Agent Lightning
github.com/microsoft/agent-lightning- Category
- AI Agents
- Rank
- No. 122Tools index
- Pricing
- Open Source
- Platform
- cli
- Type
- TOOL
- Builder
- @microsoft
- GitHub
- 18.0k stars
- Latest release
- v1.0.1
- Date
About
A framework for training and optimizing AI agents using reinforcement learning and other algorithms. Supports any agent framework with minimal code changes and enables selective optimization of multi-agent systems.
What it does
Agent Lightning turns agent runs into ordered traces containing prompts, tool activity, rewards, and other events. A central store coordinates work, retries, workers, traces, and versioned resources. Training algorithms consume those records and publish updated prompt templates or model weights back into the execution loop.
Why it's ranked here
The architecture addresses a hard integration problem with unusually clear boundaries between execution, tracing, storage, and learning. OpenTelemetry support, an OpenAI-compatible proxy, multiple store backends, and documented debugging workflows make the design credible. Its training stack still carries meaningful dependency and operational complexity.
What's good
Tracing doubles as both observability and training data. Monotonic span sequencing preserves event order across machines despite clock skew. The store tracks retries, timeouts, stalled workers, and resource versions. Users can inspect experiments through a dashboard, run components separately, and choose reinforcement learning, supervised fine-tuning, or prompt optimization workflows.
Tradeoffs
The default package pulls in a broad server, telemetry, proxy, and monitoring stack. GPU training requires carefully selected PyTorch, vLLM, VERL, CUDA, and flash-attention combinations, with several explicit conflicts and exclusions. The command-line documentation warns that it may lag current behavior. Prompt optimization currently handles only one template, and Weave tracing remains experimental.
How to use it well
Use it when you already have working agent logic, measurable outcomes, and enough representative tasks to evaluate changes. Start with isolated rollouts and an in-memory store, inspect traces, then add the trainer and distributed workers. It does not remove the need to design rewards, validate generated prompts, or debug brittle agent behavior. Keep production observability alongside it.
Technical notes+
README.md describes events flowing into LightningStore, with Trainer coordinating datasets, runners, algorithms, resources, and inference updates. pyproject.toml defines the agentlightning Python package, Hatchling builds, Python 3.10 or newer, the agl CLI entry point, optional MongoDB and algorithm integrations, plus explicit dependency conflict groups. docs/deep-dive/store.md specifies rollout and attempt state transitions, watchdog behavior, worker heartbeats, retries, and monotonic span sequence IDs. docs/tutorials/traces.md details AgentOpsTracer, OtelTracer, experimental WeaveTracer, and LLMProxy request instrumentation. docs/reference/restful.md exposes the Lightning Store REST API through an OpenAPI document, while docs/tutorials/debug.md documents the experiment dashboard and component-level debugging flow.
Observed
- License
- MIT License
- Primary language
- Python
- Runtime
- Python 3.10 or newer
- Installation
- Published as the agentlightning package and installed with pip
- Interfaces
- Python library, agl command-line interface, REST API, and OpenAI-compatible LLM proxy
- Storage
- In-memory storage is built in; MongoDB support is optional
- Build system
- Hatchling builds wheel and source distributions
- Testing
- Pytest is configured with CPU, GPU, store, telemetry, database, and framework-specific markers
Read from README.md, pyproject.toml, docs/index.md, docs/changelog.md, docs/reference/cli.md, docs/deep-dive/store.md, docs/reference/agent.md, docs/reference/store.md, docs/reference/types.md, docs/tutorials/debug.md, docs/reference/runner.md, docs/tutorials/traces.md, docs/algorithm-zoo/apo.md, docs/reference/restful.md, docs/reference/semconv.md.
What it can do
Convert existing AI agents into optimizable systems
AI agent code from any framework → Optimizable agent system with training capabilities
Train AI agents using reinforcement learning
Agent system and training environment → Improved agent performance metrics and trained model
Optimize agent prompts automatically
Agent prompts and performance feedback → Optimized prompts with improved effectiveness
Selectively optimize components in multi-agent systems
Multi-agent system with specified components to optimize → Optimized multi-agent system with improved component performance
Apply multiple optimization algorithms to agents
Agent system and selected optimization algorithm → Agent optimized using specified algorithm
Integrate training capabilities without code modifications
Existing agent codebase → Training-enabled agent system with original code intact
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