pattern:
 ```
-User Query → [Think] → [Act (tool)] → [Observe] → [Think] → ... → Final Answer
+User Query → [Agent Node] → [Tools Node] → [Agent Node] → ... → Final Answer
 ```
 
+Each node execution is:
+- **Durable**: Progress is saved after each node completes
+- **Retryable**: Failed nodes can be automatically retried
+- **Recoverable**: If the worker crashes, execution resumes from the last completed node
+
 ## Prerequisites
 
 - Temporal server running locally (`temporal server start-dev`)
diff --git a/langgraph_samples/basic/react_agent/graph.py b/langgraph_samples/basic/react_agent/graph.py
index 86d1c8d8..b70e1215 100644
--- a/langgraph_samples/basic/react_agent/graph.py
+++ b/langgraph_samples/basic/react_agent/graph.py
@@ -1,19 +1,18 @@
 """ReAct Agent Graph Definition.
 
-This module builds a ReAct agent using Temporal's create_durable_react_agent
-for fully durable execution of both LLM calls and tool invocations.
+This module builds a ReAct agent using LangGraph's create_react_agent.
+Each node (agent reasoning, tool execution) runs as a Temporal activity,
+providing automatic retries and failure recovery.
 
 Note: This module is only imported by the worker (not by the workflow).
 LangGraph cannot be imported in the workflow sandbox.
 """
 
 import os
-from datetime import timedelta
 from typing import Any
 
 from langchain_openai import ChatOpenAI
-
-from temporalio.contrib.langgraph import activity_options, create_durable_react_agent
+from langgraph.prebuilt import create_react_agent
 
 from langgraph_samples.basic.react_agent.tools import (
     calculate,
@@ -23,44 +22,35 @@
 
 
 def build_react_agent() -> Any:
-    """Build a ReAct agent with fully durable execution.
+    """Build a ReAct agent with durable execution.
 
-    Uses Temporal's create_durable_react_agent which automatically wraps:
-    - The model with temporal_model() for durable LLM calls
-    - The tools with temporal_tool() for durable tool execution
+    Uses LangGraph's create_react_agent which creates a graph with:
+    - An "agent" node that calls the LLM to decide actions
+    - A "tools" node that executes the requested tools
 
-    The agent nodes run inline in the workflow while model and tool
-    calls execute as separate Temporal activities with their own
-    timeouts and retry policies.
+    The Temporal integration runs each node as a separate activity:
+    - Progress is saved after each node completes
+    - Failed nodes can be automatically retried
+    - If the worker crashes, execution resumes from the last completed node
 
     The agent follows the ReAct pattern:
-    1. Think: LLM decides what action to take (durable activity)
-    2. Act: Execute the chosen tool (durable activity)
+    1. Think: LLM decides what action to take (agent node)
+    2. Act: Execute the chosen tool (tools node)
     3. Observe: Feed tool results back to LLM
     4. Repeat until done
 
     Returns:
         A compiled LangGraph that can be executed with ainvoke().
     """
-    # Create the model - will be automatically wrapped for durable execution
+    # Create the model
     model = ChatOpenAI(
         model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
         temperature=0,
     )
 
-    # Tools - will be automatically wrapped for durable execution
+    # Define the tools
     tools = [get_weather, calculate, search_knowledge]
 
-    # Create the ReAct agent using Temporal's durable version
-    # This automatically wraps model and tools, and marks nodes to run
-    # inline in the workflow (with model/tool calls as activities)
-    return create_durable_react_agent(
-        model,
-        tools,
-        model_activity_options=activity_options(
-            start_to_close_timeout=timedelta(minutes=2),
-        ),
-        tool_activity_options=activity_options(
-            start_to_close_timeout=timedelta(seconds=30),
-        ),
-    )
+    # Create the ReAct agent using LangGraph's prebuilt function
+    # The Temporal integration will run each node as an activity
+    return create_react_agent(model, tools)
diff --git a/langgraph_samples/basic/react_agent/tools.py b/langgraph_samples/basic/react_agent/tools.py
index c2fe7098..014d0690 100644
--- a/langgraph_samples/basic/react_agent/tools.py
+++ b/langgraph_samples/basic/react_agent/tools.py
@@ -1,7 +1,8 @@
 """Tool definitions for the ReAct Agent.
 
-These tools demonstrate how to wrap LangChain tools for durable execution
-using temporal_tool().
+These are standard LangChain tools. When used with the Temporal LangGraph
+integration, they execute inside the "tools" node activity, which provides
+automatic retries and failure recovery.
 """
 
 from langchain_core.tools import tool
diff --git a/langgraph_samples/basic/react_agent/workflow.py b/langgraph_samples/basic/react_agent/workflow.py
index a124658d..79ddac03 100644
--- a/langgraph_samples/basic/react_agent/workflow.py
+++ b/langgraph_samples/basic/react_agent/workflow.py
@@ -1,3 +1,4 @@
+
 """ReAct Agent Workflow.
 
 Temporal workflow that executes the ReAct agent with durable execution.
@@ -16,15 +17,15 @@
 
 @workflow.defn
 class ReActAgentWorkflow:
-    """Temporal workflow that executes a ReAct agent with fully durable execution.
+    """Temporal workflow that executes a ReAct agent with durable execution.
 
-    This workflow demonstrates using Temporal's create_durable_react_agent which:
-    - Runs agent nodes inline in the workflow (deterministic orchestration)
-    - Executes LLM calls as Temporal activities (durable, retryable)
-    - Executes tool calls as Temporal activities (durable, retryable)
+    This workflow uses LangGraph's create_react_agent with the Temporal
+    integration. Each graph node runs as a Temporal activity:
+    - The "agent" node calls the LLM to decide what to do
+    - The "tools" node executes the requested tools
 
-    This provides fine-grained durability where each LLM call and tool
-    invocation is individually recoverable on failure.
+    If any node fails, it is automatically retried. If the worker crashes,
+    execution resumes from the last completed node.
     """
 
     @workflow.run

From c18f9cbe82a7a04f89ec508d46b6cb36e7077e82 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 15:18:14 -0800
Subject: [PATCH 11/24] LangGraph: Update SAMPLES_PROPOSAL.md to reflect
 simplified API

---
 langgraph_samples/SAMPLES_PROPOSAL.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/langgraph_samples/SAMPLES_PROPOSAL.md b/langgraph_samples/SAMPLES_PROPOSAL.md
index 27351a1e..36b0ea47 100644
--- a/langgraph_samples/SAMPLES_PROPOSAL.md
+++ b/langgraph_samples/SAMPLES_PROPOSAL.md
@@ -33,10 +33,10 @@ Samples are organized by complexity and use case, inspired by [OpenAI Agents sam
 **Description:** Classic ReAct (Reasoning + Acting) agent that can use tools to answer questions. Implements the think-act-observe loop.
 
 **Demonstrates:**
-- `temporal_model()` for durable LLM calls
-- `temporal_tool()` for durable tool execution
-- `create_react_agent` integration
-- Automatic retries on LLM/tool failures
+- Native LangGraph `create_react_agent` with Temporal integration
+- Durable execution where each node runs as a Temporal activity
+- Automatic retries and crash recovery at the node level
+- Cyclic graph execution (agent → tools → agent → ...)
 
 **Original Reference:** [LangGraph ReAct Agent Template](https://github.com/langchain-ai/react-agent)
 

From d3fe11693cfecdca7f1d58ef916ec037b389c781 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 15:33:09 -0800
Subject: [PATCH 12/24] LangGraph: Use create_agent from langchain.agents
 (create_react_agent deprecated)

---
 langgraph_samples/SAMPLES_PROPOSAL.md         |  2 +-
 langgraph_samples/basic/react_agent/README.md |  4 ++--
 langgraph_samples/basic/react_agent/graph.py  | 13 +++++++------
 3 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/langgraph_samples/SAMPLES_PROPOSAL.md b/langgraph_samples/SAMPLES_PROPOSAL.md
index 36b0ea47..648bb18d 100644
--- a/langgraph_samples/SAMPLES_PROPOSAL.md
+++ b/langgraph_samples/SAMPLES_PROPOSAL.md
@@ -33,7 +33,7 @@ Samples are organized by complexity and use case, inspired by [OpenAI Agents sam
 **Description:** Classic ReAct (Reasoning + Acting) agent that can use tools to answer questions. Implements the think-act-observe loop.
 
 **Demonstrates:**
-- Native LangGraph `create_react_agent` with Temporal integration
+- LangChain's `create_agent` with Temporal integration
 - Durable execution where each node runs as a Temporal activity
 - Automatic retries and crash recovery at the node level
 - Cyclic graph execution (agent → tools → agent → ...)
diff --git a/langgraph_samples/basic/react_agent/README.md b/langgraph_samples/basic/react_agent/README.md
index b2e2368e..088a45c1 100644
--- a/langgraph_samples/basic/react_agent/README.md
+++ b/langgraph_samples/basic/react_agent/README.md
@@ -1,6 +1,6 @@
 # ReAct Agent with Tools
 
-A ReAct (Reasoning + Acting) agent using LangGraph's `create_react_agent` with Temporal for durable execution.
+A ReAct (Reasoning + Acting) agent using LangChain's `create_agent` with Temporal for durable execution.
 
 ## What This Sample Demonstrates
 
@@ -12,7 +12,7 @@ A ReAct (Reasoning + Acting) agent using LangGraph's `create_react_agent` with T
 ## How It Works
 
 1. **Tools**: Three LangChain tools (`get_weather`, `calculate`, `search_knowledge`) simulate external APIs
-2. **ReAct agent**: `create_react_agent()` builds a cyclic graph with "agent" and "tools" nodes
+2. **Agent**: `create_agent()` builds a cyclic graph with "agent" and "tools" nodes
 3. **Temporal integration**: Each node runs as a separate activity, providing durability
 4. **Workflow**: Invokes the agent and returns the final conversation state
 
diff --git a/langgraph_samples/basic/react_agent/graph.py b/langgraph_samples/basic/react_agent/graph.py
index b70e1215..aa6da965 100644
--- a/langgraph_samples/basic/react_agent/graph.py
+++ b/langgraph_samples/basic/react_agent/graph.py
@@ -1,6 +1,6 @@
 """ReAct Agent Graph Definition.
 
-This module builds a ReAct agent using LangGraph's create_react_agent.
+This module builds an agent using LangChain's create_agent.
 Each node (agent reasoning, tool execution) runs as a Temporal activity,
 providing automatic retries and failure recovery.
 
@@ -11,8 +11,8 @@
 import os
 from typing import Any
 
+from langchain.agents import create_agent
 from langchain_openai import ChatOpenAI
-from langgraph.prebuilt import create_react_agent
 
 from langgraph_samples.basic.react_agent.tools import (
     calculate,
@@ -22,9 +22,9 @@
 
 
 def build_react_agent() -> Any:
-    """Build a ReAct agent with durable execution.
+    """Build an agent with durable execution.
 
-    Uses LangGraph's create_react_agent which creates a graph with:
+    Uses LangChain's create_agent which creates a graph with:
     - An "agent" node that calls the LLM to decide actions
     - A "tools" node that executes the requested tools
 
@@ -51,6 +51,7 @@ def build_react_agent() -> Any:
     # Define the tools
     tools = [get_weather, calculate, search_knowledge]
 
-    # Create the ReAct agent using LangGraph's prebuilt function
+    # Create the agent using LangChain's create_agent
+    # (Note: langgraph.prebuilt.create_react_agent is deprecated)
     # The Temporal integration will run each node as an activity
-    return create_react_agent(model, tools)
+    return create_agent(model, tools)

From 771b46a9e90438079aa4c0b755c19e6214d151b1 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 18:40:18 -0800
Subject: [PATCH 13/24] LangGraph: Update react_agent sample to demonstrate
 multi-step agentic loop

- Change query to require multiple tool calls (weather + temperature conversion)
- Remove unused search_knowledge tool, keep only get_weather and calculate
- Update README to document multi-step reasoning behavior
---
 langgraph_samples/basic/react_agent/README.md | 20 ++++++++++------
 langgraph_samples/basic/react_agent/graph.py  | 11 ++++-----
 .../basic/react_agent/run_workflow.py         |  5 +++-
 langgraph_samples/basic/react_agent/tools.py  | 24 -------------------
 4 files changed, 21 insertions(+), 39 deletions(-)

diff --git a/langgraph_samples/basic/react_agent/README.md b/langgraph_samples/basic/react_agent/README.md
index 088a45c1..9b9e645c 100644
--- a/langgraph_samples/basic/react_agent/README.md
+++ b/langgraph_samples/basic/react_agent/README.md
@@ -5,22 +5,28 @@ A ReAct (Reasoning + Acting) agent using LangChain's `create_agent` with Tempora
 ## What This Sample Demonstrates
 
 - **ReAct pattern**: The think-act-observe loop where the LLM decides actions and observes results
+- **Multi-step reasoning**: The agent makes multiple tool calls to gather information and compute results
 - **Durable execution**: Each graph node runs as a Temporal activity with automatic retries
 - **Crash recovery**: If the worker fails, execution resumes from the last completed node
 - **Cyclic graph execution**: The agent loops between thinking and acting until it has an answer
 
 ## How It Works
 
-1. **Tools**: Three LangChain tools (`get_weather`, `calculate`, `search_knowledge`) simulate external APIs
+1. **Tools**: Two LangChain tools (`get_weather`, `calculate`) provide weather data and math operations
 2. **Agent**: `create_agent()` builds a cyclic graph with "agent" and "tools" nodes
 3. **Temporal integration**: Each node runs as a separate activity, providing durability
 4. **Workflow**: Invokes the agent and returns the final conversation state
 
 The ReAct pattern:
 ```
-User Query → [Agent Node] → [Tools Node] → [Agent Node] → ... → Final Answer
+User Query → [Agent Node] → [Tools Node] → [Agent Node] → [Tools Node] → ... → Final Answer
 ```
 
+The sample query "What's the weather in Tokyo? Convert the temperature to Celsius." demonstrates multiple agentic loops:
+1. Agent decides to call `get_weather("Tokyo")` → gets "68°F, Clear skies"
+2. Agent decides to call `calculate("(68-32)*5/9")` → gets the Celsius conversion
+3. Agent synthesizes final answer
+
 Each node execution is:
 - **Durable**: Progress is saved after each node completes
 - **Retryable**: Failed nodes can be automatically retried
@@ -46,10 +52,10 @@ uv run langgraph_samples/basic/react_agent/run_workflow.py
 ## Expected Output
 
 ```
-The weather in Tokyo is currently 68°F with clear skies.
+The weather in Tokyo is 68°F (20°C) with clear skies.
 ```
 
-You can modify the query in `run_workflow.py` to test different tools:
-- **Weather tool**: "What's the weather like in Tokyo?"
-- **Calculator tool**: "What is 25 * 4 + 10?"
-- **Knowledge search**: "Tell me about Temporal and LangGraph"
+You can modify the query in `run_workflow.py` to test different scenarios:
+- **Weather only**: "What's the weather like in Paris?"
+- **Calculator only**: "What is 25 * 4 + 10?"
+- **Multi-step**: "What's the weather in New York and London? Which city is warmer?"
diff --git a/langgraph_samples/basic/react_agent/graph.py b/langgraph_samples/basic/react_agent/graph.py
index aa6da965..d17a12ec 100644
--- a/langgraph_samples/basic/react_agent/graph.py
+++ b/langgraph_samples/basic/react_agent/graph.py
@@ -14,11 +14,7 @@
 from langchain.agents import create_agent
 from langchain_openai import ChatOpenAI
 
-from langgraph_samples.basic.react_agent.tools import (
-    calculate,
-    get_weather,
-    search_knowledge,
-)
+from langgraph_samples.basic.react_agent.tools import calculate, get_weather
 
 
 def build_react_agent() -> Any:
@@ -48,8 +44,9 @@ def build_react_agent() -> Any:
         temperature=0,
     )
 
-    # Define the tools
-    tools = [get_weather, calculate, search_knowledge]
+    # Define the tools - get_weather and calculate
+    # The sample query requires both: get weather, then calculate F to C conversion
+    tools = [get_weather, calculate]
 
     # Create the agent using LangChain's create_agent
     # (Note: langgraph.prebuilt.create_react_agent is deprecated)
diff --git a/langgraph_samples/basic/react_agent/run_workflow.py b/langgraph_samples/basic/react_agent/run_workflow.py
index 4ab4229f..ceaf345a 100644
--- a/langgraph_samples/basic/react_agent/run_workflow.py
+++ b/langgraph_samples/basic/react_agent/run_workflow.py
@@ -21,9 +21,12 @@ async def main() -> None:
     config.setdefault("target_host", "localhost:7233")
     client = await Client.connect(**config)
 
+    # This query requires multiple tool calls:
+    # 1. First, get_weather to find the temperature in Tokyo
+    # 2. Then, calculate to convert Fahrenheit to Celsius
     result = await client.execute_workflow(
         ReActAgentWorkflow.run,
-        "What's the weather like in Tokyo?",
+        "What's the weather in Tokyo? Convert the temperature to Celsius.",
         id="react-agent-workflow",
         task_queue="langgraph-react-agent",
     )
diff --git a/langgraph_samples/basic/react_agent/tools.py b/langgraph_samples/basic/react_agent/tools.py
index 014d0690..76fb7e1e 100644
--- a/langgraph_samples/basic/react_agent/tools.py
+++ b/langgraph_samples/basic/react_agent/tools.py
@@ -50,27 +50,3 @@ def calculate(expression: str) -> str:
         return str(result)
     except Exception as e:
         return f"Error calculating: {e}"
-
-
-@tool
-def search_knowledge(query: str) -> str:
-    """Search a knowledge base for information.
-
-    Args:
-        query: The search query.
-
-    Returns:
-        Relevant information from the knowledge base.
-    """
-    # Simulated knowledge base - in production, use a real search/RAG system
-    knowledge = {
-        "temporal": "Temporal is a durable execution platform that helps developers build reliable applications. It provides fault tolerance, state management, and workflow orchestration.",
-        "langgraph": "LangGraph is a library for building stateful, multi-actor applications with LLMs. It enables creating agent workflows with cycles, conditionals, and state management.",
-        "react": "ReAct (Reasoning + Acting) is an agent pattern where the AI alternates between thinking about what to do and taking actions. It improves reliability by making the reasoning process explicit.",
-        "python": "Python is a high-level programming language known for its readability and versatility. It's widely used in AI/ML, web development, and automation.",
-    }
-    query_lower = query.lower()
-    for key, value in knowledge.items():
-        if key in query_lower:
-            return value
-    return f"No specific information found for '{query}'. Try searching for: temporal, langgraph, react, or python."

From f579c0dfc570cb5f97743fcc5854ad7410a0bd0f Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sat, 27 Dec 2025 23:32:24 -0800
Subject: [PATCH 14/24] LangGraph: Add RAG sample and update documentation

- Add agentic_rag sample with document grading and query rewriting
- Add README files for human_in_loop and RAG sample categories
- Update main README to link to all sample categories
- Add langchain and langchain-openai dependencies for RAG sample
---
 langgraph_samples/README.md                   |   2 +
 langgraph_samples/human_in_loop/README.md     |  21 ++
 langgraph_samples/rag/README.md               |  21 ++
 langgraph_samples/rag/__init__.py             |   1 +
 langgraph_samples/rag/agentic_rag/README.md   | 111 ++++++
 langgraph_samples/rag/agentic_rag/__init__.py |   1 +
 langgraph_samples/rag/agentic_rag/graph.py    | 337 ++++++++++++++++++
 .../rag/agentic_rag/run_worker.py             |  56 +++
 .../rag/agentic_rag/run_workflow.py           |  41 +++
 langgraph_samples/rag/agentic_rag/workflow.py |  61 ++++
 pyproject.toml                                |   3 +
 uv.lock                                       |   6 +
 12 files changed, 661 insertions(+)
 create mode 100644 langgraph_samples/human_in_loop/README.md
 create mode 100644 langgraph_samples/rag/README.md
 create mode 100644 langgraph_samples/rag/__init__.py
 create mode 100644 langgraph_samples/rag/agentic_rag/README.md
 create mode 100644 langgraph_samples/rag/agentic_rag/__init__.py
 create mode 100644 langgraph_samples/rag/agentic_rag/graph.py
 create mode 100644 langgraph_samples/rag/agentic_rag/run_worker.py
 create mode 100644 langgraph_samples/rag/agentic_rag/run_workflow.py
 create mode 100644 langgraph_samples/rag/agentic_rag/workflow.py

diff --git a/langgraph_samples/README.md b/langgraph_samples/README.md
index 69ad2d33..e7f21695 100644
--- a/langgraph_samples/README.md
+++ b/langgraph_samples/README.md
@@ -22,3 +22,5 @@ This approach ensures that AI agent workflows are durable, observable, and can h
 Each directory contains complete examples with their own README for detailed instructions:
 
 - **[Basic Examples](./basic/README.md)** - Simple examples including a hello world agent demonstrating basic plugin setup and graph registration.
+- **[Human-in-the-Loop](./human_in_loop/README.md)** - Examples demonstrating interrupt/resume workflows with human approval.
+- **[RAG (Retrieval Augmented Generation)](./rag/README.md)** - Intelligent retrieval with document grading and query rewriting.
diff --git a/langgraph_samples/human_in_loop/README.md b/langgraph_samples/human_in_loop/README.md
new file mode 100644
index 00000000..de76345d
--- /dev/null
+++ b/langgraph_samples/human_in_loop/README.md
@@ -0,0 +1,21 @@
+# Human-in-the-Loop Samples
+
+This directory contains samples demonstrating human-in-the-loop patterns with Temporal LangGraph integration.
+
+## Available Samples
+
+### [Approval Workflow](./approval_workflow/README.md)
+
+An agent that pauses for human approval before taking actions, using LangGraph's `interrupt()` with Temporal signals.
+
+**Key Features:**
+- `interrupt()` for pausing execution
+- Temporal signals for receiving human input
+- Workflow queries for checking pending approvals
+- Durable waiting with automatic timeout handling
+
+## Prerequisites
+
+- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+- Dependencies installed via `uv sync --group langgraph`
diff --git a/langgraph_samples/rag/README.md b/langgraph_samples/rag/README.md
new file mode 100644
index 00000000..84f91d26
--- /dev/null
+++ b/langgraph_samples/rag/README.md
@@ -0,0 +1,21 @@
+# RAG (Retrieval Augmented Generation) Samples
+
+This directory contains samples demonstrating RAG patterns with Temporal LangGraph integration.
+
+## Available Samples
+
+### [Agentic RAG](./agentic_rag/README.md)
+
+An intelligent RAG system that decides when to retrieve documents, grades their relevance, and can rewrite queries when initial retrieval fails.
+
+**Key Features:**
+- Agent-driven retrieval decisions
+- Document relevance grading
+- Query rewriting for better retrieval
+- Durable execution with Temporal
+
+## Prerequisites
+
+- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+- Dependencies installed via `uv sync --group langgraph`
diff --git a/langgraph_samples/rag/__init__.py b/langgraph_samples/rag/__init__.py
new file mode 100644
index 00000000..675e2c58
--- /dev/null
+++ b/langgraph_samples/rag/__init__.py
@@ -0,0 +1 @@
+"""RAG (Retrieval Augmented Generation) samples for Temporal LangGraph."""
diff --git a/langgraph_samples/rag/agentic_rag/README.md b/langgraph_samples/rag/agentic_rag/README.md
new file mode 100644
index 00000000..4b130171
--- /dev/null
+++ b/langgraph_samples/rag/agentic_rag/README.md
@@ -0,0 +1,111 @@
+# Agentic RAG
+
+An intelligent RAG (Retrieval Augmented Generation) agent that decides when to retrieve documents, grades their relevance, and rewrites queries when needed.
+
+## What This Sample Demonstrates
+
+- **`create_agent` pattern**: Uses LangChain's `create_agent` for the retrieval step
+- **Document grading**: Retrieved documents are evaluated for relevance before generating answers
+- **Query rewriting**: If documents aren't relevant, the query is reformulated and retrieval is retried
+- **Durable execution**: Graph nodes run as Temporal activities with automatic retries
+- **Crash recovery**: If the worker fails, execution resumes from the last completed node
+
+## How It Works
+
+The graph implements an agentic RAG pattern with `create_agent` for retrieval:
+
+```
+                              +-------+
+                              | START |
+                              +---+---+
+                                  |
+                                  v
+                        +-----------------+
+             +--------->| retrieve_agent  |<--------+
+             |          | (create_agent)  |         |
+             |          +-----------------+         |
+             |                  |                   |
+             |     "not relevant"   "relevant"      |
+             |          |                |          |
+         +---+---+      |                |     +----+
+         |rewrite|<-----+                +---->| END|
+         +-------+                             +----+
+                                                 ^
+                                                 |
+                                           +-----+----+
+                                           | generate |
+                                           +----------+
+```
+
+1. **retrieve_agent**: Uses `create_agent` to decide whether to retrieve and fetch documents (runs as single activity)
+2. **grade_documents**: Conditional edge that evaluates document relevance
+3. **generate**: Produces the final answer using relevant documents (runs as activity)
+4. **rewrite**: Reformulates the query if documents weren't relevant, then retries (runs as activity)
+
+### Subgraph Behavior
+
+The Temporal LangGraph plugin automatically detects subgraphs (like `create_agent`) and executes their **inner nodes as separate activities**. This means:
+
+- The retrieve_agent subgraph's `model` and `tools` nodes run as separate Temporal activities
+- Each node has its own retry/timeout configuration
+- If the worker crashes during retrieval, execution resumes from the last completed inner node
+- You get full durability without manually adding separate nodes
+
+The sample includes a knowledge base with documents about:
+- LangGraph features and capabilities
+- Temporal concepts (workflows, activities, signals, queries)
+- ReAct pattern for AI agents
+- Agentic RAG patterns
+- Human-in-the-loop workflows
+
+## Prerequisites
+
+- Temporal server running locally (`temporal server start-dev`)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run langgraph_samples/rag/agentic_rag/run_worker.py
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run langgraph_samples/rag/agentic_rag/run_workflow.py
+```
+
+## Expected Output
+
+```
+The ReAct (Reasoning and Acting) pattern is an approach where an LLM alternates
+between thinking about what to do and taking actions. The loop consists of:
+1. Think - The LLM reasons about the current state and what action to take
+2. Act - Execute the chosen action (e.g., call a tool)
+3. Observe - Process the result of the action
+4. Repeat until the task is complete
+
+When combined with Temporal, the ReAct pattern gains durability because each
+graph node runs as a Temporal activity. This means progress is saved after each
+node completes, and if the worker crashes, execution resumes from the last
+completed node.
+```
+
+## Sample Queries
+
+You can modify the query in `run_workflow.py` to test different scenarios:
+
+- **Retrieval needed**: "What is LangGraph and how does it work?"
+- **Multi-topic**: "How do Temporal signals work with human-in-the-loop workflows?"
+- **Off-topic (no retrieval)**: "What's 2 + 2?" (agent responds directly)
+- **Needs rewriting**: "Tell me about durable stuff" (vague query gets rewritten)
+
+## Architecture Notes
+
+The sample uses an in-memory vector store with sample documents for simplicity. In production, you would:
+
+1. Use a persistent vector store (Pinecone, Weaviate, Chroma, etc.)
+2. Load documents from your actual knowledge base
+3. Configure appropriate chunk sizes and embedding models
+
+The document grading step adds latency but significantly improves answer quality by ensuring the RAG system only uses relevant context.
diff --git a/langgraph_samples/rag/agentic_rag/__init__.py b/langgraph_samples/rag/agentic_rag/__init__.py
new file mode 100644
index 00000000..28df7238
--- /dev/null
+++ b/langgraph_samples/rag/agentic_rag/__init__.py
@@ -0,0 +1 @@
+"""Agentic RAG sample with document grading and query rewriting."""
diff --git a/langgraph_samples/rag/agentic_rag/graph.py b/langgraph_samples/rag/agentic_rag/graph.py
new file mode 100644
index 00000000..d032b370
--- /dev/null
+++ b/langgraph_samples/rag/agentic_rag/graph.py
@@ -0,0 +1,337 @@
+"""Agentic RAG Graph Definition.
+
+This module implements an agentic RAG (Retrieval Augmented Generation) system
+that intelligently decides when to retrieve documents, grades their relevance,
+and can rewrite queries when documents aren't helpful.
+
+The graph uses create_agent for the retrieval step, which provides a convenient
+way to build a ReAct-style agent. The outer graph adds document grading and
+query rewriting logic.
+
+Flow:
+1. retrieve_agent - Uses create_agent to decide and fetch documents
+2. grade_documents - Evaluates document relevance (conditional routing)
+3. generate - Produces final answer using relevant documents
+4. rewrite - Reformulates query if documents aren't relevant, then retries
+
+Architecture Note:
+The Temporal LangGraph plugin automatically detects subgraphs (like create_agent)
+and executes their inner nodes as separate activities. This means:
+- The retrieve_agent subgraph's 'model' and 'tools' nodes run as separate activities
+- Each tool call has its own retry/timeout configuration
+- If the worker crashes, execution resumes from the last completed inner node
+- The generate, rewrite nodes in the outer graph also run as separate activities
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from typing import Annotated, Any, Literal, Sequence, cast
+
+from langchain.agents import create_agent
+from langchain_core.documents import Document
+from langchain_core.messages import BaseMessage, HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools.retriever import create_retriever_tool
+from langchain_core.vectorstores import InMemoryVectorStore
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from typing_extensions import TypedDict
+
+# Sample documents about AI agents and LangGraph for the knowledge base
+SAMPLE_DOCUMENTS = [
+    Document(
+        page_content="""LangGraph is a library for building stateful, multi-actor applications with LLMs.
+        It extends LangChain with the ability to coordinate multiple chains (or actors) across
+        multiple steps of computation in a cyclic manner. Key features include:
+        - Support for cycles and branching in agent workflows
+        - Built-in persistence for pausing and resuming
+        - Human-in-the-loop support with interrupts
+        - Streaming support for real-time updates""",
+        metadata={"source": "langgraph_overview", "topic": "langgraph"},
+    ),
+    Document(
+        page_content="""Temporal is a durable execution platform that enables developers to build
+        applications that are reliable by default. Key concepts include:
+        - Workflows: Long-running, reliable processes that survive failures
+        - Activities: Individual units of work that can be retried
+        - Signals: External events that can be sent to running workflows
+        - Queries: Read-only operations to inspect workflow state
+        The Temporal LangGraph integration runs each graph node as a Temporal activity.""",
+        metadata={"source": "temporal_overview", "topic": "temporal"},
+    ),
+    Document(
+        page_content="""The ReAct (Reasoning and Acting) pattern is an approach where an LLM
+        alternates between thinking about what to do and taking actions. The loop is:
+        1. Think: The LLM reasons about the current state and what action to take
+        2. Act: Execute the chosen action (e.g., call a tool)
+        3. Observe: Process the result of the action
+        4. Repeat until the task is complete
+        This pattern allows LLMs to effectively use tools and handle multi-step tasks.""",
+        metadata={"source": "react_pattern", "topic": "agents"},
+    ),
+    Document(
+        page_content="""Agentic RAG (Retrieval Augmented Generation) is an advanced pattern where
+        an AI agent decides when and how to retrieve information. Unlike basic RAG which always
+        retrieves, agentic RAG can:
+        - Decide if retrieval is needed based on the query
+        - Grade retrieved documents for relevance
+        - Rewrite queries if initial retrieval fails
+        - Combine multiple retrieval strategies
+        This leads to more efficient and accurate responses.""",
+        metadata={"source": "agentic_rag_overview", "topic": "rag"},
+    ),
+    Document(
+        page_content="""Human-in-the-loop workflows allow AI systems to pause and wait for
+        human input at critical decision points. In LangGraph, this is implemented using
+        the interrupt() function. When combined with Temporal:
+        - The workflow pauses durably while waiting for human input
+        - Temporal signals are used to receive the human response
+        - The workflow resumes exactly where it left off
+        This enables approval workflows, human review of AI outputs, and collaborative AI.""",
+        metadata={"source": "hitl_overview", "topic": "human_in_loop"},
+    ),
+]
+
+
+class AgentState(TypedDict):
+    """State schema for the agentic RAG graph.
+
+    Attributes:
+        messages: Conversation history with add_messages reducer for merging.
+        docs_relevant: Whether retrieved documents are relevant (set by grade_documents).
+    """
+
+    messages: Annotated[Sequence[BaseMessage], add_messages]
+    docs_relevant: bool
+
+
+class DocumentGrade(BaseModel):
+    """Grade for document relevance."""
+
+    binary_score: Literal["yes", "no"] = Field(
+        description="Documents are relevant to the question, 'yes' or 'no'"
+    )
+
+
+def _create_retriever() -> Any:
+    """Create a retriever with sample documents.
+
+    Returns:
+        A retriever that can search the sample knowledge base.
+    """
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+    vectorstore = InMemoryVectorStore(embeddings)
+    vectorstore.add_documents(SAMPLE_DOCUMENTS)
+    return vectorstore.as_retriever(search_kwargs={"k": 2})
+
+
+def build_agentic_rag_graph() -> Any:
+    """Build an agentic RAG graph with document grading.
+
+    Uses create_agent for the retrieval step. The outer graph adds document
+    grading and query rewriting logic around the retrieval agent.
+
+    Flow:
+    1. retrieve_agent decides whether to retrieve and fetches docs
+    2. grade_documents checks relevance (conditional edge)
+    3. generate produces answer OR rewrite reformulates query
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    # Create the model
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Create retriever and tool
+    retriever = _create_retriever()
+    retriever_tool = create_retriever_tool(
+        retriever,
+        "retrieve_knowledge",
+        "Search the knowledge base for information about LangGraph, Temporal, "
+        "AI agents, RAG patterns, and human-in-the-loop workflows. "
+        "Use this when the user asks about these topics.",
+    )
+    tools = [retriever_tool]
+
+    # Build retrieval agent using create_agent
+    # Note: create_agent returns a compiled graph. The Temporal plugin automatically
+    # detects this subgraph and runs its inner nodes (model, tools) as separate activities.
+    retrieval_graph: Any = create_agent(model, tools)
+    retrieve_agent = retrieval_graph.compile() if hasattr(retrieval_graph, 'compile') else retrieval_graph
+
+    # Grade documents for relevance (as a node/activity, not conditional edge)
+    def grade_documents(state: AgentState) -> dict[str, Any]:
+        """Grade retrieved documents for relevance.
+
+        This is a node (activity) that examines the retrieved documents and
+        determines if they contain information relevant to answering the
+        user's question. The result is stored in state for routing.
+
+        Note: This must be a node (not conditional edge) because it makes
+        an LLM call. LLM calls should run as Temporal activities for proper
+        durability and retry handling.
+        """
+        # Set up structured output for grading
+        grader = model.with_structured_output(DocumentGrade)
+
+        # Get the question (first human message) and docs (last message with tool results)
+        messages = state["messages"]
+        question = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)), ""
+        )
+
+        # Find the retrieved documents in the messages
+        docs_content = ""
+        for msg in reversed(messages):
+            if hasattr(msg, "content") and isinstance(msg.content, str):
+                # Tool messages contain the retrieved documents
+                if hasattr(msg, "tool_call_id"):
+                    docs_content = msg.content
+                    break
+
+        if not docs_content:
+            return {"docs_relevant": False}
+
+        # Grade the documents
+        grade_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    "You are a grader assessing relevance of retrieved documents to a user question.\n"
+                    "If the documents contain information related to the question, grade as relevant.\n"
+                    "Give a binary score 'yes' or 'no' to indicate relevance.",
+                ),
+                (
+                    "human",
+                    "Retrieved documents:\n{documents}\n\nUser question: {question}",
+                ),
+            ]
+        )
+
+        chain = grade_prompt | grader
+        result = cast(
+            DocumentGrade,
+            chain.invoke({"documents": docs_content, "question": question}),
+        )
+
+        return {"docs_relevant": result.binary_score == "yes"}
+
+    # Simple routing function (no LLM call - safe for workflow context)
+    def route_after_grading(state: AgentState) -> Literal["generate", "rewrite"]:
+        """Route based on document relevance grade.
+
+        This is a pure function that just checks the grade result.
+        Safe to run in workflow context since it makes no external calls.
+        """
+        return "generate" if state.get("docs_relevant", False) else "rewrite"
+
+    # Generate answer using retrieved documents
+    def generate(state: AgentState) -> dict[str, Any]:
+        """Generate answer using the retrieved documents.
+
+        Takes the relevant documents and user question to produce
+        a well-grounded response.
+        """
+        messages = state["messages"]
+        question = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)), ""
+        )
+
+        # Find the retrieved documents
+        docs_content = ""
+        for msg in reversed(messages):
+            if hasattr(msg, "tool_call_id") and hasattr(msg, "content"):
+                content = msg.content
+                docs_content = content if isinstance(content, str) else str(content)
+                break
+
+        # Generate response using RAG prompt
+        rag_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    "You are an assistant answering questions using the provided context.\n"
+                    "Use the following retrieved documents to answer the question.\n"
+                    "If the documents don't contain enough information, say so.\n"
+                    "Keep your answer concise and well-structured.",
+                ),
+                ("human", "Context:\n{context}\n\nQuestion: {question}"),
+            ]
+        )
+
+        chain = rag_prompt | model | StrOutputParser()
+        response = chain.invoke({"context": docs_content, "question": question})
+
+        return {"messages": [{"role": "assistant", "content": response}]}
+
+    # Rewrite query for better retrieval
+    def rewrite(state: AgentState) -> dict[str, Any]:
+        """Rewrite the query to improve retrieval.
+
+        Analyzes the original question and formulates an improved version
+        that may yield more relevant documents.
+        """
+        messages = state["messages"]
+        question = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)), ""
+        )
+
+        rewrite_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    "You are a question rewriter. Look at the input question and try to reason "
+                    "about the underlying intent. Reformulate the question to be more specific "
+                    "and likely to retrieve relevant documents about LangGraph, Temporal, AI agents, "
+                    "or related topics.",
+                ),
+                (
+                    "human",
+                    "Original question: {question}\n\nFormulate an improved question:",
+                ),
+            ]
+        )
+
+        chain = rewrite_prompt | model | StrOutputParser()
+        improved_question = chain.invoke({"question": question})
+
+        return {"messages": [HumanMessage(content=improved_question)]}
+
+    # Build the outer graph with grading logic
+    workflow = StateGraph(AgentState)
+
+    # Add nodes
+    # retrieve_agent is a compiled graph from create_agent - inner nodes run as separate activities
+    workflow.add_node("retrieve_agent", retrieve_agent)
+    workflow.add_node("grade_documents", grade_documents)  # LLM grading as activity
+    workflow.add_node("generate", generate)
+    workflow.add_node("rewrite", rewrite)
+
+    # Add edges
+    workflow.add_edge(START, "retrieve_agent")
+
+    # After retrieval, grade documents (as activity)
+    workflow.add_edge("retrieve_agent", "grade_documents")
+
+    # Route based on grade result (pure function, no LLM call)
+    workflow.add_conditional_edges(
+        "grade_documents",
+        route_after_grading,
+        {"generate": "generate", "rewrite": "rewrite"},
+    )
+
+    # Generate produces final answer
+    workflow.add_edge("generate", END)
+
+    # Rewrite goes back to retrieve_agent to try again
+    workflow.add_edge("rewrite", "retrieve_agent")
+
+    return workflow.compile()
diff --git a/langgraph_samples/rag/agentic_rag/run_worker.py b/langgraph_samples/rag/agentic_rag/run_worker.py
new file mode 100644
index 00000000..e82598d6
--- /dev/null
+++ b/langgraph_samples/rag/agentic_rag/run_worker.py
@@ -0,0 +1,56 @@
+"""Worker for the Agentic RAG sample.
+
+Starts a Temporal worker that can execute AgenticRAGWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+import logging
+import sys
+
+# Configure logging to see workflow.logger output
+logging.basicConfig(
+    stream=sys.stdout,
+    level=logging.INFO,
+    format="%(asctime)s  %(levelname)-8s  %(name)s  %(message)s",
+)
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_samples.rag.agentic_rag.graph import build_agentic_rag_graph
+from langgraph_samples.rag.agentic_rag.workflow import AgenticRAGWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the agentic RAG graph registered
+    plugin = LangGraphPlugin(
+        graphs={"agentic_rag": build_agentic_rag_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: Activities (LLM calls, retrieval, grading) are automatically registered
+    worker = Worker(
+        client,
+        task_queue="langgraph-agentic-rag",
+        workflows=[AgenticRAGWorkflow],
+    )
+
+    print("Agentic RAG worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/rag/agentic_rag/run_workflow.py b/langgraph_samples/rag/agentic_rag/run_workflow.py
new file mode 100644
index 00000000..01989ed5
--- /dev/null
+++ b/langgraph_samples/rag/agentic_rag/run_workflow.py
@@ -0,0 +1,41 @@
+"""Execute the Agentic RAG workflow.
+
+Usage:
+    # First, in another terminal, start the worker:
+    python -m langgraph_samples.rag.agentic_rag.run_worker
+
+    # Then run this script:
+    python -m langgraph_samples.rag.agentic_rag.run_workflow
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_samples.rag.agentic_rag.workflow import AgenticRAGWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # This query requires retrieval from the knowledge base
+    # The agent will:
+    # 1. Decide to retrieve documents about the topic
+    # 2. Grade the retrieved documents for relevance
+    # 3. Generate an answer using the relevant documents
+    result = await client.execute_workflow(
+        AgenticRAGWorkflow.run,
+        "What is the ReAct pattern and how does it work with Temporal?",
+        id="agentic-rag-workflow",
+        task_queue="langgraph-agentic-rag",
+    )
+
+    # Print only the text response
+    print(result["messages"][-1]["content"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/rag/agentic_rag/workflow.py b/langgraph_samples/rag/agentic_rag/workflow.py
new file mode 100644
index 00000000..4e3c18b8
--- /dev/null
+++ b/langgraph_samples/rag/agentic_rag/workflow.py
@@ -0,0 +1,61 @@
+"""Agentic RAG Workflow.
+
+Temporal workflow that executes the agentic RAG agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class AgenticRAGWorkflow:
+    """Temporal workflow that executes an agentic RAG agent.
+
+    This workflow uses a LangGraph graph that intelligently decides:
+    - Whether to retrieve documents or respond directly
+    - If retrieved documents are relevant (with grading)
+    - Whether to rewrite the query for better retrieval
+
+    Each graph node runs as a Temporal activity:
+    - The "agent" node decides whether to retrieve
+    - The "retrieve" node fetches documents
+    - The "generate" node produces answers
+    - The "rewrite" node reformulates queries
+
+    If any node fails, it is automatically retried. If the worker crashes,
+    execution resumes from the last completed node.
+    """
+
+    @workflow.run
+    async def run(self, query: str) -> dict[str, Any]:
+        """Run the agentic RAG agent to answer a query.
+
+        The agent will:
+        1. Decide if retrieval is needed based on the query
+        2. Retrieve relevant documents if needed
+        3. Grade documents for relevance
+        4. Generate answer or rewrite query as appropriate
+
+        Args:
+            query: The user's question or request.
+
+        Returns:
+            The final state containing the conversation messages and result.
+        """
+        # Get the compiled graph runner by name
+        app = compile("agentic_rag")
+
+        # Execute the agent
+        # The input format is a dict with "messages"
+        result = await app.ainvoke(
+            {"messages": [{"role": "user", "content": query}]}
+        )
+
+        return result
diff --git a/pyproject.toml b/pyproject.toml
index 563ee884..53b54436 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -60,7 +60,10 @@ cloud-export-to-parquet = [
 ]
 langgraph = [
     "langgraph>=0.2.0",
+    "langchain>=0.3.0",
     "langchain-core>=0.1.0",
+    "langchain-openai>=0.2.0",
+    "numpy>=1.26.0,<3",
 ]
 
 [tool.hatch.metadata]
diff --git a/uv.lock b/uv.lock
index 843a1994..527aad2d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2734,8 +2734,11 @@ langchain = [
     { name = "uvicorn", extra = ["standard"] },
 ]
 langgraph = [
+    { name = "langchain" },
     { name = "langchain-core" },
+    { name = "langchain-openai" },
     { name = "langgraph" },
+    { name = "numpy" },
 ]
 nexus = [
     { name = "nexus-rpc" },
@@ -2802,8 +2805,11 @@ langchain = [
     { name = "uvicorn", extras = ["standard"], specifier = ">=0.24.0" },
 ]
 langgraph = [
+    { name = "langchain", specifier = ">=0.3.0" },
     { name = "langchain-core", specifier = ">=0.1.0" },
+    { name = "langchain-openai", specifier = ">=0.2.0" },
     { name = "langgraph", specifier = ">=0.2.0" },
+    { name = "numpy", specifier = ">=1.26.0,<3" },
 ]
 nexus = [{ name = "nexus-rpc", specifier = ">=1.1.0,<2" }]
 open-telemetry = [

From 57110cfd8de7d8ae799748f31aca85000d419c0e Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sun, 28 Dec 2025 11:50:57 -0800
Subject: [PATCH 15/24] LangGraph: Update README with branch installation
 instructions

Update installation instructions to reflect that the LangGraph integration
is currently available as a preview feature in branch repositories:
- SDK: mfateev/sdk-python@langgraph-plugin
- Samples: mfateev/samples-python@langgraph_plugin
---
 langgraph_samples/README.md | 36 +++++++++++++++++++++++++++++++++---
 1 file changed, 33 insertions(+), 3 deletions(-)

diff --git a/langgraph_samples/README.md b/langgraph_samples/README.md
index e7f21695..0a2decd1 100644
--- a/langgraph_samples/README.md
+++ b/langgraph_samples/README.md
@@ -1,8 +1,8 @@
 # Temporal LangGraph Samples
 
-These samples demonstrate the [Temporal LangGraph integration](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/langgraph/README.md) - combining LangGraph's agent framework with Temporal's durable execution.
+These samples demonstrate the Temporal LangGraph integration - combining LangGraph's agent framework with Temporal's durable execution.
 
-See the [module documentation](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/langgraph/README.md) for more information.
+> **Note:** The LangGraph integration is currently available as a preview feature in the `langgraph_plugin` branch of the SDK repository.
 
 ## Overview
 
@@ -15,7 +15,37 @@ This approach ensures that AI agent workflows are durable, observable, and can h
 ## Prerequisites
 
 - Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
-- Required dependencies installed via `uv sync --group langgraph`
+- Python 3.9+
+- [uv](https://docs.astral.sh/uv/) package manager (recommended)
+
+## Installation
+
+Since the LangGraph integration is currently in a branch, you need to install from the branch repositories.
+
+### Running the Samples
+
+1. Clone this samples repository:
+   ```bash
+   git clone -b langgraph_plugin https://github.com/mfateev/samples-python.git
+   cd samples-python
+   ```
+
+2. Install dependencies:
+   ```bash
+   uv sync --group langgraph
+   ```
+
+3. Install the SDK from the `langgraph-plugin` branch:
+   ```bash
+   uv pip install "temporalio @ git+https://github.com/mfateev/sdk-python.git@langgraph-plugin"
+   ```
+
+4. Start a local Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+5. Navigate to a sample directory and follow its README for specific instructions
 
 ## Examples
 

From 0302320d0af61910c7de15bb60d6162fb2358db3 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sun, 28 Dec 2025 12:13:12 -0800
Subject: [PATCH 16/24] LangGraph: Remove unnecessary hasattr check in
 agentic_rag

create_agent always returns a compiled graph, so no need for defensive
compile() call with hasattr check.
---
 langgraph_samples/rag/agentic_rag/graph.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/langgraph_samples/rag/agentic_rag/graph.py b/langgraph_samples/rag/agentic_rag/graph.py
index d032b370..a4e7ebda 100644
--- a/langgraph_samples/rag/agentic_rag/graph.py
+++ b/langgraph_samples/rag/agentic_rag/graph.py
@@ -164,8 +164,7 @@ def build_agentic_rag_graph() -> Any:
     # Build retrieval agent using create_agent
     # Note: create_agent returns a compiled graph. The Temporal plugin automatically
     # detects this subgraph and runs its inner nodes (model, tools) as separate activities.
-    retrieval_graph: Any = create_agent(model, tools)
-    retrieve_agent = retrieval_graph.compile() if hasattr(retrieval_graph, 'compile') else retrieval_graph
+    retrieve_agent: Any = create_agent(model, tools)
 
     # Grade documents for relevance (as a node/activity, not conditional edge)
     def grade_documents(state: AgentState) -> dict[str, Any]:

From e82d0a50a159efcd27a0b051754e091acd53f7f1 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Sun, 28 Dec 2025 21:55:45 -0800
Subject: [PATCH 17/24] LangGraph samples: Use create_agent instead of
 deprecated create_react_agent

- Update imports from langgraph.prebuilt to langchain.agents
- Change prompt parameter to system_prompt
- Add multi_agent/supervisor sample
- Document the correct API in CLAUDE.md
---
 CLAUDE.md                                     |  17 ++
 langgraph_samples/basic/react_agent/graph.py  |   1 -
 langgraph_samples/multi_agent/README.md       |  34 +++
 langgraph_samples/multi_agent/__init__.py     |   1 +
 .../multi_agent/supervisor/README.md          | 115 ++++++++++
 .../multi_agent/supervisor/__init__.py        |   1 +
 .../multi_agent/supervisor/graph.py           | 211 ++++++++++++++++++
 .../multi_agent/supervisor/run_worker.py      |  53 +++++
 .../multi_agent/supervisor/run_workflow.py    |  68 ++++++
 .../multi_agent/supervisor/workflow.py        |  57 +++++
 10 files changed, 557 insertions(+), 1 deletion(-)
 create mode 100644 langgraph_samples/multi_agent/README.md
 create mode 100644 langgraph_samples/multi_agent/__init__.py
 create mode 100644 langgraph_samples/multi_agent/supervisor/README.md
 create mode 100644 langgraph_samples/multi_agent/supervisor/__init__.py
 create mode 100644 langgraph_samples/multi_agent/supervisor/graph.py
 create mode 100644 langgraph_samples/multi_agent/supervisor/run_worker.py
 create mode 100644 langgraph_samples/multi_agent/supervisor/run_workflow.py
 create mode 100644 langgraph_samples/multi_agent/supervisor/workflow.py

diff --git a/CLAUDE.md b/CLAUDE.md
index 76a799e1..7d376139 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -14,3 +14,20 @@ client = await Client.connect(**config)
 ```
 
 This pattern allows configuration via environment variables while providing sensible defaults.
+
+## LangGraph Guidelines
+
+### Agent Creation
+
+- **DO NOT** use `create_react_agent` from `langgraph.prebuilt` - it is deprecated
+- **USE** `create_agent` from `langchain.agents` instead
+
+```python
+# Wrong (deprecated)
+from langgraph.prebuilt import create_react_agent
+agent = create_react_agent(model=model, tools=[...], prompt="...")
+
+# Correct
+from langchain.agents import create_agent
+agent = create_agent(model=model, tools=[...], system_prompt="...")
+```
diff --git a/langgraph_samples/basic/react_agent/graph.py b/langgraph_samples/basic/react_agent/graph.py
index d17a12ec..213aadb8 100644
--- a/langgraph_samples/basic/react_agent/graph.py
+++ b/langgraph_samples/basic/react_agent/graph.py
@@ -49,6 +49,5 @@ def build_react_agent() -> Any:
     tools = [get_weather, calculate]
 
     # Create the agent using LangChain's create_agent
-    # (Note: langgraph.prebuilt.create_react_agent is deprecated)
     # The Temporal integration will run each node as an activity
     return create_agent(model, tools)
diff --git a/langgraph_samples/multi_agent/README.md b/langgraph_samples/multi_agent/README.md
new file mode 100644
index 00000000..ad33db08
--- /dev/null
+++ b/langgraph_samples/multi_agent/README.md
@@ -0,0 +1,34 @@
+# Multi-Agent Systems
+
+Samples demonstrating multi-agent orchestration patterns with LangGraph and Temporal.
+
+## Samples
+
+### [Supervisor](./supervisor/)
+
+A supervisor agent that coordinates specialized worker agents (researcher, writer, analyst). The supervisor routes tasks to appropriate agents and aggregates results.
+
+**Demonstrates:**
+- Multi-agent orchestration patterns
+- Supervisor decision-making and task routing
+- Agent specialization with different tools
+- Durable execution across agent handoffs
+
+## Multi-Agent Patterns
+
+LangGraph supports several multi-agent patterns:
+
+1. **Supervisor** (this sample): A central coordinator routes tasks to specialists
+2. **Swarm**: Agents hand off to each other dynamically based on the conversation
+3. **Hierarchical**: Team supervisors under a top-level coordinator
+4. **Collaborative**: Agents work together on shared state
+
+## Temporal Benefits for Multi-Agent Systems
+
+Running multi-agent systems with Temporal provides:
+
+- **Durability**: Agent interactions are checkpointed, surviving failures
+- **Retries**: Failed agent calls are automatically retried
+- **Visibility**: Track complex agent interactions through Temporal's UI
+- **Long-running support**: Multi-agent workflows can run for extended periods
+- **Scalability**: Distribute agent execution across workers
diff --git a/langgraph_samples/multi_agent/__init__.py b/langgraph_samples/multi_agent/__init__.py
new file mode 100644
index 00000000..e3d841b5
--- /dev/null
+++ b/langgraph_samples/multi_agent/__init__.py
@@ -0,0 +1 @@
+# Multi-Agent samples for LangGraph + Temporal integration
diff --git a/langgraph_samples/multi_agent/supervisor/README.md b/langgraph_samples/multi_agent/supervisor/README.md
new file mode 100644
index 00000000..a1f4ba2e
--- /dev/null
+++ b/langgraph_samples/multi_agent/supervisor/README.md
@@ -0,0 +1,115 @@
+# Supervisor Multi-Agent System
+
+A supervisor agent that coordinates specialized worker agents (researcher, writer, analyst) using LangGraph with Temporal for durable execution.
+
+## What This Sample Demonstrates
+
+- **Multi-agent orchestration**: A supervisor coordinates multiple specialized agents
+- **Task routing**: The supervisor intelligently routes tasks to the appropriate agent
+- **Agent specialization**: Each agent has specific tools and expertise
+- **Durable multi-agent execution**: All agent interactions run as Temporal activities
+- **Crash recovery**: If any agent fails or the worker crashes, execution resumes seamlessly
+
+## How It Works
+
+### Architecture
+
+```
+                    ┌─────────────┐
+                    │  Supervisor │
+                    │ (Coordinator)│
+                    └──────┬──────┘
+                           │
+           ┌───────────────┼───────────────┐
+           │               │               │
+           ▼               ▼               ▼
+    ┌────────────┐  ┌────────────┐  ┌────────────┐
+    │ Researcher │  │   Writer   │  │  Analyst   │
+    │            │  │            │  │            │
+    │ web_search │  │write_content│ │ calculate  │
+    │            │  │ summarize  │  │analyze_data│
+    └────────────┘  └────────────┘  └────────────┘
+```
+
+### Agents
+
+1. **Supervisor**: Central coordinator that analyzes requests, decides which agent(s) to use, routes tasks, and synthesizes final responses
+
+2. **Researcher**: Specialized in information gathering with web search capabilities. Use for research tasks, fact-finding, and current events
+
+3. **Writer**: Specialized in content creation and summarization. Use for writing articles, reports, summaries, or any content generation
+
+4. **Analyst**: Specialized in calculations and data analysis. Use for math problems, data interpretation, or analytical tasks
+
+### Flow
+
+1. User sends a request to the supervisor
+2. Supervisor analyzes the request and determines which agent(s) are needed
+3. Supervisor routes the task to the appropriate agent
+4. Agent completes its work and returns results to supervisor
+5. Supervisor may route to additional agents or synthesize final response
+6. Final response is returned to the user
+
+### Temporal Integration
+
+With Temporal, the multi-agent system gains:
+- **Durability**: Each agent's execution is checkpointed
+- **Retries**: Failed agent calls are automatically retried
+- **Recovery**: If the worker crashes, execution resumes from the last completed agent interaction
+- **Visibility**: Track agent interactions through Temporal's UI
+
+## Prerequisites
+
+- Temporal server running locally (`temporal server start-dev`)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+- Install dependencies: `uv sync --group langgraph`
+
+## Running the Example
+
+First, start the worker:
+```bash
+uv run langgraph_samples/multi_agent/supervisor/run_worker.py
+```
+
+Then, in a separate terminal, run the workflow:
+```bash
+uv run langgraph_samples/multi_agent/supervisor/run_workflow.py
+```
+
+## Expected Output
+
+The supervisor will coordinate the agents to:
+1. Use the researcher to find AI trends information
+2. Use the analyst to evaluate which trends are most impactful
+3. Use the writer to create an executive summary
+
+You'll see the final synthesized response incorporating work from multiple agents.
+
+## Example Queries
+
+The default query demonstrates multi-agent collaboration:
+```
+Research the latest AI trends in 2024, analyze which trends are most
+impactful for enterprise software development, and write a brief
+executive summary (2-3 paragraphs).
+```
+
+You can modify `run_workflow.py` to test different scenarios:
+
+- **Research only**: "What are the key features of Temporal workflows?"
+- **Analysis only**: "Calculate the compound interest on $10,000 at 5% for 3 years"
+- **Writing only**: "Write a brief introduction to LangGraph multi-agent systems"
+- **Multi-agent**: "Research LangGraph patterns, analyze their trade-offs, and summarize recommendations"
+
+## Key Files
+
+- `graph.py`: Defines the supervisor and specialized agents using `langgraph-supervisor`
+- `workflow.py`: Temporal workflow that executes the multi-agent system
+- `run_worker.py`: Starts the Temporal worker with the LangGraph plugin
+- `run_workflow.py`: Executes the workflow with a sample request
+
+## References
+
+- [LangGraph Supervisor Library](https://github.com/langchain-ai/langgraph-supervisor-py)
+- [Multi-Agent Systems in LangGraph](https://langchain-ai.github.io/langgraph/concepts/multi_agent/)
+- [Temporal LangGraph Integration](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/langgraph/README.md)
diff --git a/langgraph_samples/multi_agent/supervisor/__init__.py b/langgraph_samples/multi_agent/supervisor/__init__.py
new file mode 100644
index 00000000..85a56917
--- /dev/null
+++ b/langgraph_samples/multi_agent/supervisor/__init__.py
@@ -0,0 +1 @@
+# Supervisor multi-agent sample
diff --git a/langgraph_samples/multi_agent/supervisor/graph.py b/langgraph_samples/multi_agent/supervisor/graph.py
new file mode 100644
index 00000000..e4425463
--- /dev/null
+++ b/langgraph_samples/multi_agent/supervisor/graph.py
@@ -0,0 +1,211 @@
+"""Supervisor Multi-Agent Graph Definition.
+
+This module implements a supervisor agent that coordinates specialized worker agents.
+The supervisor routes tasks to appropriate agents (researcher, writer, analyst) and
+aggregates their results.
+
+Architecture:
+- Supervisor: Central coordinator that decides which agent should handle each task
+- Researcher: Agent with web search capabilities for gathering information
+- Writer: Agent specialized in content creation and summarization
+- Analyst: Agent for data analysis and mathematical operations
+
+The Temporal LangGraph plugin runs each agent's nodes as separate activities,
+providing durability and automatic retries for the entire multi-agent system.
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from typing import Any
+
+from langchain_openai import ChatOpenAI
+from langchain.agents import create_agent
+from langgraph_supervisor import create_supervisor
+
+
+# --- Tools for specialized agents ---
+
+
+def web_search(query: str) -> str:
+    """Search the web for information.
+
+    This is a mock implementation. In production, you would integrate
+    with a real search API like Tavily, SerpAPI, or similar.
+    """
+    # Mock search results for demonstration
+    mock_results = {
+        "ai trends 2024": (
+            "Key AI trends in 2024:\n"
+            "1. Multimodal AI models combining text, image, and video\n"
+            "2. AI agents and autonomous systems\n"
+            "3. Small language models for edge deployment\n"
+            "4. AI in scientific discovery\n"
+            "5. Responsible AI and governance frameworks"
+        ),
+        "temporal workflow": (
+            "Temporal is a durable execution platform:\n"
+            "- Workflows survive failures and can run for years\n"
+            "- Activities are retried automatically on failure\n"
+            "- Built-in support for signals, queries, and timers\n"
+            "- Used by companies like Netflix, Snap, and Stripe"
+        ),
+        "langgraph multi-agent": (
+            "LangGraph multi-agent patterns:\n"
+            "- Supervisor: Central coordinator routes tasks to specialists\n"
+            "- Swarm: Agents hand off to each other dynamically\n"
+            "- Hierarchical: Team supervisors under a top-level coordinator\n"
+            "- Collaborative: Agents work together on shared state"
+        ),
+    }
+
+    # Try to match query to mock results
+    query_lower = query.lower()
+    for key, result in mock_results.items():
+        if any(word in query_lower for word in key.split()):
+            return result
+
+    return f"Search results for '{query}': No specific results found. Please try a different query."
+
+
+def write_content(topic: str, style: str = "informative") -> str:
+    """Write content on a given topic.
+
+    This is a mock implementation. The actual content generation
+    would be handled by the LLM in the agent's reasoning.
+    """
+    return f"[Content draft on '{topic}' in {style} style - LLM will generate actual content]"
+
+
+def summarize(text: str, max_length: int = 100) -> str:
+    """Summarize the given text.
+
+    This is a mock implementation. The actual summarization
+    would be handled by the LLM in the agent's reasoning.
+    """
+    return f"[Summary of {len(text)} characters, max {max_length} words - LLM will generate]"
+
+
+def calculate(expression: str) -> str:
+    """Evaluate a mathematical expression safely.
+
+    Supports basic arithmetic operations.
+    """
+    try:
+        # Only allow safe mathematical operations
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Invalid characters in expression. Only numbers and +-*/.() are allowed."
+
+        result = eval(expression)  # noqa: S307
+        return str(result)
+    except Exception as e:
+        return f"Error evaluating '{expression}': {e}"
+
+
+def analyze_data(data: str, analysis_type: str = "summary") -> str:
+    """Analyze data and provide insights.
+
+    Args:
+        data: The data to analyze (numbers, text, etc.)
+        analysis_type: Type of analysis (summary, trend, comparison)
+    """
+    return f"[{analysis_type.title()} analysis of data: '{data[:50]}...' - LLM will provide detailed analysis]"
+
+
+# --- Build the multi-agent system ---
+
+
+def build_supervisor_graph() -> Any:
+    """Build a supervisor multi-agent system with durable execution.
+
+    Creates a supervisor that coordinates three specialized agents:
+    1. Researcher: Gathers information via web search
+    2. Writer: Creates and summarizes content
+    3. Analyst: Performs calculations and data analysis
+
+    The supervisor decides which agent(s) to use based on the task,
+    routes work appropriately, and synthesizes final results.
+
+    With Temporal integration:
+    - Each agent's execution runs as Temporal activities
+    - Agent failures are automatically retried
+    - Progress is checkpointed between agent handoffs
+    - The entire multi-agent workflow survives worker crashes
+
+    Returns:
+        A compiled LangGraph supervisor that can be executed with ainvoke().
+    """
+    # Create the model
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Create specialized agents
+
+    # Researcher agent - gathers information
+    researcher: Any = create_agent(
+        model=model,
+        tools=[web_search],
+        name="researcher",
+        system_prompt=(
+            "You are a research specialist with web search capabilities. "
+            "Your job is to find accurate, relevant information on any topic. "
+            "Always cite what you find and be thorough in your research. "
+            "If you can't find specific information, say so clearly."
+        ),
+    )
+
+    # Writer agent - creates content
+    writer: Any = create_agent(
+        model=model,
+        tools=[write_content, summarize],
+        name="writer",
+        system_prompt=(
+            "You are a skilled writer and content creator. "
+            "You excel at writing clear, engaging content and summarizing complex topics. "
+            "Adapt your writing style to the requested format (blog, report, summary, etc.). "
+            "Always structure your content logically with clear sections."
+        ),
+    )
+
+    # Analyst agent - data analysis and calculations
+    analyst: Any = create_agent(
+        model=model,
+        tools=[calculate, analyze_data],
+        name="analyst",
+        system_prompt=(
+            "You are a data analyst expert in mathematical calculations and data interpretation. "
+            "Use the calculate tool for any mathematical operations. "
+            "Provide clear explanations of your analysis and methodology. "
+            "Always double-check your calculations."
+        ),
+    )
+
+    # Create the supervisor to coordinate the agents
+    workflow = create_supervisor(
+        agents=[researcher, writer, analyst],
+        model=model,
+        prompt=(
+            "You are a team supervisor managing three specialized agents:\n"
+            "- researcher: Has web search capabilities. Use for gathering information, "
+            "facts, current events, or research tasks.\n"
+            "- writer: Specializes in content creation and summarization. Use for "
+            "writing articles, reports, summaries, or any content generation.\n"
+            "- analyst: Expert in calculations and data analysis. Use for math problems, "
+            "data interpretation, or analytical tasks.\n\n"
+            "Your job is to:\n"
+            "1. Understand the user's request\n"
+            "2. Break it down into subtasks if needed\n"
+            "3. Assign tasks to the appropriate agent(s)\n"
+            "4. Synthesize the final response from agent outputs\n\n"
+            "Assign work to one agent at a time. Do not do the work yourself - "
+            "always delegate to the appropriate specialist."
+        ),
+        # Include full conversation history for context
+        output_mode="full_history",
+    )
+
+    return workflow.compile()
diff --git a/langgraph_samples/multi_agent/supervisor/run_worker.py b/langgraph_samples/multi_agent/supervisor/run_worker.py
new file mode 100644
index 00000000..87269905
--- /dev/null
+++ b/langgraph_samples/multi_agent/supervisor/run_worker.py
@@ -0,0 +1,53 @@
+"""Worker for the Supervisor Multi-Agent sample.
+
+Starts a Temporal worker that can execute SupervisorWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_samples.multi_agent.supervisor.graph import build_supervisor_graph
+from langgraph_samples.multi_agent.supervisor.workflow import SupervisorWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the supervisor graph registered
+    plugin = LangGraphPlugin(
+        graphs={"supervisor": build_supervisor_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    # Note: Activities (LLM calls, tool executions, agent handoffs) are automatically registered
+    worker = Worker(
+        client,
+        task_queue="langgraph-supervisor",
+        workflows=[SupervisorWorkflow],
+    )
+
+    print("Supervisor Multi-Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    print()
+    print("This worker handles:")
+    print("  - Supervisor: Coordinates and routes tasks")
+    print("  - Researcher: Web search and information gathering")
+    print("  - Writer: Content creation and summarization")
+    print("  - Analyst: Calculations and data analysis")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/multi_agent/supervisor/run_workflow.py b/langgraph_samples/multi_agent/supervisor/run_workflow.py
new file mode 100644
index 00000000..42a936c3
--- /dev/null
+++ b/langgraph_samples/multi_agent/supervisor/run_workflow.py
@@ -0,0 +1,68 @@
+"""Execute the Supervisor Multi-Agent workflow.
+
+Usage:
+    # First, in another terminal, start the worker:
+    python -m langgraph_samples.multi_agent.supervisor.run_worker
+
+    # Then run this script:
+    python -m langgraph_samples.multi_agent.supervisor.run_workflow
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_samples.multi_agent.supervisor.workflow import SupervisorWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # This request requires multiple agents:
+    # 1. Researcher to find information about AI trends
+    # 2. Analyst to process/analyze the data
+    # 3. Writer to create a summary
+    request = (
+        "Research the latest AI trends in 2024, analyze which trends are most "
+        "impactful for enterprise software development, and write a brief "
+        "executive summary (2-3 paragraphs)."
+    )
+
+    print("Executing supervisor multi-agent workflow...")
+    print(f"Request: {request}")
+    print()
+
+    result = await client.execute_workflow(
+        SupervisorWorkflow.run,
+        request,
+        id="supervisor-workflow",
+        task_queue="langgraph-supervisor",
+    )
+
+    # Print the final response
+    print("=" * 60)
+    print("FINAL RESPONSE:")
+    print("=" * 60)
+
+    # Get the last assistant message
+    messages = result.get("messages", [])
+    for msg in reversed(messages):
+        # Handle both dict and object formats
+        if isinstance(msg, dict):
+            if msg.get("role") == "assistant" or msg.get("type") == "ai":
+                content = msg.get("content", "")
+                if content and not content.startswith("["):  # Skip tool outputs
+                    print(content)
+                    break
+        elif hasattr(msg, "content"):
+            msg_type = getattr(msg, "type", "")
+            if msg_type == "ai" and msg.content:
+                print(msg.content)
+                break
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/multi_agent/supervisor/workflow.py b/langgraph_samples/multi_agent/supervisor/workflow.py
new file mode 100644
index 00000000..00b876af
--- /dev/null
+++ b/langgraph_samples/multi_agent/supervisor/workflow.py
@@ -0,0 +1,57 @@
+"""Supervisor Multi-Agent Workflow.
+
+Temporal workflow that executes the supervisor multi-agent system with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class SupervisorWorkflow:
+    """Temporal workflow that executes a supervisor multi-agent system.
+
+    This workflow uses a supervisor pattern where a central coordinator
+    routes tasks to specialized agents (researcher, writer, analyst).
+    Each agent's execution runs as Temporal activities:
+    - The supervisor's routing decisions run as activities
+    - Each specialized agent's work runs as activities
+    - Agent handoffs are durably recorded
+
+    If any agent fails, it is automatically retried. If the worker crashes,
+    execution resumes from the last completed agent interaction.
+    """
+
+    @workflow.run
+    async def run(self, request: str) -> dict[str, Any]:
+        """Run the supervisor multi-agent system to handle a request.
+
+        The supervisor will:
+        1. Analyze the request to determine which agent(s) are needed
+        2. Route tasks to appropriate specialized agents
+        3. Coordinate agent interactions and handoffs
+        4. Synthesize a final response from agent outputs
+
+        Args:
+            request: The user's request or task description.
+
+        Returns:
+            The final state containing the conversation messages and result.
+        """
+        # Get the compiled graph runner by name
+        app = compile("supervisor")
+
+        # Execute the multi-agent system
+        # The supervisor will coordinate the specialized agents
+        result = await app.ainvoke(
+            {"messages": [{"role": "user", "content": request}]}
+        )
+
+        return result

From 3475e6f96eea532ed4936777374546acdf9ce7f5 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Mon, 29 Dec 2025 04:54:26 -0800
Subject: [PATCH 18/24] langgraph-supervisor dependency added

---
 pyproject.toml |  1 +
 uv.lock        | 15 +++++++++++++++
 2 files changed, 16 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 53b54436..f08b3325 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -60,6 +60,7 @@ cloud-export-to-parquet = [
 ]
 langgraph = [
     "langgraph>=0.2.0",
+    "langgraph-supervisor>=0.0.1",
     "langchain>=0.3.0",
     "langchain-core>=0.1.0",
     "langchain-openai>=0.2.0",
diff --git a/uv.lock b/uv.lock
index 527aad2d..fc0774d5 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1091,6 +1091,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/ab/fe/0c1c9c01a154eba62b20b02fabe811fd94a2b810061ae9e4d8462b8cf85a/langgraph_sdk-0.3.1-py3-none-any.whl", hash = "sha256:0b856923bfd20bf3441ce9d03bef488aa333fb610e972618799a9d584436acad", size = 66517 },
 ]
 
+[[package]]
+name = "langgraph-supervisor"
+version = "0.0.31"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "langchain-core" },
+    { name = "langgraph" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/15/a0/6e9286a2e4a9ed8c9d299d0131b323cae376316a63286d2880031bb01082/langgraph_supervisor-0.0.31.tar.gz", hash = "sha256:0f71d5098fa1ac274423eff08750345450045c1e0dd2480ab7f3e5c1848f6016", size = 21910 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/1d/7f/bb73da8db36c96f32cdd0292f2cd75e2d671150b0d177575da560ad994a1/langgraph_supervisor-0.0.31-py3-none-any.whl", hash = "sha256:6bbe5311f536d38779766cc6f074005e3e892d37edbfd875731923221daa1889", size = 16505 },
+]
+
 [[package]]
 name = "langsmith"
 version = "0.5.1"
@@ -2738,6 +2751,7 @@ langgraph = [
     { name = "langchain-core" },
     { name = "langchain-openai" },
     { name = "langgraph" },
+    { name = "langgraph-supervisor" },
     { name = "numpy" },
 ]
 nexus = [
@@ -2809,6 +2823,7 @@ langgraph = [
     { name = "langchain-core", specifier = ">=0.1.0" },
     { name = "langchain-openai", specifier = ">=0.2.0" },
     { name = "langgraph", specifier = ">=0.2.0" },
+    { name = "langgraph-supervisor", specifier = ">=0.0.1" },
     { name = "numpy", specifier = ">=1.26.0,<3" },
 ]
 nexus = [{ name = "nexus-rpc", specifier = ">=1.1.0,<2" }]

From 7db14fedeb72875c55c23dd89330f8843752346c Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Mon, 29 Dec 2025 10:49:28 -0800
Subject: [PATCH 19/24] LangGraph: Add deep research, plan-and-execute, and
 reflection samples

- Deep Research Agent: Multi-step research with parallel search execution
  using Send API, result evaluation, and report synthesis
- Plan-and-Execute Agent: Structured planning with sequential step
  execution, dynamic replanning, and tool integration
- Reflection Agent: Generate-critique-revise loop with quality scoring
  and iterative improvement until criteria met
---
 langgraph_samples/README.md                   |   5 +-
 langgraph_samples/advanced/README.md          |  29 ++
 langgraph_samples/advanced/__init__.py        |   1 +
 .../advanced/reflection/README.md             | 193 ++++++++
 .../advanced/reflection/__init__.py           |   1 +
 .../advanced/reflection/graph.py              | 307 +++++++++++++
 .../advanced/reflection/run_worker.py         |  46 ++
 .../advanced/reflection/run_workflow.py       |  64 +++
 .../advanced/reflection/workflow.py           |  58 +++
 langgraph_samples/planning/README.md          |  30 ++
 langgraph_samples/planning/__init__.py        |   1 +
 .../planning/plan_and_execute/README.md       | 175 +++++++
 .../planning/plan_and_execute/__init__.py     |   1 +
 .../planning/plan_and_execute/graph.py        | 426 ++++++++++++++++++
 .../planning/plan_and_execute/run_worker.py   |  50 ++
 .../planning/plan_and_execute/run_workflow.py |  58 +++
 .../planning/plan_and_execute/workflow.py     |  55 +++
 langgraph_samples/rag/README.md               |  11 +
 langgraph_samples/rag/deep_research/README.md | 145 ++++++
 .../rag/deep_research/__init__.py             |   1 +
 langgraph_samples/rag/deep_research/graph.py  | 339 ++++++++++++++
 .../rag/deep_research/run_worker.py           |  46 ++
 .../rag/deep_research/run_workflow.py         |  45 ++
 .../rag/deep_research/workflow.py             |  58 +++
 24 files changed, 2144 insertions(+), 1 deletion(-)
 create mode 100644 langgraph_samples/advanced/README.md
 create mode 100644 langgraph_samples/advanced/__init__.py
 create mode 100644 langgraph_samples/advanced/reflection/README.md
 create mode 100644 langgraph_samples/advanced/reflection/__init__.py
 create mode 100644 langgraph_samples/advanced/reflection/graph.py
 create mode 100644 langgraph_samples/advanced/reflection/run_worker.py
 create mode 100644 langgraph_samples/advanced/reflection/run_workflow.py
 create mode 100644 langgraph_samples/advanced/reflection/workflow.py
 create mode 100644 langgraph_samples/planning/README.md
 create mode 100644 langgraph_samples/planning/__init__.py
 create mode 100644 langgraph_samples/planning/plan_and_execute/README.md
 create mode 100644 langgraph_samples/planning/plan_and_execute/__init__.py
 create mode 100644 langgraph_samples/planning/plan_and_execute/graph.py
 create mode 100644 langgraph_samples/planning/plan_and_execute/run_worker.py
 create mode 100644 langgraph_samples/planning/plan_and_execute/run_workflow.py
 create mode 100644 langgraph_samples/planning/plan_and_execute/workflow.py
 create mode 100644 langgraph_samples/rag/deep_research/README.md
 create mode 100644 langgraph_samples/rag/deep_research/__init__.py
 create mode 100644 langgraph_samples/rag/deep_research/graph.py
 create mode 100644 langgraph_samples/rag/deep_research/run_worker.py
 create mode 100644 langgraph_samples/rag/deep_research/run_workflow.py
 create mode 100644 langgraph_samples/rag/deep_research/workflow.py

diff --git a/langgraph_samples/README.md b/langgraph_samples/README.md
index 0a2decd1..e2309293 100644
--- a/langgraph_samples/README.md
+++ b/langgraph_samples/README.md
@@ -53,4 +53,7 @@ Each directory contains complete examples with their own README for detailed ins
 
 - **[Basic Examples](./basic/README.md)** - Simple examples including a hello world agent demonstrating basic plugin setup and graph registration.
 - **[Human-in-the-Loop](./human_in_loop/README.md)** - Examples demonstrating interrupt/resume workflows with human approval.
-- **[RAG (Retrieval Augmented Generation)](./rag/README.md)** - Intelligent retrieval with document grading and query rewriting.
+- **[Multi-Agent](./multi_agent/README.md)** - Multi-agent patterns including supervisor coordination.
+- **[RAG (Retrieval Augmented Generation)](./rag/README.md)** - Intelligent retrieval with document grading, query rewriting, and deep research.
+- **[Planning](./planning/README.md)** - Plan-and-execute patterns with structured step execution.
+- **[Advanced Patterns](./advanced/README.md)** - Advanced techniques including reflection and self-improvement.
diff --git a/langgraph_samples/advanced/README.md b/langgraph_samples/advanced/README.md
new file mode 100644
index 00000000..dc27ce2c
--- /dev/null
+++ b/langgraph_samples/advanced/README.md
@@ -0,0 +1,29 @@
+# Advanced Pattern Samples
+
+This directory contains samples demonstrating advanced LangGraph patterns with Temporal integration.
+
+## Available Samples
+
+### [Reflection Agent](./reflection/README.md)
+
+An agent that generates content, critiques it, and iteratively improves until quality criteria are met.
+
+**Key Features:**
+- Generate-critique-revise loop
+- Structured feedback with quality scoring
+- Quality-based termination (score >= 7)
+- Maximum iteration limits
+- Full critique history visibility
+
+## Why Advanced Patterns?
+
+These patterns demonstrate sophisticated agent behaviors:
+- **Self-improvement**: Agents that refine their own outputs
+- **Quality assurance**: Automatic quality checking and iteration
+- **Transparency**: Full visibility into the improvement process
+
+## Prerequisites
+
+- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+- Dependencies installed via `uv sync --group langgraph`
diff --git a/langgraph_samples/advanced/__init__.py b/langgraph_samples/advanced/__init__.py
new file mode 100644
index 00000000..028667c7
--- /dev/null
+++ b/langgraph_samples/advanced/__init__.py
@@ -0,0 +1 @@
+"""Advanced pattern samples for Temporal LangGraph integration."""
diff --git a/langgraph_samples/advanced/reflection/README.md b/langgraph_samples/advanced/reflection/README.md
new file mode 100644
index 00000000..747fb7de
--- /dev/null
+++ b/langgraph_samples/advanced/reflection/README.md
@@ -0,0 +1,193 @@
+# Reflection Agent
+
+An agent that generates content, critiques it, and iteratively improves until quality criteria are met. This demonstrates the self-reflection pattern for producing higher-quality outputs.
+
+## Overview
+
+The Reflection Agent implements a generate-critique-revise loop:
+1. **Generate** - Create initial content based on the task
+2. **Reflect** - Critique the content with structured feedback
+3. **Revise** - Improve content based on the critique
+4. **Evaluate** - Check if quality criteria are met
+5. **Loop** - Repeat until satisfied or max iterations reached
+
+## Why Reflection?
+
+The reflection pattern improves output quality by:
+- **Self-correction**: Catching and fixing issues automatically
+- **Structured feedback**: Using specific criteria for evaluation
+- **Iterative refinement**: Multiple passes produce better results
+- **Visibility**: Each iteration's critique is recorded
+
+## Why Temporal?
+
+Temporal enhances reflection workflows with:
+- **Durability**: Long refinement sessions complete reliably
+- **Visibility**: See each iteration in the workflow history
+- **Checkpointing**: Resume from any iteration if interrupted
+- **Cost tracking**: Monitor LLM calls across iterations
+
+## Architecture
+
+```
+[Generate] --> [Reflect] --> [Score >= 7?] --> YES --> [Finalize] --> END
+                   ^              |
+                   |              NO
+                   |              v
+                   +-------- [Revise]
+```
+
+Each node runs as a Temporal activity with automatic retry.
+
+## Running the Sample
+
+### Prerequisites
+
+- Temporal server running locally
+- OpenAI API key
+
+### Steps
+
+1. Start the Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. In one terminal, start the worker:
+   ```bash
+   export OPENAI_API_KEY=your-key-here
+   python -m langgraph_samples.advanced.reflection.run_worker
+   ```
+
+3. In another terminal, run the workflow:
+   ```bash
+   python -m langgraph_samples.advanced.reflection.run_workflow
+   ```
+
+## Sample Output
+
+```
+============================================================
+REFLECTION JOURNEY
+============================================================
+
+Iteration 1 Critique:
+  Score: 5/10
+  Strengths: Clear structure, Good opening hook
+  Weaknesses: Too generic, Lacks specific examples
+
+Iteration 2 Critique:
+  Score: 7/10
+  Strengths: Specific examples added, Better flow
+  Weaknesses: Could be more concise
+
+============================================================
+FINAL CONTENT
+============================================================
+
+In the world of AI agents, failure is not just possible—it's inevitable.
+Network requests timeout, APIs rate-limit, and worker processes crash.
+Without durable execution, these failures mean lost progress, corrupted
+state, and frustrated users...
+
+[Rest of the refined blog post]
+```
+
+## Key Features Demonstrated
+
+### 1. Structured Critique
+```python
+class Critique(BaseModel):
+    strengths: list[str]
+    weaknesses: list[str]
+    suggestions: list[str]
+    quality_score: int  # 1-10
+    is_satisfactory: bool  # True if score >= 7
+```
+
+### 2. Quality-Based Routing
+```python
+def should_revise(state) -> Literal["revise", "finalize"]:
+    if critique.is_satisfactory or iteration >= max_iterations:
+        return "finalize"
+    return "revise"
+```
+
+### 3. Feedback-Driven Revision
+```python
+def revise(state: ReflectionState) -> dict[str, Any]:
+    feedback = format_critique(latest_critique)
+    revised = revision_chain.invoke({
+        "task": task,
+        "draft": draft,
+        "feedback": feedback
+    })
+    return {"current_draft": revised, "iteration": iteration + 1}
+```
+
+## Customization
+
+### Adjusting Quality Threshold
+
+Change when content is considered "good enough":
+
+```python
+class Critique(BaseModel):
+    is_satisfactory: bool = Field(
+        description="Whether content meets standards (score >= 8)"  # Stricter
+    )
+```
+
+### Adding Domain-Specific Criteria
+
+Customize the critique for specific content types:
+
+```python
+reflect_prompt = ChatPromptTemplate.from_messages([
+    ("system", """You are a technical writing editor. Evaluate:
+    - Technical accuracy
+    - Code example correctness
+    - Appropriate complexity level
+    - SEO optimization
+    ..."""),
+    ...
+])
+```
+
+### Different Models for Generation vs Critique
+
+Use different models for each role:
+
+```python
+# Creative model for generation
+generator = ChatOpenAI(model="gpt-4", temperature=0.8)
+
+# Precise model for critique
+critic = ChatOpenAI(model="gpt-4", temperature=0)
+```
+
+## Use Cases
+
+The reflection pattern works well for:
+- **Writing tasks**: Blog posts, documentation, emails
+- **Code generation**: Self-reviewing generated code
+- **Problem-solving**: Iteratively refining solutions
+- **Summarization**: Improving summary quality
+- **Translation**: Self-correcting translations
+
+## Comparison with Single-Pass Generation
+
+| Aspect | Single-Pass | Reflection |
+|--------|-------------|------------|
+| Quality | Variable | Consistently higher |
+| Cost | 1 LLM call | 3-6+ LLM calls |
+| Latency | Fast | Slower |
+| Transparency | Low | High (visible iterations) |
+| Best for | Quick drafts | Final outputs |
+
+## Next Steps
+
+- Add human-in-the-loop for critique approval
+- Implement specialized critics (grammar, style, accuracy)
+- Add A/B testing of different revision strategies
+- Integrate with external quality metrics
diff --git a/langgraph_samples/advanced/reflection/__init__.py b/langgraph_samples/advanced/reflection/__init__.py
new file mode 100644
index 00000000..1baeab1d
--- /dev/null
+++ b/langgraph_samples/advanced/reflection/__init__.py
@@ -0,0 +1 @@
+"""Reflection Agent sample for Temporal LangGraph integration."""
diff --git a/langgraph_samples/advanced/reflection/graph.py b/langgraph_samples/advanced/reflection/graph.py
new file mode 100644
index 00000000..a745cb04
--- /dev/null
+++ b/langgraph_samples/advanced/reflection/graph.py
@@ -0,0 +1,307 @@
+"""Reflection Agent Graph Definition.
+
+This module implements a reflection agent that generates content, critiques it,
+and iteratively improves until quality criteria are met.
+
+The reflection pattern:
+1. generate - Create initial content based on the prompt
+2. reflect - Critique the content and identify improvements
+3. revise - Incorporate feedback to improve the content
+4. evaluate - Check if quality criteria are met
+5. Loop until satisfied or max iterations reached
+
+This pattern is useful for:
+- Writing tasks (essays, code, documentation)
+- Problem-solving (iterative refinement)
+- Quality assurance (self-checking outputs)
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from typing import Annotated, Any, Literal
+
+from langchain_core.messages import BaseMessage, HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from typing_extensions import TypedDict
+
+
+class Critique(BaseModel):
+    """Critique of the generated content."""
+
+    strengths: list[str] = Field(description="What's good about the content")
+    weaknesses: list[str] = Field(description="What needs improvement")
+    suggestions: list[str] = Field(description="Specific suggestions for improvement")
+    quality_score: int = Field(
+        description="Quality score from 1-10", ge=1, le=10
+    )
+    is_satisfactory: bool = Field(
+        description="Whether the content meets quality standards (score >= 7)"
+    )
+
+
+class ReflectionState(TypedDict):
+    """State for the reflection agent graph.
+
+    Attributes:
+        messages: Conversation history.
+        task: The writing/generation task.
+        current_draft: The current version of the content.
+        critiques: History of critiques for each iteration.
+        iteration: Current iteration number.
+        max_iterations: Maximum allowed iterations.
+        final_content: The final approved content.
+    """
+
+    messages: Annotated[list[BaseMessage], add_messages]
+    task: str
+    current_draft: str
+    critiques: list[Critique]
+    iteration: int
+    max_iterations: int
+    final_content: str
+
+
+def build_reflection_graph() -> Any:
+    """Build a reflection agent graph.
+
+    The graph implements an iterative improvement workflow:
+    1. Generate initial content
+    2. Reflect and critique
+    3. Revise based on feedback
+    4. Check quality and loop or finish
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0.7,  # Slightly higher for creative generation
+    )
+
+    critic_model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,  # Lower for consistent critique
+    )
+
+    def generate(state: ReflectionState) -> dict[str, Any]:
+        """Generate initial content based on the task.
+
+        Creates the first draft that will be refined through reflection.
+        """
+        messages = state["messages"]
+        task = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)),
+            "Write a short paragraph",
+        )
+
+        generate_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a skilled writer. Generate high-quality content based on
+the given task. Focus on:
+- Clarity and coherence
+- Relevant information
+- Engaging style
+- Proper structure
+
+Produce your best work on the first try.""",
+                ),
+                ("human", "{task}"),
+            ]
+        )
+
+        chain = generate_prompt | model | StrOutputParser()
+        draft = chain.invoke({"task": str(task)})
+
+        return {
+            "task": str(task),
+            "current_draft": draft,
+            "iteration": 1,
+            "max_iterations": state.get("max_iterations", 3),
+            "critiques": [],
+        }
+
+    def reflect(state: ReflectionState) -> dict[str, Any]:
+        """Reflect on the current draft and provide critique.
+
+        Analyzes the content for strengths, weaknesses, and
+        provides specific improvement suggestions.
+        """
+        task = state.get("task", "")
+        draft = state.get("current_draft", "")
+        iteration = state.get("iteration", 1)
+
+        reflect_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a thoughtful critic and editor. Analyze the given content
+and provide constructive feedback.
+
+Consider:
+- Does it fulfill the original task?
+- Is it clear and well-structured?
+- Is the information accurate and relevant?
+- Is the style engaging and appropriate?
+- What specific improvements would make it better?
+
+Be specific and actionable in your feedback.
+Score 7+ means the content is ready for publication.""",
+                ),
+                (
+                    "human",
+                    "Task: {task}\n\nDraft (iteration {iteration}):\n{draft}\n\nProvide your critique:",
+                ),
+            ]
+        )
+
+        critic = reflect_prompt | critic_model.with_structured_output(Critique)
+        critique = critic.invoke({"task": task, "draft": draft, "iteration": iteration})
+
+        return {
+            "critiques": state.get("critiques", []) + [critique],
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Iteration {iteration} critique: Score {critique.quality_score}/10. "
+                    f"{'Satisfactory!' if critique.is_satisfactory else 'Needs improvement.'}",
+                }
+            ],
+        }
+
+    def should_revise(state: ReflectionState) -> Literal["revise", "finalize"]:
+        """Decide whether to revise or finalize.
+
+        Routes to finalize if:
+        - Content is satisfactory (score >= 7)
+        - Max iterations reached
+
+        Otherwise continues to revise.
+        """
+        critiques = state.get("critiques", [])
+        iteration = state.get("iteration", 1)
+        max_iterations = state.get("max_iterations", 3)
+
+        if not critiques:
+            return "revise"
+
+        latest_critique = critiques[-1]
+
+        # Finalize if satisfactory or max iterations reached
+        if latest_critique.is_satisfactory or iteration >= max_iterations:
+            return "finalize"
+
+        return "revise"
+
+    def revise(state: ReflectionState) -> dict[str, Any]:
+        """Revise the content based on critique feedback.
+
+        Incorporates the suggestions to produce an improved version.
+        """
+        task = state.get("task", "")
+        draft = state.get("current_draft", "")
+        critiques = state.get("critiques", [])
+        iteration = state.get("iteration", 1)
+
+        if not critiques:
+            return {"iteration": iteration + 1}
+
+        latest_critique = critiques[-1]
+
+        # Format the feedback
+        feedback = f"""
+Strengths: {', '.join(latest_critique.strengths)}
+Weaknesses: {', '.join(latest_critique.weaknesses)}
+Suggestions: {', '.join(latest_critique.suggestions)}
+"""
+
+        revise_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a skilled writer revising your work based on feedback.
+Carefully address each weakness and incorporate the suggestions while
+preserving the strengths.
+
+Produce an improved version that addresses the critique.""",
+                ),
+                (
+                    "human",
+                    "Original task: {task}\n\nCurrent draft:\n{draft}\n\nFeedback:\n{feedback}\n\nRevised version:",
+                ),
+            ]
+        )
+
+        chain = revise_prompt | model | StrOutputParser()
+        revised = chain.invoke({"task": task, "draft": draft, "feedback": feedback})
+
+        return {
+            "current_draft": revised,
+            "iteration": iteration + 1,
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Revised draft (iteration {iteration + 1}) created.",
+                }
+            ],
+        }
+
+    def finalize(state: ReflectionState) -> dict[str, Any]:
+        """Finalize the content and prepare the response.
+
+        Marks the current draft as final and creates the summary.
+        """
+        draft = state.get("current_draft", "")
+        critiques = state.get("critiques", [])
+        iteration = state.get("iteration", 1)
+
+        # Get final score
+        final_score = critiques[-1].quality_score if critiques else 0
+
+        summary = f"""
+Content finalized after {iteration} iteration(s).
+Final quality score: {final_score}/10
+
+--- FINAL CONTENT ---
+{draft}
+"""
+
+        return {
+            "final_content": draft,
+            "messages": [{"role": "assistant", "content": summary}],
+        }
+
+    # Build the reflection graph
+    workflow = StateGraph(ReflectionState)
+
+    # Add nodes
+    workflow.add_node("generate", generate)
+    workflow.add_node("reflect", reflect)
+    workflow.add_node("revise", revise)
+    workflow.add_node("finalize", finalize)
+
+    # Add edges
+    workflow.add_edge(START, "generate")
+    workflow.add_edge("generate", "reflect")
+
+    # Conditional: revise or finalize based on critique
+    workflow.add_conditional_edges(
+        "reflect",
+        should_revise,
+        {"revise": "revise", "finalize": "finalize"},
+    )
+
+    # After revision, reflect again
+    workflow.add_edge("revise", "reflect")
+
+    workflow.add_edge("finalize", END)
+
+    return workflow.compile()
diff --git a/langgraph_samples/advanced/reflection/run_worker.py b/langgraph_samples/advanced/reflection/run_worker.py
new file mode 100644
index 00000000..bf1f6144
--- /dev/null
+++ b/langgraph_samples/advanced/reflection/run_worker.py
@@ -0,0 +1,46 @@
+"""Worker for the Reflection Agent sample.
+
+Starts a Temporal worker that can execute ReflectionWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_samples.advanced.reflection.graph import build_reflection_graph
+from langgraph_samples.advanced.reflection.workflow import ReflectionWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the reflection graph registered
+    plugin = LangGraphPlugin(
+        graphs={"reflection": build_reflection_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    worker = Worker(
+        client,
+        task_queue="langgraph-reflection",
+        workflows=[ReflectionWorkflow],
+    )
+
+    print("Reflection Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/advanced/reflection/run_workflow.py b/langgraph_samples/advanced/reflection/run_workflow.py
new file mode 100644
index 00000000..be6df3f9
--- /dev/null
+++ b/langgraph_samples/advanced/reflection/run_workflow.py
@@ -0,0 +1,64 @@
+"""Execute the Reflection workflow.
+
+Usage:
+    # First, in another terminal, start the worker:
+    python -m langgraph_samples.advanced.reflection.run_worker
+
+    # Then run this script:
+    python -m langgraph_samples.advanced.reflection.run_workflow
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_samples.advanced.reflection.workflow import ReflectionWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # A writing task that benefits from iterative refinement
+    # The agent will:
+    # 1. Generate initial content
+    # 2. Critique and identify improvements
+    # 3. Revise based on feedback
+    # 4. Repeat until quality score >= 7 or max iterations
+    result = await client.execute_workflow(
+        ReflectionWorkflow.run,
+        args=[
+            "Write a short technical blog post introduction about why "
+            "durable execution is important for AI agents. Target audience: "
+            "software engineers. Length: 2-3 paragraphs.",
+            3,  # max iterations
+        ],
+        id="reflection-workflow",
+        task_queue="langgraph-reflection",
+    )
+
+    # Print the refinement journey
+    print("\n" + "=" * 60)
+    print("REFLECTION JOURNEY")
+    print("=" * 60 + "\n")
+
+    # Show critique history
+    if result.get("critiques"):
+        for i, critique in enumerate(result["critiques"], 1):
+            print(f"Iteration {i} Critique:")
+            print(f"  Score: {critique.quality_score}/10")
+            print(f"  Strengths: {', '.join(critique.strengths[:2])}")
+            print(f"  Weaknesses: {', '.join(critique.weaknesses[:2])}")
+            print()
+
+    # Print final content
+    print("=" * 60)
+    print("FINAL CONTENT")
+    print("=" * 60 + "\n")
+    print(result.get("final_content", result["current_draft"]))
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/advanced/reflection/workflow.py b/langgraph_samples/advanced/reflection/workflow.py
new file mode 100644
index 00000000..0d94547f
--- /dev/null
+++ b/langgraph_samples/advanced/reflection/workflow.py
@@ -0,0 +1,58 @@
+"""Reflection Agent Workflow.
+
+Temporal workflow that executes the reflection agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class ReflectionWorkflow:
+    """Temporal workflow that executes a reflection agent.
+
+    This workflow:
+    1. Generates initial content
+    2. Reflects and critiques the content
+    3. Revises based on feedback
+    4. Loops until quality criteria met or max iterations
+
+    The Temporal integration ensures:
+    - Each generation/reflection/revision runs as an activity
+    - Progress is saved after each iteration
+    - Long refinement sessions complete reliably
+    - Quality improvement is visible in workflow history
+    """
+
+    @workflow.run
+    async def run(
+        self, task: str, max_iterations: int = 3
+    ) -> dict[str, Any]:
+        """Run the reflection agent on a writing task.
+
+        Args:
+            task: The content generation task.
+            max_iterations: Maximum refinement iterations (default 3).
+
+        Returns:
+            The final state containing the refined content.
+        """
+        app = compile("reflection")
+
+        result = await app.ainvoke(
+            {
+                "messages": [{"role": "user", "content": task}],
+                "critiques": [],
+                "iteration": 0,
+                "max_iterations": max_iterations,
+            }
+        )
+
+        return result
diff --git a/langgraph_samples/planning/README.md b/langgraph_samples/planning/README.md
new file mode 100644
index 00000000..d70d865a
--- /dev/null
+++ b/langgraph_samples/planning/README.md
@@ -0,0 +1,30 @@
+# Planning Samples
+
+This directory contains samples demonstrating planning patterns with Temporal LangGraph integration.
+
+## Available Samples
+
+### [Plan-and-Execute](./plan_and_execute/README.md)
+
+An agent that separates planning from execution, creating a high-level plan first and then executing each step sequentially with the ability to replan based on results.
+
+**Key Features:**
+- Structured planning with explicit steps
+- Sequential step execution with available tools
+- Dynamic replanning when steps fail
+- Progress visibility through Temporal
+- Checkpointing after each step
+
+## Why Planning Patterns?
+
+Planning agents offer several advantages:
+- **Predictability**: The plan is visible before execution
+- **Control**: Users can review/modify plans
+- **Resilience**: Failed steps can be replanned
+- **Observability**: Progress is tracked step-by-step
+
+## Prerequisites
+
+- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
+- OpenAI API key set: `export OPENAI_API_KEY=your-key`
+- Dependencies installed via `uv sync --group langgraph`
diff --git a/langgraph_samples/planning/__init__.py b/langgraph_samples/planning/__init__.py
new file mode 100644
index 00000000..37dae022
--- /dev/null
+++ b/langgraph_samples/planning/__init__.py
@@ -0,0 +1 @@
+"""Planning samples for Temporal LangGraph integration."""
diff --git a/langgraph_samples/planning/plan_and_execute/README.md b/langgraph_samples/planning/plan_and_execute/README.md
new file mode 100644
index 00000000..696c31dd
--- /dev/null
+++ b/langgraph_samples/planning/plan_and_execute/README.md
@@ -0,0 +1,175 @@
+# Plan-and-Execute Agent
+
+An agent that separates planning from execution, creating a high-level plan first and then executing each step sequentially with the ability to replan based on results.
+
+## Overview
+
+The Plan-and-Execute pattern:
+1. **Planning phase** - Analyzes the objective and creates a step-by-step plan
+2. **Execution phase** - Runs each step using available tools
+3. **Evaluation** - Checks progress after each step
+4. **Replanning** - Adjusts the plan if steps fail or new information emerges
+5. **Response** - Synthesizes results into a final answer
+
+## Why This Pattern?
+
+Plan-and-Execute offers advantages over pure ReAct:
+- **Structured approach**: Clear separation of thinking and doing
+- **Visibility**: The plan is visible before execution starts
+- **Resumability**: Temporal can resume from any step
+- **Adaptability**: Dynamic replanning when things change
+
+## Architecture
+
+```
+[Plan] --> [Execute Step 1] --> [Evaluate] --> [Execute Step 2] --> ...
+                                    |
+                                    v
+                               [Replan?] --> [New Plan] --> [Execute] --> ...
+                                    |
+                                    v
+                               [Respond] --> END
+```
+
+Each step runs as a Temporal activity with its own retry logic.
+
+## Available Tools
+
+The executor agent has access to:
+- `calculate`: Evaluate mathematical expressions
+- `lookup`: Retrieve information about topics
+- `analyze`: Analyze data or text
+
+## Running the Sample
+
+### Prerequisites
+
+- Temporal server running locally
+- OpenAI API key
+
+### Steps
+
+1. Start the Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. In one terminal, start the worker:
+   ```bash
+   export OPENAI_API_KEY=your-key-here
+   python -m langgraph_samples.planning.plan_and_execute.run_worker
+   ```
+
+3. In another terminal, run the workflow:
+   ```bash
+   python -m langgraph_samples.planning.plan_and_execute.run_workflow
+   ```
+
+## Sample Output
+
+```
+============================================================
+EXECUTION RESULT
+============================================================
+
+Steps Executed:
+  1. Look up information about LangGraph
+     Result: LangGraph is a library for building stateful, multi-actor LLM applications...
+  2. Look up information about other agent frameworks
+     Result: AI agents are autonomous systems that use LLMs...
+  3. Calculate the feature comparison
+     Result: Result: 5...
+
+Final Answer:
+LangGraph is a library for building stateful, multi-actor LLM applications with
+support for cycles, branches, and persistence. Compared to a basic ReAct agent,
+LangGraph offers 5 additional key features: state persistence, human-in-the-loop
+support, parallel execution, conditional branching, and streaming...
+```
+
+## Key Features Demonstrated
+
+### 1. Plan Generation
+```python
+class Plan(BaseModel):
+    objective: str
+    steps: list[PlanStep]
+
+# LLM generates structured plan
+planner = prompt | model.with_structured_output(Plan)
+plan = planner.invoke({"objective": objective})
+```
+
+### 2. Step-by-Step Execution
+```python
+def execute_step(state: PlanExecuteState) -> dict[str, Any]:
+    step = plan.steps[current_step]
+    result = executor_agent.invoke({"messages": [HumanMessage(step.description)]})
+    return {"step_results": [...], "current_step": current_step + 1}
+```
+
+### 3. Dynamic Routing
+```python
+def should_continue(state) -> Literal["execute", "replan", "respond"]:
+    if state.get("needs_replan"):
+        return "replan"
+    if current_step < len(plan.steps):
+        return "execute"
+    return "respond"
+```
+
+### 4. Replanning
+```python
+def replan(state: PlanExecuteState) -> dict[str, Any]:
+    # Consider what's been done and create new steps
+    new_plan = replanner.invoke({
+        "objective": objective,
+        "completed": completed_work
+    })
+    return {"plan": new_plan, "current_step": 0}
+```
+
+## Customization
+
+### Adding Custom Tools
+
+```python
+from langchain_core.tools import tool
+
+@tool
+def search_web(query: str) -> str:
+    """Search the web for information."""
+    # Implement actual web search
+    pass
+
+tools = [calculate, lookup, analyze, search_web]
+executor_agent = create_agent(model, tools)
+```
+
+### Adjusting Plan Constraints
+
+Modify the planner prompt to control plan characteristics:
+
+```python
+plan_prompt = ChatPromptTemplate.from_messages([
+    ("system", "Create a plan with 3-5 specific steps. Each step should take 1-2 minutes to execute..."),
+    ...
+])
+```
+
+## Comparison with ReAct
+
+| Aspect | ReAct | Plan-and-Execute |
+|--------|-------|------------------|
+| Approach | Think-act in each iteration | Plan all steps, then execute |
+| Visibility | Actions emerge dynamically | Full plan visible upfront |
+| Control | Less predictable | More structured |
+| Flexibility | High adaptability | Requires explicit replanning |
+| Best for | Exploratory tasks | Well-defined objectives |
+
+## Next Steps
+
+- Add more sophisticated tools (web search, code execution)
+- Implement plan approval (human-in-the-loop before execution)
+- Add parallel step execution for independent steps
+- Implement plan caching for similar objectives
diff --git a/langgraph_samples/planning/plan_and_execute/__init__.py b/langgraph_samples/planning/plan_and_execute/__init__.py
new file mode 100644
index 00000000..c8ada293
--- /dev/null
+++ b/langgraph_samples/planning/plan_and_execute/__init__.py
@@ -0,0 +1 @@
+"""Plan-and-Execute Agent sample for Temporal LangGraph integration."""
diff --git a/langgraph_samples/planning/plan_and_execute/graph.py b/langgraph_samples/planning/plan_and_execute/graph.py
new file mode 100644
index 00000000..14bb7a7c
--- /dev/null
+++ b/langgraph_samples/planning/plan_and_execute/graph.py
@@ -0,0 +1,426 @@
+"""Plan-and-Execute Agent Graph Definition.
+
+This module implements a plan-and-execute agent that first creates a plan,
+then executes each step sequentially with the ability to replan based on results.
+
+The agent pattern separates planning from execution:
+1. Planner creates a high-level plan with specific steps
+2. Executor runs each step using available tools
+3. After each step, the agent can replan if needed
+4. Progress is tracked and visible through Temporal
+
+Flow:
+1. plan - Create initial plan from the objective
+2. execute_step - Run the next step using tools
+3. evaluate - Check if more steps needed or replanning required
+4. replan (optional) - Adjust remaining steps based on results
+5. respond - Generate final response when complete
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from typing import Annotated, Any, Literal
+
+from langchain.agents import create_agent
+from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.tools import tool
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from typing_extensions import TypedDict
+
+
+class PlanStep(BaseModel):
+    """A single step in the execution plan."""
+
+    step_number: int = Field(description="The step number (1-indexed)")
+    description: str = Field(description="What this step should accomplish")
+    tool_hint: str = Field(
+        description="Suggested tool to use (calculate, lookup, or analyze)"
+    )
+
+
+class Plan(BaseModel):
+    """An execution plan with ordered steps."""
+
+    objective: str = Field(description="The main objective to accomplish")
+    steps: list[PlanStep] = Field(
+        description="Ordered list of steps to execute", min_length=1, max_length=5
+    )
+
+
+class StepResult(BaseModel):
+    """Result of executing a single step."""
+
+    step_number: int
+    description: str
+    result: str
+    success: bool
+
+
+class PlanExecuteState(TypedDict):
+    """State for the plan-and-execute graph.
+
+    Attributes:
+        messages: Conversation history with messages.
+        objective: The main task objective.
+        plan: The current execution plan.
+        step_results: Results from completed steps.
+        current_step: Index of the current step (0-indexed).
+        needs_replan: Whether replanning is needed.
+    """
+
+    messages: Annotated[list[BaseMessage], add_messages]
+    objective: str
+    plan: Plan | None
+    step_results: list[StepResult]
+    current_step: int
+    needs_replan: bool
+
+
+# Define tools for the executor agent
+@tool
+def calculate(expression: str) -> str:
+    """Evaluate a mathematical expression.
+
+    Args:
+        expression: A mathematical expression like "2 + 2" or "10 * 5"
+
+    Returns:
+        The result of the calculation
+    """
+    try:
+        # Safe evaluation of mathematical expressions
+        allowed_chars = set("0123456789+-*/.() ")
+        if not all(c in allowed_chars for c in expression):
+            return "Error: Invalid characters in expression"
+        result = eval(expression)  # noqa: S307
+        return f"Result: {result}"
+    except Exception as e:
+        return f"Error calculating: {str(e)}"
+
+
+@tool
+def lookup(topic: str) -> str:
+    """Look up information about a topic.
+
+    Args:
+        topic: The topic to look up information about
+
+    Returns:
+        Information about the topic
+    """
+    # Mock knowledge base
+    knowledge = {
+        "python": "Python is a high-level programming language known for its simplicity and readability.",
+        "temporal": "Temporal is a durable execution platform for running reliable workflows.",
+        "langgraph": "LangGraph is a library for building stateful, multi-actor LLM applications.",
+        "agents": "AI agents are autonomous systems that use LLMs to make decisions and take actions.",
+        "planning": "Planning agents first create a plan, then execute steps sequentially.",
+    }
+
+    topic_lower = topic.lower()
+    for key, value in knowledge.items():
+        if key in topic_lower:
+            return value
+
+    return f"No specific information found about '{topic}'. Consider breaking down the query."
+
+
+@tool
+def analyze(data: str) -> str:
+    """Analyze data or text and provide insights.
+
+    Args:
+        data: The data or text to analyze
+
+    Returns:
+        Analysis results
+    """
+    # Simple mock analysis
+    word_count = len(data.split())
+    char_count = len(data)
+    return f"Analysis: {word_count} words, {char_count} characters. The content discusses: {data[:100]}..."
+
+
+def build_plan_and_execute_graph() -> Any:
+    """Build a plan-and-execute agent graph.
+
+    The graph implements a planning workflow that:
+    1. Creates a plan with specific steps
+    2. Executes each step using available tools
+    3. Evaluates progress and replans if needed
+    4. Generates a final response
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    # Build executor agent with tools
+    tools = [calculate, lookup, analyze]
+    executor_agent = create_agent(model, tools)
+
+    def create_plan(state: PlanExecuteState) -> dict[str, Any]:
+        """Create an execution plan from the objective.
+
+        Analyzes the objective and breaks it down into
+        specific, actionable steps.
+        """
+        messages = state["messages"]
+        objective = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)),
+            "Complete the task",
+        )
+
+        plan_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a planning agent. Given an objective, create a step-by-step
+plan to accomplish it. Each step should be specific and actionable.
+
+Available tools for execution:
+- calculate: For mathematical computations
+- lookup: For retrieving information about topics
+- analyze: For analyzing data or text
+
+Create 2-4 steps that can be executed sequentially.""",
+                ),
+                ("human", "Objective: {objective}"),
+            ]
+        )
+
+        planner = plan_prompt | model.with_structured_output(Plan)
+        plan = planner.invoke({"objective": str(objective)})
+
+        return {
+            "objective": str(objective),
+            "plan": plan,
+            "current_step": 0,
+            "step_results": [],
+            "needs_replan": False,
+        }
+
+    def execute_step(state: PlanExecuteState) -> dict[str, Any]:
+        """Execute the current step using the executor agent.
+
+        Runs the executor agent with the step description as input.
+        The agent uses available tools to complete the step.
+        """
+        plan = state.get("plan")
+        current_step = state.get("current_step", 0)
+
+        if not plan or current_step >= len(plan.steps):
+            return {"needs_replan": False}
+
+        step = plan.steps[current_step]
+
+        # Run the executor agent for this step
+        result = executor_agent.invoke(
+            {
+                "messages": [
+                    SystemMessage(
+                        content=f"You are executing step {step.step_number} of a plan. "
+                        f"Complete this step: {step.description}\n"
+                        f"Suggested tool: {step.tool_hint}"
+                    ),
+                    HumanMessage(content=step.description),
+                ]
+            }
+        )
+
+        # Extract the result from the agent's response
+        result_content = ""
+        if result.get("messages"):
+            last_msg = result["messages"][-1]
+            result_content = (
+                last_msg.content
+                if hasattr(last_msg, "content")
+                else str(last_msg)
+            )
+
+        step_result = StepResult(
+            step_number=step.step_number,
+            description=step.description,
+            result=result_content,
+            success=True,
+        )
+
+        return {
+            "step_results": state.get("step_results", []) + [step_result],
+            "current_step": current_step + 1,
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Step {step.step_number} completed: {result_content[:200]}...",
+                }
+            ],
+        }
+
+    def evaluate_progress(state: PlanExecuteState) -> dict[str, Any]:
+        """Evaluate if we should continue, replan, or finish.
+
+        Checks completion status and determines next action.
+        """
+        plan = state.get("plan")
+        current_step = state.get("current_step", 0)
+        step_results = state.get("step_results", [])
+
+        if not plan:
+            return {"needs_replan": True}
+
+        # Check if all steps completed
+        all_complete = current_step >= len(plan.steps)
+
+        # Check if any step failed
+        failed_steps = [r for r in step_results if not r.success]
+
+        return {
+            "needs_replan": len(failed_steps) > 0,
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Progress: {current_step}/{len(plan.steps)} steps complete. "
+                    f"Failures: {len(failed_steps)}",
+                }
+            ]
+            if not all_complete
+            else [],
+        }
+
+    def should_continue(
+        state: PlanExecuteState,
+    ) -> Literal["execute", "replan", "respond"]:
+        """Decide whether to continue execution, replan, or respond.
+
+        Routes based on:
+        - needs_replan flag -> replan
+        - more steps remaining -> execute
+        - all steps complete -> respond
+        """
+        if state.get("needs_replan", False):
+            return "replan"
+
+        plan = state.get("plan")
+        current_step = state.get("current_step", 0)
+
+        if plan and current_step < len(plan.steps):
+            return "execute"
+
+        return "respond"
+
+    def replan(state: PlanExecuteState) -> dict[str, Any]:
+        """Adjust the plan based on execution results.
+
+        Creates a new plan considering what has been accomplished
+        and what failed, to better achieve the objective.
+        """
+        objective = state.get("objective", "Complete the task")
+        step_results = state.get("step_results", [])
+
+        # Format completed work
+        completed = "\n".join(
+            f"- Step {r.step_number}: {r.description} -> {r.result[:100]}"
+            for r in step_results
+        )
+
+        replan_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a replanning agent. Given the original objective and
+work completed so far, create a new plan for remaining work.
+
+Available tools: calculate, lookup, analyze
+
+Consider what has already been accomplished and create 1-3 new steps
+to complete the objective.""",
+                ),
+                (
+                    "human",
+                    "Objective: {objective}\n\nCompleted work:\n{completed}\n\nCreate new plan:",
+                ),
+            ]
+        )
+
+        planner = replan_prompt | model.with_structured_output(Plan)
+        new_plan = planner.invoke({"objective": objective, "completed": completed})
+
+        return {
+            "plan": new_plan,
+            "current_step": 0,
+            "needs_replan": False,
+        }
+
+    def respond(state: PlanExecuteState) -> dict[str, Any]:
+        """Generate the final response summarizing the execution.
+
+        Compiles all step results into a coherent final answer.
+        """
+        objective = state.get("objective", "")
+        step_results = state.get("step_results", [])
+
+        # Format all results
+        results_text = "\n".join(
+            f"{r.step_number}. {r.description}: {r.result}" for r in step_results
+        )
+
+        response_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a summarizer. Given an objective and the results of
+executing a plan, provide a clear, concise final answer.
+
+Focus on directly answering the original objective using the
+gathered information.""",
+                ),
+                (
+                    "human",
+                    "Objective: {objective}\n\nExecution results:\n{results}\n\nProvide final answer:",
+                ),
+            ]
+        )
+
+        chain = response_prompt | model | StrOutputParser()
+        response = chain.invoke({"objective": objective, "results": results_text})
+
+        return {"messages": [{"role": "assistant", "content": response}]}
+
+    # Build the plan-and-execute graph
+    workflow = StateGraph(PlanExecuteState)
+
+    # Add nodes
+    workflow.add_node("plan", create_plan)
+    workflow.add_node("execute", execute_step)
+    workflow.add_node("evaluate", evaluate_progress)
+    workflow.add_node("replan", replan)
+    workflow.add_node("respond", respond)
+
+    # Add edges
+    workflow.add_edge(START, "plan")
+    workflow.add_edge("plan", "execute")
+    workflow.add_edge("execute", "evaluate")
+
+    # Conditional routing after evaluation
+    workflow.add_conditional_edges(
+        "evaluate",
+        should_continue,
+        {"execute": "execute", "replan": "replan", "respond": "respond"},
+    )
+
+    # After replanning, continue execution
+    workflow.add_edge("replan", "execute")
+
+    workflow.add_edge("respond", END)
+
+    return workflow.compile()
diff --git a/langgraph_samples/planning/plan_and_execute/run_worker.py b/langgraph_samples/planning/plan_and_execute/run_worker.py
new file mode 100644
index 00000000..3104563d
--- /dev/null
+++ b/langgraph_samples/planning/plan_and_execute/run_worker.py
@@ -0,0 +1,50 @@
+"""Worker for the Plan-and-Execute Agent sample.
+
+Starts a Temporal worker that can execute PlanAndExecuteWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_samples.planning.plan_and_execute.graph import (
+    build_plan_and_execute_graph,
+)
+from langgraph_samples.planning.plan_and_execute.workflow import (
+    PlanAndExecuteWorkflow,
+)
+
+
+async def main() -> None:
+    # Create the plugin with the plan-and-execute graph registered
+    plugin = LangGraphPlugin(
+        graphs={"plan_and_execute": build_plan_and_execute_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    worker = Worker(
+        client,
+        task_queue="langgraph-plan-execute",
+        workflows=[PlanAndExecuteWorkflow],
+    )
+
+    print("Plan-and-Execute Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/planning/plan_and_execute/run_workflow.py b/langgraph_samples/planning/plan_and_execute/run_workflow.py
new file mode 100644
index 00000000..c94c52a0
--- /dev/null
+++ b/langgraph_samples/planning/plan_and_execute/run_workflow.py
@@ -0,0 +1,58 @@
+"""Execute the Plan-and-Execute workflow.
+
+Usage:
+    # First, in another terminal, start the worker:
+    python -m langgraph_samples.planning.plan_and_execute.run_worker
+
+    # Then run this script:
+    python -m langgraph_samples.planning.plan_and_execute.run_workflow
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_samples.planning.plan_and_execute.workflow import (
+    PlanAndExecuteWorkflow,
+)
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # A task that requires planning and multiple steps
+    # The agent will:
+    # 1. Create a plan with specific steps
+    # 2. Execute each step using tools (lookup, calculate, analyze)
+    # 3. Compile results into a final answer
+    result = await client.execute_workflow(
+        PlanAndExecuteWorkflow.run,
+        "What is LangGraph and how does it compare to other agent frameworks? "
+        "Calculate how many more features it has than a basic ReAct agent.",
+        id="plan-execute-workflow",
+        task_queue="langgraph-plan-execute",
+    )
+
+    # Print the final response
+    print("\n" + "=" * 60)
+    print("EXECUTION RESULT")
+    print("=" * 60 + "\n")
+
+    # Print step results if available
+    if result.get("step_results"):
+        print("Steps Executed:")
+        for step in result["step_results"]:
+            print(f"  {step.step_number}. {step.description}")
+            print(f"     Result: {step.result[:100]}...")
+        print()
+
+    # Print final message
+    print("Final Answer:")
+    print(result["messages"][-1]["content"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/planning/plan_and_execute/workflow.py b/langgraph_samples/planning/plan_and_execute/workflow.py
new file mode 100644
index 00000000..9f4a5c13
--- /dev/null
+++ b/langgraph_samples/planning/plan_and_execute/workflow.py
@@ -0,0 +1,55 @@
+"""Plan-and-Execute Agent Workflow.
+
+Temporal workflow that executes the plan-and-execute agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class PlanAndExecuteWorkflow:
+    """Temporal workflow that executes a plan-and-execute agent.
+
+    This workflow:
+    1. Creates a plan from the objective
+    2. Executes each step sequentially
+    3. Replans if steps fail
+    4. Returns the final result
+
+    The Temporal integration ensures:
+    - Each step runs as a separate activity
+    - Failed steps can be retried or replanned
+    - Progress is checkpointed after each step
+    - Long-running plans complete reliably
+    """
+
+    @workflow.run
+    async def run(self, objective: str) -> dict[str, Any]:
+        """Run the plan-and-execute agent.
+
+        Args:
+            objective: The task to accomplish.
+
+        Returns:
+            The final state containing the execution results.
+        """
+        app = compile("plan_and_execute")
+
+        result = await app.ainvoke(
+            {
+                "messages": [{"role": "user", "content": objective}],
+                "step_results": [],
+                "current_step": 0,
+                "needs_replan": False,
+            }
+        )
+
+        return result
diff --git a/langgraph_samples/rag/README.md b/langgraph_samples/rag/README.md
index 84f91d26..ccf8ba6f 100644
--- a/langgraph_samples/rag/README.md
+++ b/langgraph_samples/rag/README.md
@@ -14,6 +14,17 @@ An intelligent RAG system that decides when to retrieve documents, grades their
 - Query rewriting for better retrieval
 - Durable execution with Temporal
 
+### [Deep Research Agent](./deep_research/README.md)
+
+A multi-step research agent that performs iterative research to produce comprehensive reports.
+
+**Key Features:**
+- Research planning with targeted search queries
+- Parallel search execution using LangGraph's Send API
+- Result evaluation and iteration
+- Long-running research with Temporal durability
+- Report synthesis from multiple sources
+
 ## Prerequisites
 
 - Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
diff --git a/langgraph_samples/rag/deep_research/README.md b/langgraph_samples/rag/deep_research/README.md
new file mode 100644
index 00000000..072fa334
--- /dev/null
+++ b/langgraph_samples/rag/deep_research/README.md
@@ -0,0 +1,145 @@
+# Deep Research Agent
+
+A multi-step research agent that performs iterative research to produce comprehensive reports. This sample demonstrates how Temporal's durable execution enables long-running research workflows.
+
+## Overview
+
+The Deep Research Agent:
+1. **Plans research** - Analyzes the topic and generates targeted search queries
+2. **Parallel search** - Executes multiple searches concurrently using LangGraph's Send API
+3. **Evaluates results** - Grades search results for relevance and coverage
+4. **Iterates** - Continues researching if more information is needed
+5. **Synthesizes** - Produces a comprehensive research report
+
+## Why Temporal?
+
+Research workflows are ideal for Temporal because:
+- **Long-running**: Research can take minutes to hours
+- **Parallel execution**: Multiple searches run as separate activities
+- **Fault tolerance**: If a search fails, only that activity retries
+- **Visibility**: Progress is tracked in Temporal's UI
+- **Resumable**: Interrupted research continues where it left off
+
+## Architecture
+
+```
+[Plan] --> [Search 1] -\
+       --> [Search 2] ----> [Evaluate] --> [Synthesize] --> END
+       --> [Search 3] -/         |
+                                 v
+                            [Continue?]
+                                 |
+                           [More Plan] --> ...
+```
+
+Each search runs as a separate Temporal activity, enabling parallel execution.
+
+## Running the Sample
+
+### Prerequisites
+
+- Temporal server running locally
+- OpenAI API key
+
+### Steps
+
+1. Start the Temporal server:
+   ```bash
+   temporal server start-dev
+   ```
+
+2. In one terminal, start the worker:
+   ```bash
+   export OPENAI_API_KEY=your-key-here
+   python -m langgraph_samples.rag.deep_research.run_worker
+   ```
+
+3. In another terminal, run the workflow:
+   ```bash
+   python -m langgraph_samples.rag.deep_research.run_workflow
+   ```
+
+## Sample Output
+
+```
+============================================================
+RESEARCH REPORT
+============================================================
+
+## Executive Summary
+LangGraph and Temporal combine to create durable AI agents that survive
+failures and can handle long-running workflows...
+
+## Main Findings
+### Core Concepts
+- LangGraph provides the agent graph structure
+- Temporal provides durable execution...
+
+### Integration Benefits
+- Each graph node runs as a Temporal activity
+- Automatic retries for failed operations...
+
+## Conclusions
+The LangGraph-Temporal integration is ideal for production AI agents...
+```
+
+## Key Features Demonstrated
+
+### 1. Parallel Search with Send API
+```python
+def fan_out_searches(state: ResearchState) -> list[Send]:
+    """Fan out to parallel search executions."""
+    return [
+        Send("search", {"query": q.query, "purpose": q.purpose})
+        for q in state.get("search_queries", [])
+    ]
+```
+
+### 2. Research Iteration Loop
+```python
+def should_continue(state: ResearchState) -> Literal["synthesize", "plan"]:
+    """Decide whether to continue researching."""
+    if iteration < max_iterations and coverage < threshold:
+        return "plan"  # Continue researching
+    return "synthesize"  # Ready to report
+```
+
+### 3. Result Aggregation
+Results from parallel searches are aggregated using a reducer:
+```python
+search_results: Annotated[list[SearchResult], lambda x, y: x + y]
+```
+
+## Customization
+
+### Using Real Search APIs
+
+Replace the mock search with real APIs:
+
+```python
+from langchain_community.tools import DuckDuckGoSearchRun
+
+def execute_search(state: SearchTaskState) -> dict[str, Any]:
+    search = DuckDuckGoSearchRun()
+    results = search.run(state["query"])
+    return {"search_results": [{"query": state["query"], "results": results, ...}]}
+```
+
+### Adjusting Research Depth
+
+Control iteration depth when starting the workflow:
+
+```python
+result = await client.execute_workflow(
+    DeepResearchWorkflow.run,
+    args=["Your research topic", 3],  # max 3 iterations
+    ...
+)
+```
+
+## Next Steps
+
+- Add real search API integration (DuckDuckGo, Tavily, Exa)
+- Implement continue-as-new for very long research sessions
+- Add human review checkpoints for important findings
+- Store research artifacts in Temporal's data store
diff --git a/langgraph_samples/rag/deep_research/__init__.py b/langgraph_samples/rag/deep_research/__init__.py
new file mode 100644
index 00000000..cc527396
--- /dev/null
+++ b/langgraph_samples/rag/deep_research/__init__.py
@@ -0,0 +1 @@
+"""Deep Research Agent sample for Temporal LangGraph integration."""
diff --git a/langgraph_samples/rag/deep_research/graph.py b/langgraph_samples/rag/deep_research/graph.py
new file mode 100644
index 00000000..b53b6c7d
--- /dev/null
+++ b/langgraph_samples/rag/deep_research/graph.py
@@ -0,0 +1,339 @@
+"""Deep Research Agent Graph Definition.
+
+This module implements a multi-step research agent that performs iterative
+research to produce comprehensive reports. It demonstrates:
+- Parallel search execution using LangGraph's Send API
+- Result evaluation and iteration
+- Long-running research workflows (showcasing Temporal's durability)
+- Report synthesis from multiple sources
+
+The research flow:
+1. plan_research - Analyze the topic and generate search queries
+2. search (parallel) - Execute multiple searches concurrently
+3. evaluate_results - Grade search results for relevance
+4. decide_next - Either continue researching or synthesize
+5. synthesize - Produce final research report
+
+Note: This module is only imported by the worker (not by the workflow).
+LangGraph cannot be imported in the workflow sandbox.
+"""
+
+import os
+from typing import Annotated, Any, Literal
+
+from langchain_core.messages import BaseMessage, HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_openai import ChatOpenAI
+from langgraph.constants import Send
+from langgraph.graph import END, START, StateGraph
+from langgraph.graph.message import add_messages
+from pydantic import BaseModel, Field
+from typing_extensions import TypedDict
+
+
+class SearchQuery(BaseModel):
+    """A search query to execute."""
+
+    query: str = Field(description="The search query to execute")
+    purpose: str = Field(description="What information this query aims to find")
+
+
+class ResearchPlan(BaseModel):
+    """Research plan with multiple search queries."""
+
+    queries: list[SearchQuery] = Field(
+        description="List of search queries to execute", min_length=1, max_length=5
+    )
+
+
+class SearchResult(TypedDict):
+    """Result from a single search."""
+
+    query: str
+    purpose: str
+    results: str
+    relevant: bool
+
+
+class ResearchState(TypedDict):
+    """State for the deep research graph.
+
+    Attributes:
+        messages: Conversation history with the research question.
+        topic: The main research topic extracted from the question.
+        search_queries: Planned search queries from the planner.
+        search_results: Results from executed searches.
+        iteration: Current research iteration (for limiting loops).
+        max_iterations: Maximum number of research iterations.
+    """
+
+    messages: Annotated[list[BaseMessage], add_messages]
+    topic: str
+    search_queries: list[SearchQuery]
+    search_results: Annotated[list[SearchResult], lambda x, y: x + y]
+    iteration: int
+    max_iterations: int
+
+
+class SearchTaskState(TypedDict):
+    """State for individual search tasks (used with Send)."""
+
+    query: str
+    purpose: str
+
+
+# Simulated web search results (in production, use a real search API)
+MOCK_SEARCH_RESULTS = {
+    "langgraph": """LangGraph is a library for building stateful, multi-actor applications
+    with LLMs. It provides support for cycles, branches, and state persistence. Key features:
+    - StateGraph for defining application flow
+    - Built-in persistence and checkpointing
+    - Human-in-the-loop support with interrupts
+    - Streaming support for real-time updates""",
+    "temporal": """Temporal is a durable execution platform. Key concepts:
+    - Workflows: Long-running processes that survive failures
+    - Activities: Units of work with automatic retries
+    - Signals: External events sent to workflows
+    - Queries: Read-only state inspection""",
+    "durable": """Durable execution ensures code runs to completion despite failures.
+    Benefits include: automatic retry of failed operations, state preservation
+    across restarts, and reliable execution of long-running processes.""",
+    "agent": """AI agents are autonomous systems that use LLMs to decide actions.
+    Common patterns include ReAct (reasoning + acting), plan-and-execute,
+    and multi-agent collaboration. Tools enable agents to interact with
+    external systems and data sources.""",
+    "rag": """RAG (Retrieval Augmented Generation) enhances LLM responses with
+    external knowledge. Agentic RAG adds decision-making about when to retrieve,
+    document grading, and query rewriting for better results.""",
+    "research": """Research agents perform multi-step information gathering.
+    They plan queries, execute searches, evaluate results, and synthesize
+    findings into comprehensive reports.""",
+}
+
+
+def build_deep_research_graph() -> Any:
+    """Build a deep research agent graph.
+
+    The graph implements a research workflow that:
+    1. Plans research by generating targeted search queries
+    2. Executes searches in parallel using Send
+    3. Evaluates results for relevance
+    4. Iterates if more research is needed
+    5. Synthesizes findings into a final report
+
+    Returns:
+        A compiled LangGraph that can be executed with ainvoke().
+    """
+    model = ChatOpenAI(
+        model=os.environ.get("OPENAI_MODEL", "gpt-4o-mini"),
+        temperature=0,
+    )
+
+    def plan_research(state: ResearchState) -> dict[str, Any]:
+        """Plan the research by generating search queries.
+
+        Analyzes the research topic and creates a set of targeted
+        queries to gather comprehensive information.
+        """
+        messages = state["messages"]
+        topic = next(
+            (m.content for m in messages if isinstance(m, HumanMessage)),
+            "AI agents",
+        )
+        topic_str = str(topic)
+
+        plan_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a research planner. Given a research topic, generate 2-4 search
+queries that will help gather comprehensive information. Each query should
+target a different aspect of the topic.
+
+Consider:
+- Core concepts and definitions
+- Key features and capabilities
+- Use cases and applications
+- Comparisons or alternatives""",
+                ),
+                ("human", "Research topic: {topic}"),
+            ]
+        )
+
+        planner = plan_prompt | model.with_structured_output(ResearchPlan)
+        plan = planner.invoke({"topic": topic_str})
+
+        return {
+            "topic": topic_str,
+            "search_queries": plan.queries,
+            "iteration": state.get("iteration", 0) + 1,
+            "max_iterations": state.get("max_iterations", 2),
+        }
+
+    def execute_search(state: SearchTaskState) -> dict[str, Any]:
+        """Execute a single search query.
+
+        In production, this would call a real search API.
+        Here we use mock results for demonstration.
+        """
+        query = state["query"].lower()
+        purpose = state["purpose"]
+
+        # Find matching mock results
+        results_parts = []
+        for keyword, content in MOCK_SEARCH_RESULTS.items():
+            if keyword in query:
+                results_parts.append(content)
+
+        if not results_parts:
+            results = "No specific results found. The topic may require more specialized research."
+        else:
+            results = "\n\n".join(results_parts)
+
+        return {
+            "search_results": [
+                {
+                    "query": state["query"],
+                    "purpose": purpose,
+                    "results": results,
+                    "relevant": len(results_parts) > 0,
+                }
+            ]
+        }
+
+    def fan_out_searches(
+        state: ResearchState,
+    ) -> list[Send]:
+        """Fan out to parallel search executions.
+
+        Creates a Send for each search query, enabling parallel execution
+        of searches as separate Temporal activities.
+        """
+        sends = []
+        for query in state.get("search_queries", []):
+            sends.append(
+                Send(
+                    "search",
+                    {"query": query.query, "purpose": query.purpose},
+                )
+            )
+        return sends
+
+    def evaluate_results(state: ResearchState) -> dict[str, Any]:
+        """Evaluate search results for coverage and quality.
+
+        Determines if we have enough relevant information to synthesize
+        a comprehensive report, or if more research is needed.
+        """
+        results = state.get("search_results", [])
+        relevant_count = sum(1 for r in results if r.get("relevant", False))
+
+        # Simple evaluation: check if we have enough relevant results
+        coverage = relevant_count / max(len(results), 1)
+
+        return {
+            "messages": [
+                {
+                    "role": "assistant",
+                    "content": f"Research iteration {state['iteration']}: "
+                    f"Found {relevant_count}/{len(results)} relevant results. "
+                    f"Coverage: {coverage:.0%}",
+                }
+            ]
+        }
+
+    def should_continue(state: ResearchState) -> Literal["synthesize", "plan"]:
+        """Decide whether to continue researching or synthesize.
+
+        Routes to synthesis if:
+        - We have reached max iterations
+        - We have sufficient relevant results
+
+        Otherwise, continues with another research iteration.
+        """
+        iteration = state.get("iteration", 1)
+        max_iterations = state.get("max_iterations", 2)
+        results = state.get("search_results", [])
+        relevant_count = sum(1 for r in results if r.get("relevant", False))
+
+        # Continue if under max iterations and low coverage
+        if iteration < max_iterations and relevant_count < 2:
+            return "plan"
+
+        return "synthesize"
+
+    def synthesize_report(state: ResearchState) -> dict[str, Any]:
+        """Synthesize research findings into a comprehensive report.
+
+        Combines all relevant search results into a well-structured
+        research report that answers the original question.
+        """
+        topic = state.get("topic", "the research topic")
+        results = state.get("search_results", [])
+
+        # Compile all findings
+        findings = []
+        for result in results:
+            if result.get("relevant", False):
+                findings.append(
+                    f"### {result['purpose']}\n{result['results']}"
+                )
+
+        if not findings:
+            findings_text = "Limited information was found on this topic."
+        else:
+            findings_text = "\n\n".join(findings)
+
+        synthesis_prompt = ChatPromptTemplate.from_messages(
+            [
+                (
+                    "system",
+                    """You are a research synthesizer. Create a comprehensive research report
+from the gathered findings. Structure the report with:
+1. Executive Summary - Key takeaways in 2-3 sentences
+2. Main Findings - Detailed information organized by topic
+3. Conclusions - Synthesis of the research
+
+Be thorough but concise. Cite the source topics when presenting findings.""",
+                ),
+                (
+                    "human",
+                    "Research Topic: {topic}\n\nGathered Findings:\n{findings}",
+                ),
+            ]
+        )
+
+        chain = synthesis_prompt | model | StrOutputParser()
+        report = chain.invoke({"topic": topic, "findings": findings_text})
+
+        return {"messages": [{"role": "assistant", "content": report}]}
+
+    # Build the research graph
+    workflow = StateGraph(ResearchState)
+
+    # Add nodes
+    workflow.add_node("plan", plan_research)
+    workflow.add_node("search", execute_search)
+    workflow.add_node("evaluate", evaluate_results)
+    workflow.add_node("synthesize", synthesize_report)
+
+    # Add edges
+    workflow.add_edge(START, "plan")
+
+    # Fan out from plan to parallel searches
+    workflow.add_conditional_edges("plan", fan_out_searches, ["search"])
+
+    # All searches converge at evaluate
+    workflow.add_edge("search", "evaluate")
+
+    # Decide whether to continue or synthesize
+    workflow.add_conditional_edges(
+        "evaluate",
+        should_continue,
+        {"synthesize": "synthesize", "plan": "plan"},
+    )
+
+    workflow.add_edge("synthesize", END)
+
+    return workflow.compile()
diff --git a/langgraph_samples/rag/deep_research/run_worker.py b/langgraph_samples/rag/deep_research/run_worker.py
new file mode 100644
index 00000000..f48bac7b
--- /dev/null
+++ b/langgraph_samples/rag/deep_research/run_worker.py
@@ -0,0 +1,46 @@
+"""Worker for the Deep Research Agent sample.
+
+Starts a Temporal worker that can execute DeepResearchWorkflow.
+The LangGraphPlugin registers the graph and handles activity registration.
+
+Prerequisites:
+    - Temporal server running locally
+    - OPENAI_API_KEY environment variable set
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.contrib.langgraph import LangGraphPlugin
+from temporalio.envconfig import ClientConfig
+from temporalio.worker import Worker
+
+from langgraph_samples.rag.deep_research.graph import build_deep_research_graph
+from langgraph_samples.rag.deep_research.workflow import DeepResearchWorkflow
+
+
+async def main() -> None:
+    # Create the plugin with the deep research graph registered
+    plugin = LangGraphPlugin(
+        graphs={"deep_research": build_deep_research_graph},
+    )
+
+    # Connect to Temporal with the plugin
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config, plugins=[plugin])
+
+    # Create and run the worker
+    worker = Worker(
+        client,
+        task_queue="langgraph-deep-research",
+        workflows=[DeepResearchWorkflow],
+    )
+
+    print("Deep Research Agent worker started. Ctrl+C to exit.")
+    print("Make sure OPENAI_API_KEY is set in your environment.")
+    await worker.run()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/rag/deep_research/run_workflow.py b/langgraph_samples/rag/deep_research/run_workflow.py
new file mode 100644
index 00000000..43248196
--- /dev/null
+++ b/langgraph_samples/rag/deep_research/run_workflow.py
@@ -0,0 +1,45 @@
+"""Execute the Deep Research workflow.
+
+Usage:
+    # First, in another terminal, start the worker:
+    python -m langgraph_samples.rag.deep_research.run_worker
+
+    # Then run this script:
+    python -m langgraph_samples.rag.deep_research.run_workflow
+"""
+
+import asyncio
+
+from temporalio.client import Client
+from temporalio.envconfig import ClientConfig
+
+from langgraph_samples.rag.deep_research.workflow import DeepResearchWorkflow
+
+
+async def main() -> None:
+    config = ClientConfig.load_client_connect_config()
+    config.setdefault("target_host", "localhost:7233")
+    client = await Client.connect(**config)
+
+    # Research topic that will trigger multiple search queries
+    # The agent will:
+    # 1. Plan queries about LangGraph, Temporal, durable execution
+    # 2. Execute searches in parallel
+    # 3. Evaluate results and possibly iterate
+    # 4. Synthesize a comprehensive research report
+    result = await client.execute_workflow(
+        DeepResearchWorkflow.run,
+        "How do LangGraph and Temporal work together for durable AI agents?",
+        id="deep-research-workflow",
+        task_queue="langgraph-deep-research",
+    )
+
+    # Print the research report (last message)
+    print("\n" + "=" * 60)
+    print("RESEARCH REPORT")
+    print("=" * 60 + "\n")
+    print(result["messages"][-1]["content"])
+
+
+if __name__ == "__main__":
+    asyncio.run(main())
diff --git a/langgraph_samples/rag/deep_research/workflow.py b/langgraph_samples/rag/deep_research/workflow.py
new file mode 100644
index 00000000..83545513
--- /dev/null
+++ b/langgraph_samples/rag/deep_research/workflow.py
@@ -0,0 +1,58 @@
+"""Deep Research Agent Workflow.
+
+Temporal workflow that executes the deep research agent with durable execution.
+
+Note: This module only contains the workflow definition. The graph definition
+is in graph.py and is only imported by the worker (not by this workflow module).
+This separation is required because LangGraph cannot be imported in the
+workflow sandbox.
+"""
+
+from typing import Any
+
+from temporalio import workflow
+from temporalio.contrib.langgraph import compile
+
+
+@workflow.defn
+class DeepResearchWorkflow:
+    """Temporal workflow that executes deep research with durable execution.
+
+    This workflow performs multi-step research on a topic:
+    1. Plans research queries
+    2. Executes parallel searches
+    3. Evaluates and iterates if needed
+    4. Synthesizes a comprehensive report
+
+    The Temporal integration ensures:
+    - Each search runs as a separate activity (parallel execution)
+    - Research survives worker crashes
+    - Long-running research (minutes to hours) completes reliably
+    - Progress is visible through workflow history
+    """
+
+    @workflow.run
+    async def run(
+        self, topic: str, max_iterations: int = 2
+    ) -> dict[str, Any]:
+        """Run deep research on a topic.
+
+        Args:
+            topic: The research topic or question.
+            max_iterations: Maximum research iterations (default 2).
+
+        Returns:
+            The final state containing the research report.
+        """
+        app = compile("deep_research")
+
+        result = await app.ainvoke(
+            {
+                "messages": [{"role": "user", "content": topic}],
+                "search_results": [],
+                "iteration": 0,
+                "max_iterations": max_iterations,
+            }
+        )
+
+        return result

From 0beeb3cfb50f55024abbf89fcc383413acaa486a Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Mon, 29 Dec 2025 11:41:54 -0800
Subject: [PATCH 20/24] LangGraph samples: Standardize usage instructions in
 READMEs

- Update all README files to use `uv run` format for running scripts
- Remove redundant Usage sections from run_*.py files
- Keep usage documentation centralized in README files only
---
 langgraph_samples/advanced/reflection/README.md    |  4 ++--
 .../advanced/reflection/run_workflow.py            | 10 +---------
 .../basic/react_agent/run_workflow.py              | 10 +---------
 .../human_in_loop/approval_workflow/README.md      | 14 +++++++-------
 .../human_in_loop/approval_workflow/run_respond.py | 10 ----------
 .../human_in_loop/approval_workflow/run_worker.py  |  3 ---
 .../approval_workflow/run_workflow.py              | 10 ----------
 .../multi_agent/supervisor/run_workflow.py         | 10 +---------
 .../planning/plan_and_execute/README.md            |  4 ++--
 .../planning/plan_and_execute/run_workflow.py      | 10 +---------
 langgraph_samples/rag/agentic_rag/run_workflow.py  | 10 +---------
 langgraph_samples/rag/deep_research/README.md      |  4 ++--
 .../rag/deep_research/run_workflow.py              | 10 +---------
 13 files changed, 19 insertions(+), 90 deletions(-)

diff --git a/langgraph_samples/advanced/reflection/README.md b/langgraph_samples/advanced/reflection/README.md
index 747fb7de..1124c975 100644
--- a/langgraph_samples/advanced/reflection/README.md
+++ b/langgraph_samples/advanced/reflection/README.md
@@ -56,12 +56,12 @@ Each node runs as a Temporal activity with automatic retry.
 2. In one terminal, start the worker:
    ```bash
    export OPENAI_API_KEY=your-key-here
-   python -m langgraph_samples.advanced.reflection.run_worker
+   uv run langgraph_samples/advanced/reflection/run_worker.py
    ```
 
 3. In another terminal, run the workflow:
    ```bash
-   python -m langgraph_samples.advanced.reflection.run_workflow
+   uv run langgraph_samples/advanced/reflection/run_workflow.py
    ```
 
 ## Sample Output
diff --git a/langgraph_samples/advanced/reflection/run_workflow.py b/langgraph_samples/advanced/reflection/run_workflow.py
index be6df3f9..f10b6f16 100644
--- a/langgraph_samples/advanced/reflection/run_workflow.py
+++ b/langgraph_samples/advanced/reflection/run_workflow.py
@@ -1,12 +1,4 @@
-"""Execute the Reflection workflow.
-
-Usage:
-    # First, in another terminal, start the worker:
-    python -m langgraph_samples.advanced.reflection.run_worker
-
-    # Then run this script:
-    python -m langgraph_samples.advanced.reflection.run_workflow
-"""
+"""Execute the Reflection workflow."""
 
 import asyncio
 
diff --git a/langgraph_samples/basic/react_agent/run_workflow.py b/langgraph_samples/basic/react_agent/run_workflow.py
index ceaf345a..49f49f48 100644
--- a/langgraph_samples/basic/react_agent/run_workflow.py
+++ b/langgraph_samples/basic/react_agent/run_workflow.py
@@ -1,12 +1,4 @@
-"""Execute the ReAct Agent workflow.
-
-Usage:
-    # First, in another terminal, start the worker:
-    python -m langgraph_samples.basic.react_agent.run_worker
-
-    # Then run this script:
-    python -m langgraph_samples.basic.react_agent.run_workflow
-"""
+"""Execute the ReAct Agent workflow."""
 
 import asyncio
 
diff --git a/langgraph_samples/human_in_loop/approval_workflow/README.md b/langgraph_samples/human_in_loop/approval_workflow/README.md
index a179a1cc..174cc5a9 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/README.md
+++ b/langgraph_samples/human_in_loop/approval_workflow/README.md
@@ -31,12 +31,12 @@ Request → [Process & Assess Risk] → [Interrupt] → [Notify Approver] → [W
 
 **Terminal 1 - Start the worker:**
 ```bash
-uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_worker
+uv run langgraph_samples/human_in_loop/approval_workflow/run_worker.py
 ```
 
 **Terminal 2 - Start a workflow:**
 ```bash
-uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_workflow
+uv run langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
 ```
 
 The worker will print notification instructions like:
@@ -46,20 +46,20 @@ Workflow ID: approval-abc12345
 Request: Please approve purchase for $500.00 (Risk: medium)
 
 To respond, run:
-  Approve: uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --approve --reason 'Your reason'
-  Reject:  uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --reject --reason 'Your reason'
+  Approve: uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --approve --reason 'Your reason'
+  Reject:  uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --reject --reason 'Your reason'
 ```
 
 **Terminal 3 - Respond to the approval request:**
 ```bash
 # Check status
-uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --status
+uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --status
 
 # Approve
-uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --approve --reason "Within budget"
+uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --approve --reason "Within budget"
 
 # Or reject
-uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond approval-abc12345 --reject --reason "Needs manager approval"
+uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --reject --reason "Needs manager approval"
 ```
 
 ## Response Script Options
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_respond.py b/langgraph_samples/human_in_loop/approval_workflow/run_respond.py
index b4417910..f51a2326 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/run_respond.py
+++ b/langgraph_samples/human_in_loop/approval_workflow/run_respond.py
@@ -1,16 +1,6 @@
 """Respond to an approval request.
 
 This script allows an approver to approve or reject a pending approval workflow.
-
-Usage:
-    # Approve a request:
-    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond <workflow-id> --approve --reason "Approved by manager"
-
-    # Reject a request:
-    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond <workflow-id> --reject --reason "Budget exceeded"
-
-    # Check status first:
-    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond <workflow-id> --status
 """
 
 import argparse
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_worker.py b/langgraph_samples/human_in_loop/approval_workflow/run_worker.py
index ac1d04ac..de667d3e 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/run_worker.py
+++ b/langgraph_samples/human_in_loop/approval_workflow/run_worker.py
@@ -1,9 +1,6 @@
 """Run the Approval Workflow worker.
 
 Starts a Temporal worker that can execute the approval workflow.
-
-Usage:
-    python -m langgraph_samples.human_in_loop.approval_workflow.run_worker
 """
 
 import asyncio
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py b/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
index 2b1bfb72..2d2107e9 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
+++ b/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
@@ -2,16 +2,6 @@
 
 Starts an approval workflow that pauses for human approval.
 The worker will print instructions for how to approve/reject.
-
-Usage:
-    # First, start the worker in another terminal:
-    python -m langgraph_samples.human_in_loop.approval_workflow.run_worker
-
-    # Then run this script to start a workflow:
-    python -m langgraph_samples.human_in_loop.approval_workflow.run_workflow
-
-    # Use the respond script to approve/reject:
-    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond <workflow-id> --approve --reason "OK"
 """
 
 import asyncio
diff --git a/langgraph_samples/multi_agent/supervisor/run_workflow.py b/langgraph_samples/multi_agent/supervisor/run_workflow.py
index 42a936c3..6d5f1103 100644
--- a/langgraph_samples/multi_agent/supervisor/run_workflow.py
+++ b/langgraph_samples/multi_agent/supervisor/run_workflow.py
@@ -1,12 +1,4 @@
-"""Execute the Supervisor Multi-Agent workflow.
-
-Usage:
-    # First, in another terminal, start the worker:
-    python -m langgraph_samples.multi_agent.supervisor.run_worker
-
-    # Then run this script:
-    python -m langgraph_samples.multi_agent.supervisor.run_workflow
-"""
+"""Execute the Supervisor Multi-Agent workflow."""
 
 import asyncio
 
diff --git a/langgraph_samples/planning/plan_and_execute/README.md b/langgraph_samples/planning/plan_and_execute/README.md
index 696c31dd..8205ecb3 100644
--- a/langgraph_samples/planning/plan_and_execute/README.md
+++ b/langgraph_samples/planning/plan_and_execute/README.md
@@ -57,12 +57,12 @@ The executor agent has access to:
 2. In one terminal, start the worker:
    ```bash
    export OPENAI_API_KEY=your-key-here
-   python -m langgraph_samples.planning.plan_and_execute.run_worker
+   uv run langgraph_samples/planning/plan_and_execute/run_worker.py
    ```
 
 3. In another terminal, run the workflow:
    ```bash
-   python -m langgraph_samples.planning.plan_and_execute.run_workflow
+   uv run langgraph_samples/planning/plan_and_execute/run_workflow.py
    ```
 
 ## Sample Output
diff --git a/langgraph_samples/planning/plan_and_execute/run_workflow.py b/langgraph_samples/planning/plan_and_execute/run_workflow.py
index c94c52a0..1844c14f 100644
--- a/langgraph_samples/planning/plan_and_execute/run_workflow.py
+++ b/langgraph_samples/planning/plan_and_execute/run_workflow.py
@@ -1,12 +1,4 @@
-"""Execute the Plan-and-Execute workflow.
-
-Usage:
-    # First, in another terminal, start the worker:
-    python -m langgraph_samples.planning.plan_and_execute.run_worker
-
-    # Then run this script:
-    python -m langgraph_samples.planning.plan_and_execute.run_workflow
-"""
+"""Execute the Plan-and-Execute workflow."""
 
 import asyncio
 
diff --git a/langgraph_samples/rag/agentic_rag/run_workflow.py b/langgraph_samples/rag/agentic_rag/run_workflow.py
index 01989ed5..6fd32285 100644
--- a/langgraph_samples/rag/agentic_rag/run_workflow.py
+++ b/langgraph_samples/rag/agentic_rag/run_workflow.py
@@ -1,12 +1,4 @@
-"""Execute the Agentic RAG workflow.
-
-Usage:
-    # First, in another terminal, start the worker:
-    python -m langgraph_samples.rag.agentic_rag.run_worker
-
-    # Then run this script:
-    python -m langgraph_samples.rag.agentic_rag.run_workflow
-"""
+"""Execute the Agentic RAG workflow."""
 
 import asyncio
 
diff --git a/langgraph_samples/rag/deep_research/README.md b/langgraph_samples/rag/deep_research/README.md
index 072fa334..c5b21df2 100644
--- a/langgraph_samples/rag/deep_research/README.md
+++ b/langgraph_samples/rag/deep_research/README.md
@@ -51,12 +51,12 @@ Each search runs as a separate Temporal activity, enabling parallel execution.
 2. In one terminal, start the worker:
    ```bash
    export OPENAI_API_KEY=your-key-here
-   python -m langgraph_samples.rag.deep_research.run_worker
+   uv run langgraph_samples/rag/deep_research/run_worker.py
    ```
 
 3. In another terminal, run the workflow:
    ```bash
-   python -m langgraph_samples.rag.deep_research.run_workflow
+   uv run langgraph_samples/rag/deep_research/run_workflow.py
    ```
 
 ## Sample Output
diff --git a/langgraph_samples/rag/deep_research/run_workflow.py b/langgraph_samples/rag/deep_research/run_workflow.py
index 43248196..6a6bdb88 100644
--- a/langgraph_samples/rag/deep_research/run_workflow.py
+++ b/langgraph_samples/rag/deep_research/run_workflow.py
@@ -1,12 +1,4 @@
-"""Execute the Deep Research workflow.
-
-Usage:
-    # First, in another terminal, start the worker:
-    python -m langgraph_samples.rag.deep_research.run_worker
-
-    # Then run this script:
-    python -m langgraph_samples.rag.deep_research.run_workflow
-"""
+"""Execute the Deep Research workflow."""
 
 import asyncio
 

From 3e6df8f128659236498b9dc31adcf8b68c430841 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Mon, 29 Dec 2025 12:42:20 -0800
Subject: [PATCH 21/24] LangGraph: Deep research sample uses real DuckDuckGo
 web search

- Replace mock search results with real DuckDuckGo web search
- Add langchain-community and duckduckgo-search dependencies
- Update README to reflect real search functionality
- Add error handling for failed searches
---
 langgraph_samples/rag/deep_research/README.md |  25 +-
 langgraph_samples/rag/deep_research/graph.py  |  65 +-
 pyproject.toml                                |   3 +
 uv.lock                                       | 559 +++++++++++++++++-
 4 files changed, 589 insertions(+), 63 deletions(-)

diff --git a/langgraph_samples/rag/deep_research/README.md b/langgraph_samples/rag/deep_research/README.md
index c5b21df2..523e53f9 100644
--- a/langgraph_samples/rag/deep_research/README.md
+++ b/langgraph_samples/rag/deep_research/README.md
@@ -1,21 +1,21 @@
 # Deep Research Agent
 
-A multi-step research agent that performs iterative research to produce comprehensive reports. This sample demonstrates how Temporal's durable execution enables long-running research workflows.
+A multi-step research agent that performs iterative research using real web search to produce comprehensive reports. This sample demonstrates how Temporal's durable execution enables long-running research workflows with LangGraph's parallel execution.
 
 ## Overview
 
 The Deep Research Agent:
-1. **Plans research** - Analyzes the topic and generates targeted search queries
-2. **Parallel search** - Executes multiple searches concurrently using LangGraph's Send API
+1. **Plans research** - Analyzes the topic and generates targeted search queries using OpenAI
+2. **Parallel search** - Executes multiple DuckDuckGo web searches concurrently using LangGraph's Send API
 3. **Evaluates results** - Grades search results for relevance and coverage
 4. **Iterates** - Continues researching if more information is needed
-5. **Synthesizes** - Produces a comprehensive research report
+5. **Synthesizes** - Produces a comprehensive research report using OpenAI
 
 ## Why Temporal?
 
 Research workflows are ideal for Temporal because:
 - **Long-running**: Research can take minutes to hours
-- **Parallel execution**: Multiple searches run as separate activities
+- **Parallel execution**: Multiple web searches run as separate activities
 - **Fault tolerance**: If a search fails, only that activity retries
 - **Visibility**: Progress is tracked in Temporal's UI
 - **Resumable**: Interrupted research continues where it left off
@@ -112,17 +112,16 @@ search_results: Annotated[list[SearchResult], lambda x, y: x + y]
 
 ## Customization
 
-### Using Real Search APIs
+### Using Alternative Search APIs
 
-Replace the mock search with real APIs:
+The sample uses DuckDuckGo by default. To use other search providers:
 
 ```python
-from langchain_community.tools import DuckDuckGoSearchRun
+# Tavily (requires TAVILY_API_KEY)
+from langchain_community.tools import TavilySearchResults
+search_tool = TavilySearchResults()
 
-def execute_search(state: SearchTaskState) -> dict[str, Any]:
-    search = DuckDuckGoSearchRun()
-    results = search.run(state["query"])
-    return {"search_results": [{"query": state["query"], "results": results, ...}]}
+# Or use any LangChain-compatible search tool
 ```
 
 ### Adjusting Research Depth
@@ -139,7 +138,7 @@ result = await client.execute_workflow(
 
 ## Next Steps
 
-- Add real search API integration (DuckDuckGo, Tavily, Exa)
+- Integrate additional search providers (Tavily, Exa, Google)
 - Implement continue-as-new for very long research sessions
 - Add human review checkpoints for important findings
 - Store research artifacts in Temporal's data store
diff --git a/langgraph_samples/rag/deep_research/graph.py b/langgraph_samples/rag/deep_research/graph.py
index b53b6c7d..8db53762 100644
--- a/langgraph_samples/rag/deep_research/graph.py
+++ b/langgraph_samples/rag/deep_research/graph.py
@@ -3,24 +3,29 @@
 This module implements a multi-step research agent that performs iterative
 research to produce comprehensive reports. It demonstrates:
 - Parallel search execution using LangGraph's Send API
+- Real web search using DuckDuckGo
 - Result evaluation and iteration
 - Long-running research workflows (showcasing Temporal's durability)
 - Report synthesis from multiple sources
 
 The research flow:
 1. plan_research - Analyze the topic and generate search queries
-2. search (parallel) - Execute multiple searches concurrently
+2. search (parallel) - Execute multiple web searches concurrently
 3. evaluate_results - Grade search results for relevance
 4. decide_next - Either continue researching or synthesize
 5. synthesize - Produce final research report
 
 Note: This module is only imported by the worker (not by the workflow).
 LangGraph cannot be imported in the workflow sandbox.
+
+Prerequisites:
+    - OPENAI_API_KEY environment variable set
 """
 
 import os
 from typing import Annotated, Any, Literal
 
+from langchain_community.tools import DuckDuckGoSearchRun
 from langchain_core.messages import BaseMessage, HumanMessage
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
@@ -83,33 +88,8 @@ class SearchTaskState(TypedDict):
     purpose: str
 
 
-# Simulated web search results (in production, use a real search API)
-MOCK_SEARCH_RESULTS = {
-    "langgraph": """LangGraph is a library for building stateful, multi-actor applications
-    with LLMs. It provides support for cycles, branches, and state persistence. Key features:
-    - StateGraph for defining application flow
-    - Built-in persistence and checkpointing
-    - Human-in-the-loop support with interrupts
-    - Streaming support for real-time updates""",
-    "temporal": """Temporal is a durable execution platform. Key concepts:
-    - Workflows: Long-running processes that survive failures
-    - Activities: Units of work with automatic retries
-    - Signals: External events sent to workflows
-    - Queries: Read-only state inspection""",
-    "durable": """Durable execution ensures code runs to completion despite failures.
-    Benefits include: automatic retry of failed operations, state preservation
-    across restarts, and reliable execution of long-running processes.""",
-    "agent": """AI agents are autonomous systems that use LLMs to decide actions.
-    Common patterns include ReAct (reasoning + acting), plan-and-execute,
-    and multi-agent collaboration. Tools enable agents to interact with
-    external systems and data sources.""",
-    "rag": """RAG (Retrieval Augmented Generation) enhances LLM responses with
-    external knowledge. Agentic RAG adds decision-making about when to retrieve,
-    document grading, and query rewriting for better results.""",
-    "research": """Research agents perform multi-step information gathering.
-    They plan queries, execute searches, evaluate results, and synthesize
-    findings into comprehensive reports.""",
-}
+# Initialize DuckDuckGo search tool
+search_tool = DuckDuckGoSearchRun()
 
 
 def build_deep_research_graph() -> Any:
@@ -172,32 +152,29 @@ def plan_research(state: ResearchState) -> dict[str, Any]:
         }
 
     def execute_search(state: SearchTaskState) -> dict[str, Any]:
-        """Execute a single search query.
+        """Execute a single web search query using DuckDuckGo.
 
-        In production, this would call a real search API.
-        Here we use mock results for demonstration.
+        Performs real web search and returns the results.
+        Each search runs as a separate Temporal activity for durability.
         """
-        query = state["query"].lower()
+        query = state["query"]
         purpose = state["purpose"]
 
-        # Find matching mock results
-        results_parts = []
-        for keyword, content in MOCK_SEARCH_RESULTS.items():
-            if keyword in query:
-                results_parts.append(content)
-
-        if not results_parts:
-            results = "No specific results found. The topic may require more specialized research."
-        else:
-            results = "\n\n".join(results_parts)
+        try:
+            # Execute real web search
+            results = search_tool.invoke(query)
+            has_results = bool(results and len(results.strip()) > 0)
+        except Exception as e:
+            results = f"Search failed: {e}"
+            has_results = False
 
         return {
             "search_results": [
                 {
-                    "query": state["query"],
+                    "query": query,
                     "purpose": purpose,
                     "results": results,
-                    "relevant": len(results_parts) > 0,
+                    "relevant": has_results,
                 }
             ]
         }
diff --git a/pyproject.toml b/pyproject.toml
index f08b3325..fed97151 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -64,6 +64,9 @@ langgraph = [
     "langchain>=0.3.0",
     "langchain-core>=0.1.0",
     "langchain-openai>=0.2.0",
+    "langchain-community>=0.3.0",
+    "duckduckgo-search>=6.0.0",
+    "ddgs>=7.0.0",
     "numpy>=1.26.0,<3",
 ]
 
diff --git a/uv.lock b/uv.lock
index fc0774d5..5e75e522 100644
--- a/uv.lock
+++ b/uv.lock
@@ -186,6 +186,84 @@ wheels = [
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 ]
 
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+version = "1.2.0"
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+    { url = "https://files.pythonhosted.org/packages/54/0f/d15398b98b65c2bce288d5ee3f7d0a81f77ab89d9456994d5c7cc8b2a9db/sqlalchemy-2.0.45-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:9a62b446b7d86a3909abbcd1cd3cc550a832f99c2bc37c5b22e1925438b9367b", size = 3475142 },
+    { url = "https://files.pythonhosted.org/packages/bf/e1/3ccb13c643399d22289c6a9786c1a91e3dcbb68bce4beb44926ac2c557bf/sqlalchemy-2.0.45-py3-none-any.whl", hash = "sha256:5225a288e4c8cc2308dbdd874edad6e7d0fd38eac1e9e5f23503425c8eee20d0", size = 1936672 },
+]
+
 [[package]]
 name = "sse-starlette"
 version = "2.4.1"
@@ -2710,7 +3237,7 @@ bedrock = [
 ]
 cloud-export-to-parquet = [
     { name = "boto3" },
-    { name = "numpy", marker = "python_full_version < '3.13'" },
+    { name = "numpy", version = "1.26.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13'" },
     { name = "pandas", marker = "python_full_version < '4'" },
     { name = "pyarrow" },
 ]
@@ -2747,12 +3274,16 @@ langchain = [
     { name = "uvicorn", extra = ["standard"] },
 ]
 langgraph = [
+    { name = "ddgs" },
+    { name = "duckduckgo-search" },
     { name = "langchain" },
+    { name = "langchain-community" },
     { name = "langchain-core" },
     { name = "langchain-openai" },
     { name = "langgraph" },
     { name = "langgraph-supervisor" },
-    { name = "numpy" },
+    { name = "numpy", version = "1.26.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13'" },
+    { name = "numpy", version = "2.4.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" },
 ]
 nexus = [
     { name = "nexus-rpc" },
@@ -2819,7 +3350,10 @@ langchain = [
     { name = "uvicorn", extras = ["standard"], specifier = ">=0.24.0" },
 ]
 langgraph = [
+    { name = "ddgs", specifier = ">=7.0.0" },
+    { name = "duckduckgo-search", specifier = ">=6.0.0" },
     { name = "langchain", specifier = ">=0.3.0" },
+    { name = "langchain-community", specifier = ">=0.3.0" },
     { name = "langchain-core", specifier = ">=0.1.0" },
     { name = "langchain-openai", specifier = ">=0.2.0" },
     { name = "langgraph", specifier = ">=0.2.0" },
@@ -3062,6 +3596,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b5/00/d631e67a838026495268c2f6884f3711a15a9a2a96cd244fdaea53b823fb/typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76", size = 43906 },
 ]
 
+[[package]]
+name = "typing-inspect"
+version = "0.9.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "mypy-extensions" },
+    { name = "typing-extensions" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/dc/74/1789779d91f1961fa9438e9a8710cdae6bd138c80d7303996933d117264a/typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78", size = 13825 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/65/f3/107a22063bf27bdccf2024833d3445f4eea42b2e598abfbd46f6a63b6cb0/typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f", size = 8827 },
+]
+
 [[package]]
 name = "typing-inspection"
 version = "0.4.2"

From d772f0a5fd5dbab70e9f54a81b3432b9c8de4a33 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Mon, 29 Dec 2025 15:45:48 -0800
Subject: [PATCH 22/24] LangGraph samples: Flatten directory structure to
 single level

Move all samples from nested category directories to top level:
- basic/hello_world -> hello_world
- basic/react_agent -> react_agent
- human_in_loop/approval_workflow -> approval_workflow
- multi_agent/supervisor -> supervisor
- rag/agentic_rag -> agentic_rag
- rag/deep_research -> deep_research
- planning/plan_and_execute -> plan_and_execute
- advanced/reflection -> reflection

Update all import paths accordingly and refresh README.md with
a table listing all samples.
---
 langgraph_samples/README.md                   |  20 +-
 langgraph_samples/SAMPLES_PROPOSAL.md         | 349 ------------------
 langgraph_samples/advanced/README.md          |  29 --
 langgraph_samples/advanced/__init__.py        |   1 -
 .../{rag => }/agentic_rag/README.md           |   0
 .../{rag => }/agentic_rag/__init__.py         |   0
 .../{rag => }/agentic_rag/graph.py            |   0
 .../{rag => }/agentic_rag/run_worker.py       |   4 +-
 .../{rag => }/agentic_rag/run_workflow.py     |   2 +-
 .../{rag => }/agentic_rag/workflow.py         |   0
 .../approval_workflow/README.md               |   0
 .../approval_workflow/__init__.py             |   0
 .../approval_workflow/activities.py           |   8 +-
 .../approval_workflow/graph.py                |   0
 .../approval_workflow/run_respond.py          |   2 +-
 .../approval_workflow/run_worker.py           |   6 +-
 .../approval_workflow/run_workflow.py         |   2 +-
 .../approval_workflow/workflow.py             |   2 +-
 langgraph_samples/basic/README.md             |  18 -
 langgraph_samples/basic/__init__.py           |   1 -
 .../{rag => }/deep_research/README.md         |   0
 .../{rag => }/deep_research/__init__.py       |   0
 .../{rag => }/deep_research/graph.py          |   0
 .../{rag => }/deep_research/run_worker.py     |   4 +-
 .../{rag => }/deep_research/run_workflow.py   |   2 +-
 .../{rag => }/deep_research/workflow.py       |   0
 .../{basic => }/hello_world/README.md         |   0
 .../{basic => }/hello_world/__init__.py       |   0
 .../{basic => }/hello_world/graph.py          |   0
 .../{basic => }/hello_world/run_worker.py     |   4 +-
 .../{basic => }/hello_world/run_workflow.py   |   2 +-
 .../{basic => }/hello_world/workflow.py       |   0
 langgraph_samples/human_in_loop/README.md     |  21 --
 langgraph_samples/human_in_loop/__init__.py   |   1 -
 langgraph_samples/multi_agent/README.md       |  34 --
 langgraph_samples/multi_agent/__init__.py     |   1 -
 .../{planning => }/plan_and_execute/README.md |   0
 .../plan_and_execute/__init__.py              |   0
 .../{planning => }/plan_and_execute/graph.py  |   0
 .../plan_and_execute/run_worker.py            |   4 +-
 .../plan_and_execute/run_workflow.py          |   2 +-
 .../plan_and_execute/workflow.py              |   0
 langgraph_samples/planning/README.md          |  30 --
 langgraph_samples/planning/__init__.py        |   1 -
 langgraph_samples/rag/README.md               |  32 --
 langgraph_samples/rag/__init__.py             |   1 -
 .../{basic => }/react_agent/README.md         |   0
 .../{basic => }/react_agent/__init__.py       |   0
 .../{basic => }/react_agent/graph.py          |   2 +-
 .../{basic => }/react_agent/run_worker.py     |   4 +-
 .../{basic => }/react_agent/run_workflow.py   |   2 +-
 .../{basic => }/react_agent/tools.py          |   0
 .../{basic => }/react_agent/workflow.py       |   0
 .../{advanced => }/reflection/README.md       |   0
 .../{advanced => }/reflection/__init__.py     |   0
 .../{advanced => }/reflection/graph.py        |   0
 .../{advanced => }/reflection/run_worker.py   |   4 +-
 .../{advanced => }/reflection/run_workflow.py |   2 +-
 .../{advanced => }/reflection/workflow.py     |   0
 .../{multi_agent => }/supervisor/README.md    |   0
 .../{multi_agent => }/supervisor/__init__.py  |   0
 .../{multi_agent => }/supervisor/graph.py     |   0
 .../supervisor/run_worker.py                  |   4 +-
 .../supervisor/run_workflow.py                |   2 +-
 .../{multi_agent => }/supervisor/workflow.py  |   0
 65 files changed, 44 insertions(+), 559 deletions(-)
 delete mode 100644 langgraph_samples/SAMPLES_PROPOSAL.md
 delete mode 100644 langgraph_samples/advanced/README.md
 delete mode 100644 langgraph_samples/advanced/__init__.py
 rename langgraph_samples/{rag => }/agentic_rag/README.md (100%)
 rename langgraph_samples/{rag => }/agentic_rag/__init__.py (100%)
 rename langgraph_samples/{rag => }/agentic_rag/graph.py (100%)
 rename langgraph_samples/{rag => }/agentic_rag/run_worker.py (91%)
 rename langgraph_samples/{rag => }/agentic_rag/run_workflow.py (92%)
 rename langgraph_samples/{rag => }/agentic_rag/workflow.py (100%)
 rename langgraph_samples/{human_in_loop => }/approval_workflow/README.md (100%)
 rename langgraph_samples/{human_in_loop => }/approval_workflow/__init__.py (100%)
 rename langgraph_samples/{human_in_loop => }/approval_workflow/activities.py (65%)
 rename langgraph_samples/{human_in_loop => }/approval_workflow/graph.py (100%)
 rename langgraph_samples/{human_in_loop => }/approval_workflow/run_respond.py (96%)
 rename langgraph_samples/{human_in_loop => }/approval_workflow/run_worker.py (81%)
 rename langgraph_samples/{human_in_loop => }/approval_workflow/run_workflow.py (95%)
 rename langgraph_samples/{human_in_loop => }/approval_workflow/workflow.py (98%)
 delete mode 100644 langgraph_samples/basic/README.md
 delete mode 100644 langgraph_samples/basic/__init__.py
 rename langgraph_samples/{rag => }/deep_research/README.md (100%)
 rename langgraph_samples/{rag => }/deep_research/__init__.py (100%)
 rename langgraph_samples/{rag => }/deep_research/graph.py (100%)
 rename langgraph_samples/{rag => }/deep_research/run_worker.py (88%)
 rename langgraph_samples/{rag => }/deep_research/run_workflow.py (93%)
 rename langgraph_samples/{rag => }/deep_research/workflow.py (100%)
 rename langgraph_samples/{basic => }/hello_world/README.md (100%)
 rename langgraph_samples/{basic => }/hello_world/__init__.py (100%)
 rename langgraph_samples/{basic => }/hello_world/graph.py (100%)
 rename langgraph_samples/{basic => }/hello_world/run_worker.py (88%)
 rename langgraph_samples/{basic => }/hello_world/run_workflow.py (90%)
 rename langgraph_samples/{basic => }/hello_world/workflow.py (100%)
 delete mode 100644 langgraph_samples/human_in_loop/README.md
 delete mode 100644 langgraph_samples/human_in_loop/__init__.py
 delete mode 100644 langgraph_samples/multi_agent/README.md
 delete mode 100644 langgraph_samples/multi_agent/__init__.py
 rename langgraph_samples/{planning => }/plan_and_execute/README.md (100%)
 rename langgraph_samples/{planning => }/plan_and_execute/__init__.py (100%)
 rename langgraph_samples/{planning => }/plan_and_execute/graph.py (100%)
 rename langgraph_samples/{planning => }/plan_and_execute/run_worker.py (91%)
 rename langgraph_samples/{planning => }/plan_and_execute/run_workflow.py (95%)
 rename langgraph_samples/{planning => }/plan_and_execute/workflow.py (100%)
 delete mode 100644 langgraph_samples/planning/README.md
 delete mode 100644 langgraph_samples/planning/__init__.py
 delete mode 100644 langgraph_samples/rag/README.md
 delete mode 100644 langgraph_samples/rag/__init__.py
 rename langgraph_samples/{basic => }/react_agent/README.md (100%)
 rename langgraph_samples/{basic => }/react_agent/__init__.py (100%)
 rename langgraph_samples/{basic => }/react_agent/graph.py (95%)
 rename langgraph_samples/{basic => }/react_agent/run_worker.py (89%)
 rename langgraph_samples/{basic => }/react_agent/run_workflow.py (91%)
 rename langgraph_samples/{basic => }/react_agent/tools.py (100%)
 rename langgraph_samples/{basic => }/react_agent/workflow.py (100%)
 rename langgraph_samples/{advanced => }/reflection/README.md (100%)
 rename langgraph_samples/{advanced => }/reflection/__init__.py (100%)
 rename langgraph_samples/{advanced => }/reflection/graph.py (100%)
 rename langgraph_samples/{advanced => }/reflection/run_worker.py (88%)
 rename langgraph_samples/{advanced => }/reflection/run_workflow.py (95%)
 rename langgraph_samples/{advanced => }/reflection/workflow.py (100%)
 rename langgraph_samples/{multi_agent => }/supervisor/README.md (100%)
 rename langgraph_samples/{multi_agent => }/supervisor/__init__.py (100%)
 rename langgraph_samples/{multi_agent => }/supervisor/graph.py (100%)
 rename langgraph_samples/{multi_agent => }/supervisor/run_worker.py (90%)
 rename langgraph_samples/{multi_agent => }/supervisor/run_workflow.py (95%)
 rename langgraph_samples/{multi_agent => }/supervisor/workflow.py (100%)

diff --git a/langgraph_samples/README.md b/langgraph_samples/README.md
index e2309293..c2a2ea2f 100644
--- a/langgraph_samples/README.md
+++ b/langgraph_samples/README.md
@@ -49,11 +49,15 @@ Since the LangGraph integration is currently in a branch, you need to install fr
 
 ## Examples
 
-Each directory contains complete examples with their own README for detailed instructions:
-
-- **[Basic Examples](./basic/README.md)** - Simple examples including a hello world agent demonstrating basic plugin setup and graph registration.
-- **[Human-in-the-Loop](./human_in_loop/README.md)** - Examples demonstrating interrupt/resume workflows with human approval.
-- **[Multi-Agent](./multi_agent/README.md)** - Multi-agent patterns including supervisor coordination.
-- **[RAG (Retrieval Augmented Generation)](./rag/README.md)** - Intelligent retrieval with document grading, query rewriting, and deep research.
-- **[Planning](./planning/README.md)** - Plan-and-execute patterns with structured step execution.
-- **[Advanced Patterns](./advanced/README.md)** - Advanced techniques including reflection and self-improvement.
+Each directory contains a complete example with its own README for detailed instructions:
+
+| Sample | Description |
+|--------|-------------|
+| [hello_world](./hello_world/) | Simple starter example demonstrating basic plugin setup and graph registration |
+| [react_agent](./react_agent/) | ReAct agent pattern with tool calling and multi-step reasoning |
+| [approval_workflow](./approval_workflow/) | Human-in-the-loop with interrupt/resume for approval workflows |
+| [supervisor](./supervisor/) | Multi-agent supervisor pattern coordinating specialized agents |
+| [agentic_rag](./agentic_rag/) | Retrieval-augmented generation with document grading and query rewriting |
+| [deep_research](./deep_research/) | Multi-step research with web search and iterative refinement |
+| [plan_and_execute](./plan_and_execute/) | Plan-and-execute pattern with structured step execution |
+| [reflection](./reflection/) | Self-reflection pattern for iterative improvement |
diff --git a/langgraph_samples/SAMPLES_PROPOSAL.md b/langgraph_samples/SAMPLES_PROPOSAL.md
deleted file mode 100644
index 648bb18d..00000000
--- a/langgraph_samples/SAMPLES_PROPOSAL.md
+++ /dev/null
@@ -1,349 +0,0 @@
-# Temporal LangGraph Samples Proposal
-
-This document proposes sample applications demonstrating the [Temporal LangGraph integration](https://github.com/temporalio/sdk-python/blob/main/temporalio/contrib/langgraph/README.md) - combining LangGraph's agent framework with Temporal's durable execution.
-
-## Design Principles
-
-Each sample should demonstrate:
-1. **Durability** - Agents that survive failures and can resume from checkpoints
-2. **Production Readiness** - Proper timeout/retry configuration, error handling
-3. **Temporal Features** - Leveraging signals, queries, child workflows where appropriate
-
-Samples are organized by complexity and use case, inspired by [OpenAI Agents samples](../openai_agents/README.md) structure.
-
----
-
-## Proposed Samples
-
-### 1. Basic Examples (`basic/`)
-
-#### 1.1 Hello World Agent
-**Description:** Minimal LangGraph agent with Temporal integration. Single-node graph that processes a query.
-
-**Demonstrates:**
-- Basic plugin setup and graph registration
-- Simple workflow invocation
-- Activity-based node execution
-
-**Original Reference:** N/A (custom introductory example)
-
----
-
-#### 1.2 ReAct Agent with Tools
-**Description:** Classic ReAct (Reasoning + Acting) agent that can use tools to answer questions. Implements the think-act-observe loop.
-
-**Demonstrates:**
-- LangChain's `create_agent` with Temporal integration
-- Durable execution where each node runs as a Temporal activity
-- Automatic retries and crash recovery at the node level
-- Cyclic graph execution (agent → tools → agent → ...)
-
-**Original Reference:** [LangGraph ReAct Agent Template](https://github.com/langchain-ai/react-agent)
-
----
-
-### 2. Human-in-the-Loop (`human_in_loop/`)
-
-#### 2.1 Approval Workflow
-**Description:** Agent that pauses for human approval before taking actions. Uses LangGraph's `interrupt()` with Temporal signals.
-
-**Demonstrates:**
-- `interrupt()` for pausing execution
-- Temporal signals for receiving human input
-- Workflow queries for checking pending approvals
-- Timeout handling for approval deadlines
-
-**Original Reference:** [LangGraph Human-in-the-Loop Guide](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/)
-
----
-
-#### 2.2 Document Review Agent
-**Description:** Agent that drafts documents and requests human review/edits before finalizing. Supports iterative refinement cycles.
-
-**Demonstrates:**
-- Multi-step interrupt/resume cycles
-- State preservation across interrupts
-- Combining AI drafting with human editing
-
-**Original Reference:** [LangGraph Human Feedback Discussion](https://github.com/langchain-ai/langgraph/discussions/1137)
-
----
-
-### 3. Multi-Agent Systems (`multi_agent/`)
-
-#### 3.1 Supervisor Agent
-**Description:** Supervisor that coordinates specialized worker agents (researcher, writer, critic). The supervisor routes tasks to appropriate agents and aggregates results.
-
-**Demonstrates:**
-- Multi-agent orchestration patterns
-- Supervisor decision-making logic
-- Agent handoffs and communication
-- Parallel agent execution with `Send`
-
-**Original Reference:** [LangGraph Supervisor Library](https://github.com/langchain-ai/langgraph-supervisor-py) | [Multi-Agent Blog Post](https://blog.langchain.com/langgraph-multi-agent-workflows/)
-
----
-
-#### 3.2 Hierarchical Agent Teams
-**Description:** Two-level hierarchy with a top supervisor managing team supervisors (research team + writing team), each with their own worker agents.
-
-**Demonstrates:**
-- Child workflows for team isolation
-- Hierarchical agent coordination
-- Complex routing logic
-- Cross-team communication
-
-**Original Reference:** [LangGraph Hierarchical Agent Teams Tutorial](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/)
-
----
-
-### 4. RAG and Research (`rag/`)
-
-#### 4.1 Agentic RAG
-**Description:** RAG agent that decides when to retrieve documents vs. respond directly. Includes document relevance grading and query rewriting.
-
-**Demonstrates:**
-- Conditional retrieval decisions
-- Document grading nodes
-- Query rewriting for better retrieval
-- Combining retrieval with generation
-
-**Original Reference:** [LangGraph Agentic RAG Tutorial](https://github.com/langchain-ai/langgraph/blob/main/examples/rag/langgraph_agentic_rag.ipynb) | [Agentic RAG Docs](https://docs.langchain.com/oss/python/langgraph/agentic-rag)
-
----
-
-#### 4.2 Corrective RAG
-**Description:** Self-correcting RAG that validates retrieved documents and falls back to web search when needed. Includes hallucination detection.
-
-**Demonstrates:**
-- Document relevance validation
-- Fallback to web search
-- Self-correction loops
-- Multiple retrieval strategies
-
-**Original Reference:** [Corrective RAG Guide](https://www.analyticsvidhya.com/blog/2024/07/building-agentic-rag-systems-with-langgraph/)
-
----
-
-#### 4.3 Deep Research Agent
-**Description:** Multi-step research agent that performs iterative web searches, evaluates results, and synthesizes findings into a comprehensive report.
-
-**Demonstrates:**
-- Long-running research workflows (minutes to hours)
-- Parallel search execution
-- Result evaluation and iteration
-- Continue-as-new for large histories
-- Report generation
-
-**Original Reference:** [LangGraph Deep Research Agent](https://towardsdatascience.com/langgraph-101-lets-build-a-deep-research-agent/) | [Exa Research System](https://blog.langchain.com/exa/)
-
----
-
-### 5. Customer Service (`customer_service/`)
-
-#### 5.1 Customer Support Agent
-**Description:** Interactive customer service agent that handles inquiries, accesses knowledge bases, and escalates to humans when needed.
-
-**Demonstrates:**
-- Conversational state management
-- Knowledge base integration
-- Escalation workflows with `interrupt()`
-- Session continuity across interactions
-
-**Original Reference:** [LangGraph Customer Support Tutorial](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/tutorials/customer-support/customer-support.ipynb)
-
----
-
-#### 5.2 Email Triage Agent
-**Description:** Agent that processes incoming emails, categorizes them, drafts responses using RAG for policy questions, and routes to appropriate handlers.
-
-**Demonstrates:**
-- Email classification
-- RAG for policy/knowledge lookup
-- Conditional routing based on email type
-- Draft review before sending
-
-**Original Reference:** [Agentic RAG Customer Support](https://blog.lancedb.com/agentic-rag-using-langgraph-building-a-simple-customer-support-autonomous-agent/)
-
----
-
-### 6. Planning and Execution (`planning/`)
-
-#### 6.1 Plan-and-Execute Agent
-**Description:** Agent that first creates a plan, then executes steps sequentially, with the ability to replan based on results.
-
-**Demonstrates:**
-- Separation of planning and execution
-- Dynamic replanning
-- Step-by-step execution with checkpointing
-- Plan validation
-
-**Original Reference:** [LangGraph Plan-and-Execute Tutorial](https://github.com/langchain-ai/langgraph/blob/main/examples/plan-and-execute/plan-and-execute.ipynb)
-
----
-
-#### 6.2 Travel Planning Agent
-**Description:** Agent that plans trips by coordinating flight searches, hotel bookings, and itinerary creation with user preferences.
-
-**Demonstrates:**
-- Multi-tool coordination
-- User preference handling
-- Complex planning with constraints
-- Real API integration patterns
-
-**Original Reference:** [LangGraph Travel Planning Tutorial](https://www.projectpro.io/article/langgraph/1109)
-
----
-
-### 7. Code and Development (`code/`)
-
-#### 7.1 Code Assistant
-**Description:** Agent that helps with code-related tasks: explaining code, generating code, debugging, and answering programming questions.
-
-**Demonstrates:**
-- Code-aware prompting
-- Multi-turn coding conversations
-- Tool use for code execution/testing
-- Context management for large codebases
-
-**Original Reference:** [LangGraph Code Assistant Tutorial](https://github.com/langchain-ai/langgraph/blob/main/examples/code_assistant/langgraph_code_assistant.ipynb)
-
----
-
-### 8. Advanced Patterns (`advanced/`)
-
-#### 8.1 Reflection Agent
-**Description:** Agent that generates output, reflects on it, and iteratively improves until quality criteria are met.
-
-**Demonstrates:**
-- Self-critique and improvement loops
-- Quality evaluation nodes
-- Iterative refinement patterns
-- Loop termination conditions
-
-**Original Reference:** [LangGraph Reflection Tutorial](https://github.com/langchain-ai/langgraph/tree/main/docs/docs/tutorials/reflection)
-
----
-
-#### 8.2 Tree of Thoughts
-**Description:** Agent that explores multiple reasoning paths in parallel and selects the best solution.
-
-**Demonstrates:**
-- Parallel exploration with `Send`
-- Solution evaluation and ranking
-- Branch pruning logic
-- Complex graph patterns
-
-**Original Reference:** [LangGraph Tree of Thoughts Tutorial](https://github.com/langchain-ai/langgraph/tree/main/docs/docs/tutorials/tot)
-
----
-
-#### 8.3 Long-Running Workflow with Checkpointing
-**Description:** Demonstrates continue-as-new for workflows that may run for extended periods and generate large event histories.
-
-**Demonstrates:**
-- `should_continue` callback for checkpointing
-- State serialization and restoration
-- Continue-as-new pattern
-- Store API for cross-workflow data
-
-**Original Reference:** N/A (Temporal-specific pattern)
-
----
-
-## Implementation Priority
-
-### Phase 1 - Core Examples (Recommended First)
-1. **Basic: Hello World Agent** - Entry point for new users
-2. **Basic: ReAct Agent with Tools** - Most common agent pattern
-3. **Human-in-the-Loop: Approval Workflow** - Key differentiator for Temporal
-4. **RAG: Agentic RAG** - Popular production use case
-
-### Phase 2 - Multi-Agent and Research
-5. **Multi-Agent: Supervisor Agent** - Popular advanced pattern
-6. **RAG: Deep Research Agent** - Showcases long-running durability
-7. **Customer Service: Support Agent** - Business-relevant example
-
-### Phase 3 - Advanced Patterns
-8. **Planning: Plan-and-Execute** - Structured agent execution
-9. **Advanced: Reflection Agent** - Self-improvement pattern
-10. **Multi-Agent: Hierarchical Teams** - Complex orchestration
-
----
-
-## Directory Structure
-
-```
-langgraph/
-├── README.md                    # Overview and getting started
-├── __init__.py
-├── basic/
-│   ├── README.md
-│   ├── hello_world/
-│   │   ├── README.md
-│   │   ├── run_worker.py
-│   │   └── run_workflow.py
-│   └── react_agent/
-│       ├── README.md
-│       ├── agent.py
-│       ├── tools.py
-│       ├── run_worker.py
-│       └── run_workflow.py
-├── human_in_loop/
-│   ├── README.md
-│   ├── approval_workflow/
-│   └── document_review/
-├── multi_agent/
-│   ├── README.md
-│   ├── supervisor/
-│   └── hierarchical_teams/
-├── rag/
-│   ├── README.md
-│   ├── agentic_rag/
-│   ├── corrective_rag/
-│   └── deep_research/
-├── customer_service/
-│   ├── README.md
-│   ├── support_agent/
-│   └── email_triage/
-├── planning/
-│   ├── README.md
-│   ├── plan_and_execute/
-│   └── travel_planner/
-├── code/
-│   ├── README.md
-│   └── code_assistant/
-└── advanced/
-    ├── README.md
-    ├── reflection/
-    ├── tree_of_thoughts/
-    └── long_running_workflow/
-```
-
----
-
-## References
-
-### Official LangGraph Resources
-- [LangGraph Documentation](https://docs.langchain.com/oss/python/langgraph/overview)
-- [LangGraph GitHub Repository](https://github.com/langchain-ai/langgraph)
-- [LangGraph Tutorials](https://github.com/langchain-ai/langgraph/tree/main/docs/docs/tutorials)
-- [LangChain Academy - Intro to LangGraph](https://academy.langchain.com/courses/intro-to-langgraph)
-
-### Multi-Agent Resources
-- [LangGraph Multi-Agent Concepts](https://github.com/langchain-ai/langgraph/blob/main/docs/docs/concepts/multi_agent.md)
-- [LangGraph Supervisor Library](https://github.com/langchain-ai/langgraph-supervisor-py)
-- [Hierarchical Agent Teams Tutorial](https://langchain-ai.github.io/langgraph/tutorials/multi_agent/hierarchical_agent_teams/)
-
-### RAG and Research
-- [Agentic RAG Documentation](https://docs.langchain.com/oss/python/langgraph/agentic-rag)
-- [RAG Research Agent Template](https://github.com/langchain-ai/rag-research-agent-template)
-- [Deep Research Agent Article](https://towardsdatascience.com/langgraph-101-lets-build-a-deep-research-agent/)
-
-### Human-in-the-Loop
-- [Human-in-the-Loop How-To](https://langchain-ai.github.io/langgraph/how-tos/human_in_the_loop/)
-- [Human Feedback Discussion](https://github.com/langchain-ai/langgraph/discussions/1137)
-
-### Courses and Tutorials
-- [DeepLearning.AI - AI Agents in LangGraph](https://www.deeplearning.ai/short-courses/ai-agents-in-langgraph/)
-- [Real Python - LangGraph Tutorial](https://realpython.com/langgraph-python/)
diff --git a/langgraph_samples/advanced/README.md b/langgraph_samples/advanced/README.md
deleted file mode 100644
index dc27ce2c..00000000
--- a/langgraph_samples/advanced/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Advanced Pattern Samples
-
-This directory contains samples demonstrating advanced LangGraph patterns with Temporal integration.
-
-## Available Samples
-
-### [Reflection Agent](./reflection/README.md)
-
-An agent that generates content, critiques it, and iteratively improves until quality criteria are met.
-
-**Key Features:**
-- Generate-critique-revise loop
-- Structured feedback with quality scoring
-- Quality-based termination (score >= 7)
-- Maximum iteration limits
-- Full critique history visibility
-
-## Why Advanced Patterns?
-
-These patterns demonstrate sophisticated agent behaviors:
-- **Self-improvement**: Agents that refine their own outputs
-- **Quality assurance**: Automatic quality checking and iteration
-- **Transparency**: Full visibility into the improvement process
-
-## Prerequisites
-
-- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
-- OpenAI API key set: `export OPENAI_API_KEY=your-key`
-- Dependencies installed via `uv sync --group langgraph`
diff --git a/langgraph_samples/advanced/__init__.py b/langgraph_samples/advanced/__init__.py
deleted file mode 100644
index 028667c7..00000000
--- a/langgraph_samples/advanced/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Advanced pattern samples for Temporal LangGraph integration."""
diff --git a/langgraph_samples/rag/agentic_rag/README.md b/langgraph_samples/agentic_rag/README.md
similarity index 100%
rename from langgraph_samples/rag/agentic_rag/README.md
rename to langgraph_samples/agentic_rag/README.md
diff --git a/langgraph_samples/rag/agentic_rag/__init__.py b/langgraph_samples/agentic_rag/__init__.py
similarity index 100%
rename from langgraph_samples/rag/agentic_rag/__init__.py
rename to langgraph_samples/agentic_rag/__init__.py
diff --git a/langgraph_samples/rag/agentic_rag/graph.py b/langgraph_samples/agentic_rag/graph.py
similarity index 100%
rename from langgraph_samples/rag/agentic_rag/graph.py
rename to langgraph_samples/agentic_rag/graph.py
diff --git a/langgraph_samples/rag/agentic_rag/run_worker.py b/langgraph_samples/agentic_rag/run_worker.py
similarity index 91%
rename from langgraph_samples/rag/agentic_rag/run_worker.py
rename to langgraph_samples/agentic_rag/run_worker.py
index e82598d6..4aec8df7 100644
--- a/langgraph_samples/rag/agentic_rag/run_worker.py
+++ b/langgraph_samples/agentic_rag/run_worker.py
@@ -24,8 +24,8 @@
 from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
-from langgraph_samples.rag.agentic_rag.graph import build_agentic_rag_graph
-from langgraph_samples.rag.agentic_rag.workflow import AgenticRAGWorkflow
+from langgraph_samples.agentic_rag.graph import build_agentic_rag_graph
+from langgraph_samples.agentic_rag.workflow import AgenticRAGWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/rag/agentic_rag/run_workflow.py b/langgraph_samples/agentic_rag/run_workflow.py
similarity index 92%
rename from langgraph_samples/rag/agentic_rag/run_workflow.py
rename to langgraph_samples/agentic_rag/run_workflow.py
index 6fd32285..09d7c2de 100644
--- a/langgraph_samples/rag/agentic_rag/run_workflow.py
+++ b/langgraph_samples/agentic_rag/run_workflow.py
@@ -5,7 +5,7 @@
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
 
-from langgraph_samples.rag.agentic_rag.workflow import AgenticRAGWorkflow
+from langgraph_samples.agentic_rag.workflow import AgenticRAGWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/rag/agentic_rag/workflow.py b/langgraph_samples/agentic_rag/workflow.py
similarity index 100%
rename from langgraph_samples/rag/agentic_rag/workflow.py
rename to langgraph_samples/agentic_rag/workflow.py
diff --git a/langgraph_samples/human_in_loop/approval_workflow/README.md b/langgraph_samples/approval_workflow/README.md
similarity index 100%
rename from langgraph_samples/human_in_loop/approval_workflow/README.md
rename to langgraph_samples/approval_workflow/README.md
diff --git a/langgraph_samples/human_in_loop/approval_workflow/__init__.py b/langgraph_samples/approval_workflow/__init__.py
similarity index 100%
rename from langgraph_samples/human_in_loop/approval_workflow/__init__.py
rename to langgraph_samples/approval_workflow/__init__.py
diff --git a/langgraph_samples/human_in_loop/approval_workflow/activities.py b/langgraph_samples/approval_workflow/activities.py
similarity index 65%
rename from langgraph_samples/human_in_loop/approval_workflow/activities.py
rename to langgraph_samples/approval_workflow/activities.py
index cc2c9ea0..4c31a06a 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/activities.py
+++ b/langgraph_samples/approval_workflow/activities.py
@@ -27,8 +27,8 @@ async def notify_approver(request_info: dict) -> str:
         f"NOTIFICATION: {message}\n"
         f"  Workflow ID: {workflow_id}\n"
         f"  To respond, run:\n"
-        f"    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond {workflow_id} --approve --reason 'Approved'\n"
-        f"    python -m langgraph_samples.human_in_loop.approval_workflow.run_respond {workflow_id} --reject --reason 'Rejected'"
+        f"    python -m langgraph_samples.approval_workflow.run_respond {workflow_id} --approve --reason 'Approved'\n"
+        f"    python -m langgraph_samples.approval_workflow.run_respond {workflow_id} --reject --reason 'Rejected'"
     )
 
     # In production, you would send actual notification here
@@ -36,8 +36,8 @@ async def notify_approver(request_info: dict) -> str:
     print(f"Workflow ID: {workflow_id}")
     print(f"Request: {message}")
     print(f"\nTo respond, run:")
-    print(f"  Approve: uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond {workflow_id} --approve --reason 'Your reason'")
-    print(f"  Reject:  uv run python -m langgraph_samples.human_in_loop.approval_workflow.run_respond {workflow_id} --reject --reason 'Your reason'")
+    print(f"  Approve: uv run python -m langgraph_samples.approval_workflow.run_respond {workflow_id} --approve --reason 'Your reason'")
+    print(f"  Reject:  uv run python -m langgraph_samples.approval_workflow.run_respond {workflow_id} --reject --reason 'Your reason'")
     print()
 
     return f"Notification sent for workflow {workflow_id}"
diff --git a/langgraph_samples/human_in_loop/approval_workflow/graph.py b/langgraph_samples/approval_workflow/graph.py
similarity index 100%
rename from langgraph_samples/human_in_loop/approval_workflow/graph.py
rename to langgraph_samples/approval_workflow/graph.py
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_respond.py b/langgraph_samples/approval_workflow/run_respond.py
similarity index 96%
rename from langgraph_samples/human_in_loop/approval_workflow/run_respond.py
rename to langgraph_samples/approval_workflow/run_respond.py
index f51a2326..5f0b2146 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/run_respond.py
+++ b/langgraph_samples/approval_workflow/run_respond.py
@@ -9,7 +9,7 @@
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
 
-from langgraph_samples.human_in_loop.approval_workflow.workflow import ApprovalWorkflow
+from langgraph_samples.approval_workflow.workflow import ApprovalWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_worker.py b/langgraph_samples/approval_workflow/run_worker.py
similarity index 81%
rename from langgraph_samples/human_in_loop/approval_workflow/run_worker.py
rename to langgraph_samples/approval_workflow/run_worker.py
index de667d3e..83fe5672 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/run_worker.py
+++ b/langgraph_samples/approval_workflow/run_worker.py
@@ -11,9 +11,9 @@
 from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
-from langgraph_samples.human_in_loop.approval_workflow.activities import notify_approver
-from langgraph_samples.human_in_loop.approval_workflow.graph import build_approval_graph
-from langgraph_samples.human_in_loop.approval_workflow.workflow import ApprovalWorkflow
+from langgraph_samples.approval_workflow.activities import notify_approver
+from langgraph_samples.approval_workflow.graph import build_approval_graph
+from langgraph_samples.approval_workflow.workflow import ApprovalWorkflow
 
 TASK_QUEUE = "langgraph-approval"
 
diff --git a/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py b/langgraph_samples/approval_workflow/run_workflow.py
similarity index 95%
rename from langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
rename to langgraph_samples/approval_workflow/run_workflow.py
index 2d2107e9..ac612113 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
+++ b/langgraph_samples/approval_workflow/run_workflow.py
@@ -10,7 +10,7 @@
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
 
-from langgraph_samples.human_in_loop.approval_workflow.workflow import (
+from langgraph_samples.approval_workflow.workflow import (
     ApprovalRequest,
     ApprovalWorkflow,
 )
diff --git a/langgraph_samples/human_in_loop/approval_workflow/workflow.py b/langgraph_samples/approval_workflow/workflow.py
similarity index 98%
rename from langgraph_samples/human_in_loop/approval_workflow/workflow.py
rename to langgraph_samples/approval_workflow/workflow.py
index bd8c94eb..5b262353 100644
--- a/langgraph_samples/human_in_loop/approval_workflow/workflow.py
+++ b/langgraph_samples/approval_workflow/workflow.py
@@ -17,7 +17,7 @@
 with workflow.unsafe.imports_passed_through():
     from langgraph.types import Command
 
-    from langgraph_samples.human_in_loop.approval_workflow.activities import (
+    from langgraph_samples.approval_workflow.activities import (
         notify_approver,
     )
     from temporalio.contrib.langgraph import compile as lg_compile
diff --git a/langgraph_samples/basic/README.md b/langgraph_samples/basic/README.md
deleted file mode 100644
index d88057d1..00000000
--- a/langgraph_samples/basic/README.md
+++ /dev/null
@@ -1,18 +0,0 @@
-# Basic LangGraph Examples
-
-Simple examples to get started with LangGraph integrated with Temporal workflows.
-
-Before running these examples, be sure to review the [prerequisites and background on the integration](../README.md).
-
-## Examples
-
-### Hello World Agent
-
-Minimal LangGraph agent with Temporal integration. A single-node graph that processes a query.
-
-**Demonstrates:**
-- Basic plugin setup and graph registration
-- Simple workflow invocation
-- Activity-based node execution
-
-See [hello_world/README.md](./hello_world/README.md) for details.
diff --git a/langgraph_samples/basic/__init__.py b/langgraph_samples/basic/__init__.py
deleted file mode 100644
index 98e74479..00000000
--- a/langgraph_samples/basic/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Basic LangGraph Examples
diff --git a/langgraph_samples/rag/deep_research/README.md b/langgraph_samples/deep_research/README.md
similarity index 100%
rename from langgraph_samples/rag/deep_research/README.md
rename to langgraph_samples/deep_research/README.md
diff --git a/langgraph_samples/rag/deep_research/__init__.py b/langgraph_samples/deep_research/__init__.py
similarity index 100%
rename from langgraph_samples/rag/deep_research/__init__.py
rename to langgraph_samples/deep_research/__init__.py
diff --git a/langgraph_samples/rag/deep_research/graph.py b/langgraph_samples/deep_research/graph.py
similarity index 100%
rename from langgraph_samples/rag/deep_research/graph.py
rename to langgraph_samples/deep_research/graph.py
diff --git a/langgraph_samples/rag/deep_research/run_worker.py b/langgraph_samples/deep_research/run_worker.py
similarity index 88%
rename from langgraph_samples/rag/deep_research/run_worker.py
rename to langgraph_samples/deep_research/run_worker.py
index f48bac7b..39e65f06 100644
--- a/langgraph_samples/rag/deep_research/run_worker.py
+++ b/langgraph_samples/deep_research/run_worker.py
@@ -15,8 +15,8 @@
 from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
-from langgraph_samples.rag.deep_research.graph import build_deep_research_graph
-from langgraph_samples.rag.deep_research.workflow import DeepResearchWorkflow
+from langgraph_samples.deep_research.graph import build_deep_research_graph
+from langgraph_samples.deep_research.workflow import DeepResearchWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/rag/deep_research/run_workflow.py b/langgraph_samples/deep_research/run_workflow.py
similarity index 93%
rename from langgraph_samples/rag/deep_research/run_workflow.py
rename to langgraph_samples/deep_research/run_workflow.py
index 6a6bdb88..37331428 100644
--- a/langgraph_samples/rag/deep_research/run_workflow.py
+++ b/langgraph_samples/deep_research/run_workflow.py
@@ -5,7 +5,7 @@
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
 
-from langgraph_samples.rag.deep_research.workflow import DeepResearchWorkflow
+from langgraph_samples.deep_research.workflow import DeepResearchWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/rag/deep_research/workflow.py b/langgraph_samples/deep_research/workflow.py
similarity index 100%
rename from langgraph_samples/rag/deep_research/workflow.py
rename to langgraph_samples/deep_research/workflow.py
diff --git a/langgraph_samples/basic/hello_world/README.md b/langgraph_samples/hello_world/README.md
similarity index 100%
rename from langgraph_samples/basic/hello_world/README.md
rename to langgraph_samples/hello_world/README.md
diff --git a/langgraph_samples/basic/hello_world/__init__.py b/langgraph_samples/hello_world/__init__.py
similarity index 100%
rename from langgraph_samples/basic/hello_world/__init__.py
rename to langgraph_samples/hello_world/__init__.py
diff --git a/langgraph_samples/basic/hello_world/graph.py b/langgraph_samples/hello_world/graph.py
similarity index 100%
rename from langgraph_samples/basic/hello_world/graph.py
rename to langgraph_samples/hello_world/graph.py
diff --git a/langgraph_samples/basic/hello_world/run_worker.py b/langgraph_samples/hello_world/run_worker.py
similarity index 88%
rename from langgraph_samples/basic/hello_world/run_worker.py
rename to langgraph_samples/hello_world/run_worker.py
index 8734513d..eae58ce0 100644
--- a/langgraph_samples/basic/hello_world/run_worker.py
+++ b/langgraph_samples/hello_world/run_worker.py
@@ -11,8 +11,8 @@
 from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
-from langgraph_samples.basic.hello_world.graph import build_hello_graph
-from langgraph_samples.basic.hello_world.workflow import HelloWorldWorkflow
+from langgraph_samples.hello_world.graph import build_hello_graph
+from langgraph_samples.hello_world.workflow import HelloWorldWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/basic/hello_world/run_workflow.py b/langgraph_samples/hello_world/run_workflow.py
similarity index 90%
rename from langgraph_samples/basic/hello_world/run_workflow.py
rename to langgraph_samples/hello_world/run_workflow.py
index c5c4f475..895c2647 100644
--- a/langgraph_samples/basic/hello_world/run_workflow.py
+++ b/langgraph_samples/hello_world/run_workflow.py
@@ -9,7 +9,7 @@
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
 
-from langgraph_samples.basic.hello_world.workflow import HelloWorldWorkflow
+from langgraph_samples.hello_world.workflow import HelloWorldWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/basic/hello_world/workflow.py b/langgraph_samples/hello_world/workflow.py
similarity index 100%
rename from langgraph_samples/basic/hello_world/workflow.py
rename to langgraph_samples/hello_world/workflow.py
diff --git a/langgraph_samples/human_in_loop/README.md b/langgraph_samples/human_in_loop/README.md
deleted file mode 100644
index de76345d..00000000
--- a/langgraph_samples/human_in_loop/README.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Human-in-the-Loop Samples
-
-This directory contains samples demonstrating human-in-the-loop patterns with Temporal LangGraph integration.
-
-## Available Samples
-
-### [Approval Workflow](./approval_workflow/README.md)
-
-An agent that pauses for human approval before taking actions, using LangGraph's `interrupt()` with Temporal signals.
-
-**Key Features:**
-- `interrupt()` for pausing execution
-- Temporal signals for receiving human input
-- Workflow queries for checking pending approvals
-- Durable waiting with automatic timeout handling
-
-## Prerequisites
-
-- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
-- OpenAI API key set: `export OPENAI_API_KEY=your-key`
-- Dependencies installed via `uv sync --group langgraph`
diff --git a/langgraph_samples/human_in_loop/__init__.py b/langgraph_samples/human_in_loop/__init__.py
deleted file mode 100644
index a41e9687..00000000
--- a/langgraph_samples/human_in_loop/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Human-in-the-Loop samples demonstrating interrupt and approval patterns."""
diff --git a/langgraph_samples/multi_agent/README.md b/langgraph_samples/multi_agent/README.md
deleted file mode 100644
index ad33db08..00000000
--- a/langgraph_samples/multi_agent/README.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Multi-Agent Systems
-
-Samples demonstrating multi-agent orchestration patterns with LangGraph and Temporal.
-
-## Samples
-
-### [Supervisor](./supervisor/)
-
-A supervisor agent that coordinates specialized worker agents (researcher, writer, analyst). The supervisor routes tasks to appropriate agents and aggregates results.
-
-**Demonstrates:**
-- Multi-agent orchestration patterns
-- Supervisor decision-making and task routing
-- Agent specialization with different tools
-- Durable execution across agent handoffs
-
-## Multi-Agent Patterns
-
-LangGraph supports several multi-agent patterns:
-
-1. **Supervisor** (this sample): A central coordinator routes tasks to specialists
-2. **Swarm**: Agents hand off to each other dynamically based on the conversation
-3. **Hierarchical**: Team supervisors under a top-level coordinator
-4. **Collaborative**: Agents work together on shared state
-
-## Temporal Benefits for Multi-Agent Systems
-
-Running multi-agent systems with Temporal provides:
-
-- **Durability**: Agent interactions are checkpointed, surviving failures
-- **Retries**: Failed agent calls are automatically retried
-- **Visibility**: Track complex agent interactions through Temporal's UI
-- **Long-running support**: Multi-agent workflows can run for extended periods
-- **Scalability**: Distribute agent execution across workers
diff --git a/langgraph_samples/multi_agent/__init__.py b/langgraph_samples/multi_agent/__init__.py
deleted file mode 100644
index e3d841b5..00000000
--- a/langgraph_samples/multi_agent/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# Multi-Agent samples for LangGraph + Temporal integration
diff --git a/langgraph_samples/planning/plan_and_execute/README.md b/langgraph_samples/plan_and_execute/README.md
similarity index 100%
rename from langgraph_samples/planning/plan_and_execute/README.md
rename to langgraph_samples/plan_and_execute/README.md
diff --git a/langgraph_samples/planning/plan_and_execute/__init__.py b/langgraph_samples/plan_and_execute/__init__.py
similarity index 100%
rename from langgraph_samples/planning/plan_and_execute/__init__.py
rename to langgraph_samples/plan_and_execute/__init__.py
diff --git a/langgraph_samples/planning/plan_and_execute/graph.py b/langgraph_samples/plan_and_execute/graph.py
similarity index 100%
rename from langgraph_samples/planning/plan_and_execute/graph.py
rename to langgraph_samples/plan_and_execute/graph.py
diff --git a/langgraph_samples/planning/plan_and_execute/run_worker.py b/langgraph_samples/plan_and_execute/run_worker.py
similarity index 91%
rename from langgraph_samples/planning/plan_and_execute/run_worker.py
rename to langgraph_samples/plan_and_execute/run_worker.py
index 3104563d..a82e1e81 100644
--- a/langgraph_samples/planning/plan_and_execute/run_worker.py
+++ b/langgraph_samples/plan_and_execute/run_worker.py
@@ -15,10 +15,10 @@
 from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
-from langgraph_samples.planning.plan_and_execute.graph import (
+from langgraph_samples.plan_and_execute.graph import (
     build_plan_and_execute_graph,
 )
-from langgraph_samples.planning.plan_and_execute.workflow import (
+from langgraph_samples.plan_and_execute.workflow import (
     PlanAndExecuteWorkflow,
 )
 
diff --git a/langgraph_samples/planning/plan_and_execute/run_workflow.py b/langgraph_samples/plan_and_execute/run_workflow.py
similarity index 95%
rename from langgraph_samples/planning/plan_and_execute/run_workflow.py
rename to langgraph_samples/plan_and_execute/run_workflow.py
index 1844c14f..672aaa8c 100644
--- a/langgraph_samples/planning/plan_and_execute/run_workflow.py
+++ b/langgraph_samples/plan_and_execute/run_workflow.py
@@ -5,7 +5,7 @@
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
 
-from langgraph_samples.planning.plan_and_execute.workflow import (
+from langgraph_samples.plan_and_execute.workflow import (
     PlanAndExecuteWorkflow,
 )
 
diff --git a/langgraph_samples/planning/plan_and_execute/workflow.py b/langgraph_samples/plan_and_execute/workflow.py
similarity index 100%
rename from langgraph_samples/planning/plan_and_execute/workflow.py
rename to langgraph_samples/plan_and_execute/workflow.py
diff --git a/langgraph_samples/planning/README.md b/langgraph_samples/planning/README.md
deleted file mode 100644
index d70d865a..00000000
--- a/langgraph_samples/planning/README.md
+++ /dev/null
@@ -1,30 +0,0 @@
-# Planning Samples
-
-This directory contains samples demonstrating planning patterns with Temporal LangGraph integration.
-
-## Available Samples
-
-### [Plan-and-Execute](./plan_and_execute/README.md)
-
-An agent that separates planning from execution, creating a high-level plan first and then executing each step sequentially with the ability to replan based on results.
-
-**Key Features:**
-- Structured planning with explicit steps
-- Sequential step execution with available tools
-- Dynamic replanning when steps fail
-- Progress visibility through Temporal
-- Checkpointing after each step
-
-## Why Planning Patterns?
-
-Planning agents offer several advantages:
-- **Predictability**: The plan is visible before execution
-- **Control**: Users can review/modify plans
-- **Resilience**: Failed steps can be replanned
-- **Observability**: Progress is tracked step-by-step
-
-## Prerequisites
-
-- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
-- OpenAI API key set: `export OPENAI_API_KEY=your-key`
-- Dependencies installed via `uv sync --group langgraph`
diff --git a/langgraph_samples/planning/__init__.py b/langgraph_samples/planning/__init__.py
deleted file mode 100644
index 37dae022..00000000
--- a/langgraph_samples/planning/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Planning samples for Temporal LangGraph integration."""
diff --git a/langgraph_samples/rag/README.md b/langgraph_samples/rag/README.md
deleted file mode 100644
index ccf8ba6f..00000000
--- a/langgraph_samples/rag/README.md
+++ /dev/null
@@ -1,32 +0,0 @@
-# RAG (Retrieval Augmented Generation) Samples
-
-This directory contains samples demonstrating RAG patterns with Temporal LangGraph integration.
-
-## Available Samples
-
-### [Agentic RAG](./agentic_rag/README.md)
-
-An intelligent RAG system that decides when to retrieve documents, grades their relevance, and can rewrite queries when initial retrieval fails.
-
-**Key Features:**
-- Agent-driven retrieval decisions
-- Document relevance grading
-- Query rewriting for better retrieval
-- Durable execution with Temporal
-
-### [Deep Research Agent](./deep_research/README.md)
-
-A multi-step research agent that performs iterative research to produce comprehensive reports.
-
-**Key Features:**
-- Research planning with targeted search queries
-- Parallel search execution using LangGraph's Send API
-- Result evaluation and iteration
-- Long-running research with Temporal durability
-- Report synthesis from multiple sources
-
-## Prerequisites
-
-- Temporal server [running locally](https://docs.temporal.io/cli/server#start-dev)
-- OpenAI API key set: `export OPENAI_API_KEY=your-key`
-- Dependencies installed via `uv sync --group langgraph`
diff --git a/langgraph_samples/rag/__init__.py b/langgraph_samples/rag/__init__.py
deleted file mode 100644
index 675e2c58..00000000
--- a/langgraph_samples/rag/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""RAG (Retrieval Augmented Generation) samples for Temporal LangGraph."""
diff --git a/langgraph_samples/basic/react_agent/README.md b/langgraph_samples/react_agent/README.md
similarity index 100%
rename from langgraph_samples/basic/react_agent/README.md
rename to langgraph_samples/react_agent/README.md
diff --git a/langgraph_samples/basic/react_agent/__init__.py b/langgraph_samples/react_agent/__init__.py
similarity index 100%
rename from langgraph_samples/basic/react_agent/__init__.py
rename to langgraph_samples/react_agent/__init__.py
diff --git a/langgraph_samples/basic/react_agent/graph.py b/langgraph_samples/react_agent/graph.py
similarity index 95%
rename from langgraph_samples/basic/react_agent/graph.py
rename to langgraph_samples/react_agent/graph.py
index 213aadb8..964c596f 100644
--- a/langgraph_samples/basic/react_agent/graph.py
+++ b/langgraph_samples/react_agent/graph.py
@@ -14,7 +14,7 @@
 from langchain.agents import create_agent
 from langchain_openai import ChatOpenAI
 
-from langgraph_samples.basic.react_agent.tools import calculate, get_weather
+from langgraph_samples.react_agent.tools import calculate, get_weather
 
 
 def build_react_agent() -> Any:
diff --git a/langgraph_samples/basic/react_agent/run_worker.py b/langgraph_samples/react_agent/run_worker.py
similarity index 89%
rename from langgraph_samples/basic/react_agent/run_worker.py
rename to langgraph_samples/react_agent/run_worker.py
index ee2f6474..691720ff 100644
--- a/langgraph_samples/basic/react_agent/run_worker.py
+++ b/langgraph_samples/react_agent/run_worker.py
@@ -15,8 +15,8 @@
 from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
-from langgraph_samples.basic.react_agent.graph import build_react_agent
-from langgraph_samples.basic.react_agent.workflow import ReActAgentWorkflow
+from langgraph_samples.react_agent.graph import build_react_agent
+from langgraph_samples.react_agent.workflow import ReActAgentWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/basic/react_agent/run_workflow.py b/langgraph_samples/react_agent/run_workflow.py
similarity index 91%
rename from langgraph_samples/basic/react_agent/run_workflow.py
rename to langgraph_samples/react_agent/run_workflow.py
index 49f49f48..7850cef0 100644
--- a/langgraph_samples/basic/react_agent/run_workflow.py
+++ b/langgraph_samples/react_agent/run_workflow.py
@@ -5,7 +5,7 @@
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
 
-from langgraph_samples.basic.react_agent.workflow import ReActAgentWorkflow
+from langgraph_samples.react_agent.workflow import ReActAgentWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/basic/react_agent/tools.py b/langgraph_samples/react_agent/tools.py
similarity index 100%
rename from langgraph_samples/basic/react_agent/tools.py
rename to langgraph_samples/react_agent/tools.py
diff --git a/langgraph_samples/basic/react_agent/workflow.py b/langgraph_samples/react_agent/workflow.py
similarity index 100%
rename from langgraph_samples/basic/react_agent/workflow.py
rename to langgraph_samples/react_agent/workflow.py
diff --git a/langgraph_samples/advanced/reflection/README.md b/langgraph_samples/reflection/README.md
similarity index 100%
rename from langgraph_samples/advanced/reflection/README.md
rename to langgraph_samples/reflection/README.md
diff --git a/langgraph_samples/advanced/reflection/__init__.py b/langgraph_samples/reflection/__init__.py
similarity index 100%
rename from langgraph_samples/advanced/reflection/__init__.py
rename to langgraph_samples/reflection/__init__.py
diff --git a/langgraph_samples/advanced/reflection/graph.py b/langgraph_samples/reflection/graph.py
similarity index 100%
rename from langgraph_samples/advanced/reflection/graph.py
rename to langgraph_samples/reflection/graph.py
diff --git a/langgraph_samples/advanced/reflection/run_worker.py b/langgraph_samples/reflection/run_worker.py
similarity index 88%
rename from langgraph_samples/advanced/reflection/run_worker.py
rename to langgraph_samples/reflection/run_worker.py
index bf1f6144..f288b351 100644
--- a/langgraph_samples/advanced/reflection/run_worker.py
+++ b/langgraph_samples/reflection/run_worker.py
@@ -15,8 +15,8 @@
 from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
-from langgraph_samples.advanced.reflection.graph import build_reflection_graph
-from langgraph_samples.advanced.reflection.workflow import ReflectionWorkflow
+from langgraph_samples.reflection.graph import build_reflection_graph
+from langgraph_samples.reflection.workflow import ReflectionWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/advanced/reflection/run_workflow.py b/langgraph_samples/reflection/run_workflow.py
similarity index 95%
rename from langgraph_samples/advanced/reflection/run_workflow.py
rename to langgraph_samples/reflection/run_workflow.py
index f10b6f16..50e6d6e2 100644
--- a/langgraph_samples/advanced/reflection/run_workflow.py
+++ b/langgraph_samples/reflection/run_workflow.py
@@ -5,7 +5,7 @@
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
 
-from langgraph_samples.advanced.reflection.workflow import ReflectionWorkflow
+from langgraph_samples.reflection.workflow import ReflectionWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/advanced/reflection/workflow.py b/langgraph_samples/reflection/workflow.py
similarity index 100%
rename from langgraph_samples/advanced/reflection/workflow.py
rename to langgraph_samples/reflection/workflow.py
diff --git a/langgraph_samples/multi_agent/supervisor/README.md b/langgraph_samples/supervisor/README.md
similarity index 100%
rename from langgraph_samples/multi_agent/supervisor/README.md
rename to langgraph_samples/supervisor/README.md
diff --git a/langgraph_samples/multi_agent/supervisor/__init__.py b/langgraph_samples/supervisor/__init__.py
similarity index 100%
rename from langgraph_samples/multi_agent/supervisor/__init__.py
rename to langgraph_samples/supervisor/__init__.py
diff --git a/langgraph_samples/multi_agent/supervisor/graph.py b/langgraph_samples/supervisor/graph.py
similarity index 100%
rename from langgraph_samples/multi_agent/supervisor/graph.py
rename to langgraph_samples/supervisor/graph.py
diff --git a/langgraph_samples/multi_agent/supervisor/run_worker.py b/langgraph_samples/supervisor/run_worker.py
similarity index 90%
rename from langgraph_samples/multi_agent/supervisor/run_worker.py
rename to langgraph_samples/supervisor/run_worker.py
index 87269905..9bf07ff6 100644
--- a/langgraph_samples/multi_agent/supervisor/run_worker.py
+++ b/langgraph_samples/supervisor/run_worker.py
@@ -15,8 +15,8 @@
 from temporalio.envconfig import ClientConfig
 from temporalio.worker import Worker
 
-from langgraph_samples.multi_agent.supervisor.graph import build_supervisor_graph
-from langgraph_samples.multi_agent.supervisor.workflow import SupervisorWorkflow
+from langgraph_samples.supervisor.graph import build_supervisor_graph
+from langgraph_samples.supervisor.workflow import SupervisorWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/multi_agent/supervisor/run_workflow.py b/langgraph_samples/supervisor/run_workflow.py
similarity index 95%
rename from langgraph_samples/multi_agent/supervisor/run_workflow.py
rename to langgraph_samples/supervisor/run_workflow.py
index 6d5f1103..953fcacb 100644
--- a/langgraph_samples/multi_agent/supervisor/run_workflow.py
+++ b/langgraph_samples/supervisor/run_workflow.py
@@ -5,7 +5,7 @@
 from temporalio.client import Client
 from temporalio.envconfig import ClientConfig
 
-from langgraph_samples.multi_agent.supervisor.workflow import SupervisorWorkflow
+from langgraph_samples.supervisor.workflow import SupervisorWorkflow
 
 
 async def main() -> None:
diff --git a/langgraph_samples/multi_agent/supervisor/workflow.py b/langgraph_samples/supervisor/workflow.py
similarity index 100%
rename from langgraph_samples/multi_agent/supervisor/workflow.py
rename to langgraph_samples/supervisor/workflow.py

From fe7134dc62cb99291bd64cea6114a784a7fd7ec1 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Mon, 29 Dec 2025 15:51:36 -0800
Subject: [PATCH 23/24] LangGraph samples: Update README run commands for
 flattened structure

---
 langgraph_samples/agentic_rag/README.md       |  4 ++--
 langgraph_samples/approval_workflow/README.md | 14 +++++++-------
 langgraph_samples/deep_research/README.md     |  4 ++--
 langgraph_samples/hello_world/README.md       |  4 ++--
 langgraph_samples/plan_and_execute/README.md  |  4 ++--
 langgraph_samples/react_agent/README.md       |  4 ++--
 langgraph_samples/reflection/README.md        |  4 ++--
 langgraph_samples/supervisor/README.md        |  4 ++--
 8 files changed, 21 insertions(+), 21 deletions(-)

diff --git a/langgraph_samples/agentic_rag/README.md b/langgraph_samples/agentic_rag/README.md
index 4b130171..2fa66af1 100644
--- a/langgraph_samples/agentic_rag/README.md
+++ b/langgraph_samples/agentic_rag/README.md
@@ -67,12 +67,12 @@ The sample includes a knowledge base with documents about:
 
 First, start the worker:
 ```bash
-uv run langgraph_samples/rag/agentic_rag/run_worker.py
+uv run langgraph_samples/agentic_rag/run_worker.py
 ```
 
 Then, in a separate terminal, run the workflow:
 ```bash
-uv run langgraph_samples/rag/agentic_rag/run_workflow.py
+uv run langgraph_samples/agentic_rag/run_workflow.py
 ```
 
 ## Expected Output
diff --git a/langgraph_samples/approval_workflow/README.md b/langgraph_samples/approval_workflow/README.md
index 174cc5a9..a5c38ae0 100644
--- a/langgraph_samples/approval_workflow/README.md
+++ b/langgraph_samples/approval_workflow/README.md
@@ -31,12 +31,12 @@ Request → [Process & Assess Risk] → [Interrupt] → [Notify Approver] → [W
 
 **Terminal 1 - Start the worker:**
 ```bash
-uv run langgraph_samples/human_in_loop/approval_workflow/run_worker.py
+uv run langgraph_samples/approval_workflow/run_worker.py
 ```
 
 **Terminal 2 - Start a workflow:**
 ```bash
-uv run langgraph_samples/human_in_loop/approval_workflow/run_workflow.py
+uv run langgraph_samples/approval_workflow/run_workflow.py
 ```
 
 The worker will print notification instructions like:
@@ -46,20 +46,20 @@ Workflow ID: approval-abc12345
 Request: Please approve purchase for $500.00 (Risk: medium)
 
 To respond, run:
-  Approve: uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --approve --reason 'Your reason'
-  Reject:  uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --reject --reason 'Your reason'
+  Approve: uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --approve --reason 'Your reason'
+  Reject:  uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --reject --reason 'Your reason'
 ```
 
 **Terminal 3 - Respond to the approval request:**
 ```bash
 # Check status
-uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --status
+uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --status
 
 # Approve
-uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --approve --reason "Within budget"
+uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --approve --reason "Within budget"
 
 # Or reject
-uv run langgraph_samples/human_in_loop/approval_workflow/run_respond.py approval-abc12345 --reject --reason "Needs manager approval"
+uv run langgraph_samples/approval_workflow/run_respond.py approval-abc12345 --reject --reason "Needs manager approval"
 ```
 
 ## Response Script Options
diff --git a/langgraph_samples/deep_research/README.md b/langgraph_samples/deep_research/README.md
index 523e53f9..39c6b5de 100644
--- a/langgraph_samples/deep_research/README.md
+++ b/langgraph_samples/deep_research/README.md
@@ -51,12 +51,12 @@ Each search runs as a separate Temporal activity, enabling parallel execution.
 2. In one terminal, start the worker:
    ```bash
    export OPENAI_API_KEY=your-key-here
-   uv run langgraph_samples/rag/deep_research/run_worker.py
+   uv run langgraph_samples/deep_research/run_worker.py
    ```
 
 3. In another terminal, run the workflow:
    ```bash
-   uv run langgraph_samples/rag/deep_research/run_workflow.py
+   uv run langgraph_samples/deep_research/run_workflow.py
    ```
 
 ## Sample Output
diff --git a/langgraph_samples/hello_world/README.md b/langgraph_samples/hello_world/README.md
index f06885df..328f77c8 100644
--- a/langgraph_samples/hello_world/README.md
+++ b/langgraph_samples/hello_world/README.md
@@ -21,12 +21,12 @@ Minimal LangGraph agent with Temporal integration. A single-node graph that proc
 
 First, start the worker:
 ```bash
-uv run langgraph_samples/basic/hello_world/run_worker.py
+uv run langgraph_samples/hello_world/run_worker.py
 ```
 
 Then, in a separate terminal, run the workflow:
 ```bash
-uv run langgraph_samples/basic/hello_world/run_workflow.py
+uv run langgraph_samples/hello_world/run_workflow.py
 ```
 
 ## Expected Output
diff --git a/langgraph_samples/plan_and_execute/README.md b/langgraph_samples/plan_and_execute/README.md
index 8205ecb3..d0c108e3 100644
--- a/langgraph_samples/plan_and_execute/README.md
+++ b/langgraph_samples/plan_and_execute/README.md
@@ -57,12 +57,12 @@ The executor agent has access to:
 2. In one terminal, start the worker:
    ```bash
    export OPENAI_API_KEY=your-key-here
-   uv run langgraph_samples/planning/plan_and_execute/run_worker.py
+   uv run langgraph_samples/plan_and_execute/run_worker.py
    ```
 
 3. In another terminal, run the workflow:
    ```bash
-   uv run langgraph_samples/planning/plan_and_execute/run_workflow.py
+   uv run langgraph_samples/plan_and_execute/run_workflow.py
    ```
 
 ## Sample Output
diff --git a/langgraph_samples/react_agent/README.md b/langgraph_samples/react_agent/README.md
index 9b9e645c..697c4c87 100644
--- a/langgraph_samples/react_agent/README.md
+++ b/langgraph_samples/react_agent/README.md
@@ -41,12 +41,12 @@ Each node execution is:
 
 First, start the worker:
 ```bash
-uv run langgraph_samples/basic/react_agent/run_worker.py
+uv run langgraph_samples/react_agent/run_worker.py
 ```
 
 Then, in a separate terminal, run the workflow:
 ```bash
-uv run langgraph_samples/basic/react_agent/run_workflow.py
+uv run langgraph_samples/react_agent/run_workflow.py
 ```
 
 ## Expected Output
diff --git a/langgraph_samples/reflection/README.md b/langgraph_samples/reflection/README.md
index 1124c975..3db2b665 100644
--- a/langgraph_samples/reflection/README.md
+++ b/langgraph_samples/reflection/README.md
@@ -56,12 +56,12 @@ Each node runs as a Temporal activity with automatic retry.
 2. In one terminal, start the worker:
    ```bash
    export OPENAI_API_KEY=your-key-here
-   uv run langgraph_samples/advanced/reflection/run_worker.py
+   uv run langgraph_samples/reflection/run_worker.py
    ```
 
 3. In another terminal, run the workflow:
    ```bash
-   uv run langgraph_samples/advanced/reflection/run_workflow.py
+   uv run langgraph_samples/reflection/run_workflow.py
    ```
 
 ## Sample Output
diff --git a/langgraph_samples/supervisor/README.md b/langgraph_samples/supervisor/README.md
index a1f4ba2e..172e08d1 100644
--- a/langgraph_samples/supervisor/README.md
+++ b/langgraph_samples/supervisor/README.md
@@ -68,12 +68,12 @@ With Temporal, the multi-agent system gains:
 
 First, start the worker:
 ```bash
-uv run langgraph_samples/multi_agent/supervisor/run_worker.py
+uv run langgraph_samples/supervisor/run_worker.py
 ```
 
 Then, in a separate terminal, run the workflow:
 ```bash
-uv run langgraph_samples/multi_agent/supervisor/run_workflow.py
+uv run langgraph_samples/supervisor/run_workflow.py
 ```
 
 ## Expected Output

From 58516c1e9630e576177fc13fffcc9f50467a5dc5 Mon Sep 17 00:00:00 2001
From: Maxim Fateev <mfateev@gmail.com>
Date: Mon, 29 Dec 2025 21:01:20 -0800
Subject: [PATCH 24/24] Fix lint errors in langgraph samples

- Fix import sorting (I001) across all sample modules
- Apply ruff formatting to 11 files
- Add type annotations for mypy (executor_agent, critique, plan)
- Use cast() for structured output return types
---
 langgraph_samples/agentic_rag/graph.py              | 3 ++-
 langgraph_samples/agentic_rag/workflow.py           | 4 +---
 langgraph_samples/approval_workflow/activities.py   | 8 ++++++--
 langgraph_samples/approval_workflow/run_workflow.py | 4 +++-
 langgraph_samples/approval_workflow/workflow.py     | 6 ++++--
 langgraph_samples/deep_research/graph.py            | 8 +++-----
 langgraph_samples/deep_research/workflow.py         | 4 +---
 langgraph_samples/hello_world/graph.py              | 1 -
 langgraph_samples/plan_and_execute/graph.py         | 9 ++++-----
 langgraph_samples/react_agent/graph.py              | 2 +-
 langgraph_samples/react_agent/workflow.py           | 5 +----
 langgraph_samples/reflection/graph.py               | 8 ++++----
 langgraph_samples/reflection/workflow.py            | 4 +---
 langgraph_samples/supervisor/graph.py               | 2 +-
 langgraph_samples/supervisor/workflow.py            | 4 +---
 15 files changed, 33 insertions(+), 39 deletions(-)

diff --git a/langgraph_samples/agentic_rag/graph.py b/langgraph_samples/agentic_rag/graph.py
index a4e7ebda..c8e35fcc 100644
--- a/langgraph_samples/agentic_rag/graph.py
+++ b/langgraph_samples/agentic_rag/graph.py
@@ -29,7 +29,6 @@
 import os
 from typing import Annotated, Any, Literal, Sequence, cast
 
-from langchain.agents import create_agent
 from langchain_core.documents import Document
 from langchain_core.messages import BaseMessage, HumanMessage
 from langchain_core.output_parsers import StrOutputParser
@@ -42,6 +41,8 @@
 from pydantic import BaseModel, Field
 from typing_extensions import TypedDict
 
+from langchain.agents import create_agent
+
 # Sample documents about AI agents and LangGraph for the knowledge base
 SAMPLE_DOCUMENTS = [
     Document(
diff --git a/langgraph_samples/agentic_rag/workflow.py b/langgraph_samples/agentic_rag/workflow.py
index 4e3c18b8..ec979216 100644
--- a/langgraph_samples/agentic_rag/workflow.py
+++ b/langgraph_samples/agentic_rag/workflow.py
@@ -54,8 +54,6 @@ async def run(self, query: str) -> dict[str, Any]:
 
         # Execute the agent
         # The input format is a dict with "messages"
-        result = await app.ainvoke(
-            {"messages": [{"role": "user", "content": query}]}
-        )
+        result = await app.ainvoke({"messages": [{"role": "user", "content": query}]})
 
         return result
diff --git a/langgraph_samples/approval_workflow/activities.py b/langgraph_samples/approval_workflow/activities.py
index 4c31a06a..21aa9630 100644
--- a/langgraph_samples/approval_workflow/activities.py
+++ b/langgraph_samples/approval_workflow/activities.py
@@ -36,8 +36,12 @@ async def notify_approver(request_info: dict) -> str:
     print(f"Workflow ID: {workflow_id}")
     print(f"Request: {message}")
     print(f"\nTo respond, run:")
-    print(f"  Approve: uv run python -m langgraph_samples.approval_workflow.run_respond {workflow_id} --approve --reason 'Your reason'")
-    print(f"  Reject:  uv run python -m langgraph_samples.approval_workflow.run_respond {workflow_id} --reject --reason 'Your reason'")
+    print(
+        f"  Approve: uv run python -m langgraph_samples.approval_workflow.run_respond {workflow_id} --approve --reason 'Your reason'"
+    )
+    print(
+        f"  Reject:  uv run python -m langgraph_samples.approval_workflow.run_respond {workflow_id} --reject --reason 'Your reason'"
+    )
     print()
 
     return f"Notification sent for workflow {workflow_id}"
diff --git a/langgraph_samples/approval_workflow/run_workflow.py b/langgraph_samples/approval_workflow/run_workflow.py
index ac612113..d84f79b1 100644
--- a/langgraph_samples/approval_workflow/run_workflow.py
+++ b/langgraph_samples/approval_workflow/run_workflow.py
@@ -40,7 +40,9 @@ async def main() -> None:
     )
 
     print(f"Workflow started. Waiting for result...")
-    print(f"\nTo approve/reject, use the run_respond script (see worker output for commands)")
+    print(
+        f"\nTo approve/reject, use the run_respond script (see worker output for commands)"
+    )
 
     # Wait for the workflow to complete
     result = await handle.result()
diff --git a/langgraph_samples/approval_workflow/workflow.py b/langgraph_samples/approval_workflow/workflow.py
index 5b262353..c96ba0d4 100644
--- a/langgraph_samples/approval_workflow/workflow.py
+++ b/langgraph_samples/approval_workflow/workflow.py
@@ -16,11 +16,11 @@
 
 with workflow.unsafe.imports_passed_through():
     from langgraph.types import Command
+    from temporalio.contrib.langgraph import compile as lg_compile
 
     from langgraph_samples.approval_workflow.activities import (
         notify_approver,
     )
-    from temporalio.contrib.langgraph import compile as lg_compile
 
 
 @dataclass
@@ -147,7 +147,9 @@ async def run(
 
             workflow.logger.info(
                 "Received approval response: approved=%s",
-                self._approval_response.get("approved") if self._approval_response else None,
+                self._approval_response.get("approved")
+                if self._approval_response
+                else None,
             )
 
             # Resume with the approval response
diff --git a/langgraph_samples/deep_research/graph.py b/langgraph_samples/deep_research/graph.py
index 8db53762..8831da99 100644
--- a/langgraph_samples/deep_research/graph.py
+++ b/langgraph_samples/deep_research/graph.py
@@ -23,7 +23,7 @@
 """
 
 import os
-from typing import Annotated, Any, Literal
+from typing import Annotated, Any, Literal, cast
 
 from langchain_community.tools import DuckDuckGoSearchRun
 from langchain_core.messages import BaseMessage, HumanMessage
@@ -142,7 +142,7 @@ def plan_research(state: ResearchState) -> dict[str, Any]:
         )
 
         planner = plan_prompt | model.with_structured_output(ResearchPlan)
-        plan = planner.invoke({"topic": topic_str})
+        plan = cast(ResearchPlan, planner.invoke({"topic": topic_str}))
 
         return {
             "topic": topic_str,
@@ -253,9 +253,7 @@ def synthesize_report(state: ResearchState) -> dict[str, Any]:
         findings = []
         for result in results:
             if result.get("relevant", False):
-                findings.append(
-                    f"### {result['purpose']}\n{result['results']}"
-                )
+                findings.append(f"### {result['purpose']}\n{result['results']}")
 
         if not findings:
             findings_text = "Limited information was found on this topic."
diff --git a/langgraph_samples/deep_research/workflow.py b/langgraph_samples/deep_research/workflow.py
index 83545513..2f86a576 100644
--- a/langgraph_samples/deep_research/workflow.py
+++ b/langgraph_samples/deep_research/workflow.py
@@ -32,9 +32,7 @@ class DeepResearchWorkflow:
     """
 
     @workflow.run
-    async def run(
-        self, topic: str, max_iterations: int = 2
-    ) -> dict[str, Any]:
+    async def run(self, topic: str, max_iterations: int = 2) -> dict[str, Any]:
         """Run deep research on a topic.
 
         Args:
diff --git a/langgraph_samples/hello_world/graph.py b/langgraph_samples/hello_world/graph.py
index b374245e..dea3cef5 100644
--- a/langgraph_samples/hello_world/graph.py
+++ b/langgraph_samples/hello_world/graph.py
@@ -9,7 +9,6 @@
 from langgraph.graph import END, START, StateGraph
 from typing_extensions import TypedDict
 
-
 # =============================================================================
 # State Definition
 # =============================================================================
diff --git a/langgraph_samples/plan_and_execute/graph.py b/langgraph_samples/plan_and_execute/graph.py
index 14bb7a7c..99bfcd34 100644
--- a/langgraph_samples/plan_and_execute/graph.py
+++ b/langgraph_samples/plan_and_execute/graph.py
@@ -23,7 +23,6 @@
 import os
 from typing import Annotated, Any, Literal
 
-from langchain.agents import create_agent
 from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate
@@ -34,6 +33,8 @@
 from pydantic import BaseModel, Field
 from typing_extensions import TypedDict
 
+from langchain.agents import create_agent
+
 
 class PlanStep(BaseModel):
     """A single step in the execution plan."""
@@ -167,7 +168,7 @@ def build_plan_and_execute_graph() -> Any:
 
     # Build executor agent with tools
     tools = [calculate, lookup, analyze]
-    executor_agent = create_agent(model, tools)
+    executor_agent: Any = create_agent(model, tools)
 
     def create_plan(state: PlanExecuteState) -> dict[str, Any]:
         """Create an execution plan from the objective.
@@ -243,9 +244,7 @@ def execute_step(state: PlanExecuteState) -> dict[str, Any]:
         if result.get("messages"):
             last_msg = result["messages"][-1]
             result_content = (
-                last_msg.content
-                if hasattr(last_msg, "content")
-                else str(last_msg)
+                last_msg.content if hasattr(last_msg, "content") else str(last_msg)
             )
 
         step_result = StepResult(
diff --git a/langgraph_samples/react_agent/graph.py b/langgraph_samples/react_agent/graph.py
index 964c596f..8657ac3a 100644
--- a/langgraph_samples/react_agent/graph.py
+++ b/langgraph_samples/react_agent/graph.py
@@ -11,9 +11,9 @@
 import os
 from typing import Any
 
-from langchain.agents import create_agent
 from langchain_openai import ChatOpenAI
 
+from langchain.agents import create_agent
 from langgraph_samples.react_agent.tools import calculate, get_weather
 
 
diff --git a/langgraph_samples/react_agent/workflow.py b/langgraph_samples/react_agent/workflow.py
index 79ddac03..f28192b5 100644
--- a/langgraph_samples/react_agent/workflow.py
+++ b/langgraph_samples/react_agent/workflow.py
@@ -1,4 +1,3 @@
-
 """ReAct Agent Workflow.
 
 Temporal workflow that executes the ReAct agent with durable execution.
@@ -48,8 +47,6 @@ async def run(self, query: str) -> dict[str, Any]:
 
         # Execute the agent
         # The input format for create_react_agent is a dict with "messages"
-        result = await app.ainvoke(
-            {"messages": [{"role": "user", "content": query}]}
-        )
+        result = await app.ainvoke({"messages": [{"role": "user", "content": query}]})
 
         return result
diff --git a/langgraph_samples/reflection/graph.py b/langgraph_samples/reflection/graph.py
index a745cb04..396ca0fb 100644
--- a/langgraph_samples/reflection/graph.py
+++ b/langgraph_samples/reflection/graph.py
@@ -38,9 +38,7 @@ class Critique(BaseModel):
     strengths: list[str] = Field(description="What's good about the content")
     weaknesses: list[str] = Field(description="What needs improvement")
     suggestions: list[str] = Field(description="Specific suggestions for improvement")
-    quality_score: int = Field(
-        description="Quality score from 1-10", ge=1, le=10
-    )
+    quality_score: int = Field(description="Quality score from 1-10", ge=1, le=10)
     is_satisfactory: bool = Field(
         description="Whether the content meets quality standards (score >= 7)"
     )
@@ -164,7 +162,9 @@ def reflect(state: ReflectionState) -> dict[str, Any]:
         )
 
         critic = reflect_prompt | critic_model.with_structured_output(Critique)
-        critique = critic.invoke({"task": task, "draft": draft, "iteration": iteration})
+        critique: Any = critic.invoke(
+            {"task": task, "draft": draft, "iteration": iteration}
+        )
 
         return {
             "critiques": state.get("critiques", []) + [critique],
diff --git a/langgraph_samples/reflection/workflow.py b/langgraph_samples/reflection/workflow.py
index 0d94547f..63c79af7 100644
--- a/langgraph_samples/reflection/workflow.py
+++ b/langgraph_samples/reflection/workflow.py
@@ -32,9 +32,7 @@ class ReflectionWorkflow:
     """
 
     @workflow.run
-    async def run(
-        self, task: str, max_iterations: int = 3
-    ) -> dict[str, Any]:
+    async def run(self, task: str, max_iterations: int = 3) -> dict[str, Any]:
         """Run the reflection agent on a writing task.
 
         Args:
diff --git a/langgraph_samples/supervisor/graph.py b/langgraph_samples/supervisor/graph.py
index e4425463..ccf61617 100644
--- a/langgraph_samples/supervisor/graph.py
+++ b/langgraph_samples/supervisor/graph.py
@@ -21,9 +21,9 @@
 from typing import Any
 
 from langchain_openai import ChatOpenAI
-from langchain.agents import create_agent
 from langgraph_supervisor import create_supervisor
 
+from langchain.agents import create_agent
 
 # --- Tools for specialized agents ---
 
diff --git a/langgraph_samples/supervisor/workflow.py b/langgraph_samples/supervisor/workflow.py
index 00b876af..138176ed 100644
--- a/langgraph_samples/supervisor/workflow.py
+++ b/langgraph_samples/supervisor/workflow.py
@@ -50,8 +50,6 @@ async def run(self, request: str) -> dict[str, Any]:
 
         # Execute the multi-agent system
         # The supervisor will coordinate the specialized agents
-        result = await app.ainvoke(
-            {"messages": [{"role": "user", "content": request}]}
-        )
+        result = await app.ainvoke({"messages": [{"role": "user", "content": request}]})
 
         return result