Agent Lightning v0.2.0

Agent-Lightning v0.2.0 introduces major framework improvements, new execution strategies, expanded documentation, and enhanced reliability across the agent training and deployment workflow. This release includes 78 pull requests since v0.1.2.

Core Enhancements

• Lightning Store: Added unified interface and implementation for Agent-lightning's core storage.
• Emitter: Emitting any objects as spans to the store.
• Adapter and Tracer: Adapting to OpenAI-like messages, and OpenTelemetry dummy tracer.
• LLM Proxy: Added LLM Proxy as the first-class citizen in Agent-lightning.
• Agent Runner: New version providing a more modular and robust runner design.
• Embedded Algorithms: Algorithms are now embedded directly into trainers for simplicity.
• New Execution Strategies: Introduced Client-Server and Shared Memory execution models.
• Trainer Updates: Integrated v0.2 interfaces and FastAlgorithm validation.

Documentation & Examples

• Revamped documentation with new guides for agent creation, training, debugging, and store concepts.
• Improved quickstart tutorials, clarified installation and new deep-dive articles.
• Added and updated examples: SQL Agent, Calc-X, Local SFT, Search-R1, and APO algorithm.

Developer Experience

• Migrated build and CI pipelines to 1ES, split workflows and aggregate badges for clarity.
• Adopted uv as the dependency manager.
• Added GPU-based pytest workflows for full test coverage.
• Enhanced debugging UX, pre-commit configs, and linting (Pyright fixes, import sorting).

Ecosystem & Integrations

• Added support for agents built with Agent-framework.
• Added new community listings: DeepWerewolf and AgentFlow.

New Contributors

A warm welcome to our first-time contributors:
@hzy46, @lunaqiu, @syeehyn, @linhx1999, @SiyunZhao, and @acured 🎉

Full changelog: v0.1.2 → v0.2.0