OpenManus/OpenManus-RL

Health v2 · maintenance only

Flags: prerelease_only

Adoption not part of the score

4155 stars · 592 forks observed · 2026-08-28

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

OpenManus-RL is an open-source project for reinforcement-learning-based tuning of LLM agents, built on the verl training framework. It provides agent trajectory datasets, training pipelines, and benchmark evaluation on agent tasks like GAIA, AgentBench, WebShop, and OSWorld.

Use cases

• fine-tune an LLM agent with reinforcement learning
• train agents to reason and use tools with RL
• evaluate tuned models on agent benchmarks like GAIA and OSWorld
• get an open SFT dataset of agent trajectories
• experiment with rollout strategies and reward models for agents

When to choose

• you want to RL-tune an LLM agent rather than just prompt it
• you need an open agent trajectory dataset for training
• you want to reproduce or extend reasoning-model-style RL training for agents

When to avoid

• you only need to run an agent without any training
• you lack GPU resources for RL fine-tuning
• you need a production-ready, stable framework rather than a live research project

Facets

library · maturity active

llm-training reinforcement-learning agent-framework machine-learning large-language-models reinforcement-learning machine-learning python rlhf agent-tuning verl post-training llm-agents benchmarks sft-dataset ai-agents gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="OpenManus/OpenManus-RL")