{"id":"openmanus-openmanus-rl","name":"OpenManus-RL","homepage":null,"repo_url":"https://github.com/OpenManus/OpenManus-RL","category":"ai-ml","subcategories":[],"tags":["ai-ml","rl","llm","agentic","finetuning","reinforcement-learning","training","dataset","verla","pytorch","python"],"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.","use_cases":["Supervised fine-tuning (SFT) for agentic ReAct-style behaviors","RL-based fine-tuning of LLM agents (e.g., PPO/GRPO/DPO) using trajectory/reward signals","Training/validating agent reward modeling from annotated trajectories","Benchmarking tuned agents in simulated environments (WebShop, ALFWorld/ALFWorld)","Research on rollout strategies and reasoning formats for agent tuning"],"not_for":["Production-ready, turnkey hosted APIs for end users","Secure multi-tenant deployment without additional operational hardening","Low-dependency/simple integration scenarios (heavy ML training stack and submodules)","Use as a general-purpose authentication/authorization service"],"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.","alternatives":["VolcEngine Verl (directly)","Hugging Face TRL (for PPO/DPO-style training)","Ray/RLlib-based RL training pipelines","Open-source agent tuning datasets on Hugging Face (to pair with your own training code)"],"af_score":34.0,"security_score":16.8,"reliability_score":20.0,"package_type":"skill","discovery_source":["openclaw"],"priority":"high","status":"evaluated","version_evaluated":null,"last_evaluated":"2026-03-29T15:01:59.871797+00:00","interface":{"has_rest_api":false,"has_graphql":false,"has_grpc":false,"has_mcp_server":false,"mcp_server_url":null,"has_sdk":false,"sdk_languages":["Python"],"openapi_spec_url":null,"webhooks":false},"auth":{"methods":[],"oauth":false,"scopes":false,"notes":"No authentication/authorization mechanism is described because this appears to be a local/offline training and dataset project rather than a network service."},"pricing":{"model":null,"free_tier_exists":false,"free_tier_limits":null,"paid_tiers":[],"requires_credit_card":false,"estimated_workload_costs":null,"notes":"No pricing information is provided; costs likely come from compute for RL training (not specified)."},"requirements":{"requires_signup":false,"requires_credit_card":false,"domain_verification":false,"data_residency":[],"compliance":[],"min_contract":null},"agent_readiness":{"af_score":34.0,"security_score":16.8,"reliability_score":20.0,"mcp_server_quality":0.0,"documentation_accuracy":55.0,"error_message_quality":0.0,"error_message_notes":null,"auth_complexity":100.0,"rate_limit_clarity":0.0,"tls_enforcement":0.0,"auth_strength":0.0,"scope_granularity":0.0,"dependency_hygiene":45.0,"secret_handling":50.0,"security_notes":"This is an ML training/dataset repository; TLS/auth/scopes/rate-limits are not applicable as a network service. The README instructs installing packages such as wandb and flash-attn; no explicit guidance is given on secret handling (e.g., avoiding logging API keys) or on dependency/version pinning or vulnerability management.","uptime_documented":0.0,"version_stability":40.0,"breaking_changes_history":20.0,"error_recovery":20.0,"idempotency_support":"false","idempotency_notes":null,"pagination_style":"none","retry_guidance_documented":false,"known_agent_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."]}}