{"id":"openmanus-openmanus-rl","name":"OpenManus-RL","af_score":34.0,"security_score":16.8,"reliability_score":20.0,"what_it_does":"OpenManus-RL is a Python-based RL tuning/finetuning codebase for agentic LLM systems, paired with an OpenManus-RL dataset of agent trajectories. It positions Verl (Volcano Engine Reinforcement Learning) as the primary RL training framework (e.g., PPO/DPO/custom reward modeling) and includes environment setup scripts for benchmarks such as WebShop and ALFWorld.","best_when":"You have ML infrastructure (GPUs), are comfortable with PyTorch/Verl-style RL training, and want to research/improve agent reasoning and tool-use behaviors using offline trajectory data and simulated environments.","avoid_when":"You need a simple REST/SDK integration, strict reproducibility guarantees without additional version pinning, or you cannot run/maintain complex training dependencies and environments.","last_evaluated":"2026-03-29T15:01:59.871797+00:00","has_mcp":false,"has_api":false,"auth_methods":[],"has_free_tier":false,"known_gotchas":["Heavy reliance on submodules (git submodule update --init --recursive) and ML dependencies; automation should include robust environment setup and version pinning.","Dataset is hosted on Hugging Face; agents may need reliable dataset download/caching and large artifact handling (not detailed here).","Training scripts are referenced (e.g., scripts/ppo_train/train_alfworld.sh) but granular API-like interfaces are not provided; agent integration is likely via running CLI/scripts rather than calling stable functions.","No explicit guidance for retries/error recovery or idempotent training runs is provided in the README excerpts."],"error_quality":0.0}