microsoft/agent-lightning

Health v2 · maintenance only

Adoption not part of the score

17863 stars · 1570 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Agent Lightning is a lightweight (~3,500-line) agentic reinforcement learning framework from Microsoft for training LLM-powered agents with their real harnesses. It proxies model requests to capture training data, runs rollouts locally or as Kubernetes Jobs, and uses verl/vLLM for policy updates.

Use cases

• train an LLM agent with reinforcement learning
• fine-tune a coding agent on SWE-bench tasks
• run RL training rollouts on Kubernetes
• capture agent interactions as training data without changing agent code
• improve a Qwen model's agentic coding performance with RL

When to choose

• you want to RL-train agents while keeping their existing tools, control flow, and environments unchanged
• you need Kubernetes-native rollout execution without external sandbox services
• you want a minimal, hackable RL training stack built on verl and vLLM

When to avoid

• you only need simple supervised fine-tuning or prompt engineering rather than agentic RL
• you lack GPU infrastructure or don't want a verl/vLLM dependency stack
• you need a turnkey commercial product rather than a research-oriented framework

Facets

framework · maturity active

llm-training reinforcement-learning agent-framework llm-inference large-language-models reinforcement-learning machine-learning python agentic-rl verl vllm mlops rl-training agent-harness swe-bench ai-agents kubernetes docker gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="microsoft/agent-lightning")