# AgentR1/Agent-R1

Agent-R1: Training Powerful LLM Agents with End-to-End Reinforcement Learning

Repository: https://github.com/AgentR1/Agent-R1
Canonical: https://ross.abutalabs.com/products/agent-r1
Language: Python
License: MIT
License Family: permissive
Topics: agent, agentic-rl, llm
Last push: 2026-08-24T18:10:46+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 35, longevity 39
- inputs: {"age_days": 548, "days_push": 9, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1633, forks 113 (observed 2026-08-28T04:05:14.273118+00:00)

## What it is
Agent-R1 is a modular Python framework for training LLM agents with end-to-end reinforcement learning. It models each interaction turn as a step-level MDP transition, making tool use, environment feedback, context management, and reward assignment explicit parts of the training loop.

## Use cases
- train an LLM agent to use tools via reinforcement learning
- run multi-step agentic RL with environment feedback
- fine-tune models on HotpotQA, ALFWorld, or WebShop agent tasks
- apply StepPO-style step-level policy optimization
- perform online policy distillation for agents
- build custom agentic RL training environments

## When to choose
- you need step-native RL training for multi-turn LLM agents rather than single-turn pipelines
- you want explicit control over tool use, context management, and reward assignment during training
- you want a research framework with recipes for standard agent benchmarks

## When to avoid
- you only need single-turn RLHF or DPO fine-tuning without agent interaction
- you need a production inference or agent-serving framework rather than a training framework
- you lack GPU resources for RL training

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, llm-training, reinforcement-learning, machine-learning
- domain: artificial-intelligence, reinforcement-learning, large-language-models, machine-learning
- platform: python
- tags: agentic-rl, step-level-mdp, tool-use, multi-step-agents, policy-optimization, online-policy-distillation, ai-agents, gpu, linux

## Member repositories
- AgentR1/Agent-R1 (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:14.273118+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:47:10.284793+00:00, confidence not recorded.
  - readme: https://github.com/AgentR1/Agent-R1 (fetched 2026-08-28T04:05:14.273118+00:00, sha 0ecfc7b21d79)
- Data as of 2026-08-30T08:39:29.467469+00:00.