⏳ This skill is pending AI review.
Scores will appear once the review pipeline completes.
release
Prepare and publish stable Agent Lightning releases through the repository's version bump, pull-request checks, merge, tag, PyPI trusted-publishing, and versioned-documentation workflows. Use when asked to plan, cut, verify, or explain a release; treat nightly TestPyPI builds as a separate path.
Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
Agent Lightning was completely refactored in v1.0. For legacy releases earlier than v1.0, see this branch.
⚡ News
- [2026/09] We release a new coding agent example based on an MoE model. Pure RL improves Qwen3.5-35B-A3B on SWE-bench Verified from 47.8% to 61.6% after training it on only 1.8K training examples!
- [2026/08] Agent Lightning Skill is released. It helps a coding agent improve another AI agent against a benchmark.
- [2026/08] Agent Lightning v1.0 technical report is released!
- [2026/08] We open source Agent Lightning v1.0!
⚡ Key Features
- 🪶 ~3,500 lines of code: We treat simplicity as the first principle.
- 🧩 Train with real agent harnesses: Agents interact with the model through the Agent Lightning v1.0 proxy with ZERO changes, while keeping tools, context, control flow, and environments in the loop.
- ☸️ Native Kubernetes support: Run agents directly as Kubernetes Jobs without relying on external sandbox services.
- 💻 Full coding agent training example: Using only 6K training samples, an end-to-end Qwen3.5-9B workflow improves SWE-bench Verified from 41.8% to 56.4%, a gain of 14.6 percentage points. We release the full pipeline, including data cleaning, reward-hacking prevention, and training scripts. Update: We release a new coding agent training example based on Qwen3.5-35B-A3B. Pure RL improves Qwen3.5-35B-A3B on SWE-bench Verified from 47.8% to 61.6% after only 1.8K training examples, a gain of 13.8 percentage points.
⚡ Installation
The following is an example installation on a CUDA 13.0 machine:
cd <this-repo>
uv sync
bash scripts/setup_verl.sh 0.8.0 cu130
See the Installation Guide for details.
⚡ Architecture
Agent Lightning v1.0 keeps the training architecture simple with three lightweight components:
- Trainer: Runs
verland vLLM, builds training samples, and updates the policy. - API Gateway: Proxies model requests and captures training data.
- Rollout Controller: Runs agents locally or as Kubernetes Jobs.
The Trainer creates rollouts, the Controller launches agents, and the Gateway turns interactions into training data, while agents continue to run with their real harnesses.
⚡ Results
We evaluate Agent Lightning v1.0 across several practical training domains, including Search R1, LLM-in-Sandbox, and Coding Agent. Pure RL delivers substantial improvements across all three domains, as shown below.
⚡ Documentation
| Section | Content |
|---|---|
| Installation | Base environment and verl GPU stack |
| Quick Start | Local first run and end-to-end flow |
| Basics | Components, rollouts, events, and trajectories |
| Trainer Configuration | verl integration and trace aggregation |
| API Gateway Configuration | Gateway and model proxy settings |
| Controller Configuration | Local and Kubernetes runners |
| Asynchronous Training | Collocated async collection and pause/drain |
⚡ Examples
| Example | Description |
|---|---|
| Calc-X | POC math reasoning example with AutoGen and MCP calculator tools, requiring only one GPU. |
| GSM8K | POC grade-school math reasoning example. |
| ScienceWorld | Interactive science tasks in a text-based environment. |
| Search-R1 | Multi-turn retrieval and reasoning agent. |
| LLM-in-Sandbox | General agent with computer and code execution tools. |
| Coding Agent | Coding agent trained with repository tests. |
| Coding Agent: MoE | Train Qwen3.5-35B-A3B with Megatron and R3. |
⚡ Articles
- 8/19/2026 Agent Lightning v1.0: Towards Harnessed Agentic RL technical report.
- 12/17/2025 Adopting the Trajectory Level Aggregation for Faster Training Agent-lightning blog.
- 11/4/2025 Tuning ANY AI agent with Tinker ✕ Agent-lightning Medium. See also Part 2.
- 10/22/2025 No More Retokenization Drift: Returning Token IDs via the OpenAI Compatible API Matters in Agent RL vLLM blog. See also Zhihu writeup.
- 8/11/2025 Training AI Agents to Write and Self-correct SQL with Reinforcement Learning Medium.
- 8/5/2025 Agent Lightning: Train ANY AI Agents with Reinforcement Learning arXiv paper.
- 7/26/2025 We discovered an approach to train any AI agent with RL, with (almost) zero code changes. Reddit.
- 6/6/2025 Agent Lightning - Microsoft Research Project page.
⚡ Community Projects
- DeepWerewolf — A case study of agent RL training for the Chinese Werewolf game built with AgentScope and Agent Lightning.
- AgentFlow — A modular multi-agent framework that combines planner, executor, verifier, and generator agents with the Flow-GRPO algorithm to tackle long-horizon, sparse-reward tasks.
- Youtu-Agent — Youtu-Agent lets you build and train your agent with ease. Built with [a modified branch](https://github.com/microsoft/agent-lightning/tree/contrib/youtu-agent-lightnin
// HOW IT'S BUILT
KEY FILES