{"id":"petergriffinjin-search-r1","name":"Search-R1","af_score":39.0,"security_score":22.0,"reliability_score":22.5,"what_it_does":"Search-R1 is an open-source reinforcement learning (RL) framework for training “reasoning-and-searching interleaved” LLMs. It supports RL methods (e.g., PPO/GRPO/reinforce), multiple base LLMs, and pluggable search/retrieval engines (local sparse/dense retrievers and online search). It also supports launching a separate local retrieval server that the LLM can call via an HTTP search/retrieve API during training/inference.","best_when":"You want to train or fine-tune LLMs for learned search/tool-calling behavior and you can run the required infrastructure locally (LLM runtime, retrieval server, compute).","avoid_when":"You need a simple plug-and-play API with strong built-in security guarantees, fine-grained auth, and clear rate-limit/error contracts; or you cannot manage the operational complexity of RL training and separate retrieval services.","last_evaluated":"2026-03-29T15:01:13.281558+00:00","has_mcp":false,"has_api":false,"auth_methods":[],"has_free_tier":false,"known_gotchas":["No standardized agent-friendly API surface for the RL framework itself is described; integration is primarily via scripts and training configs.","The retrieval/search component is a separate server; reliability and safety depend on how that server is implemented and deployed.","No explicit rate-limit or robust error-contract documentation is provided for the retrieval server endpoint(s) mentioned (e.g., /retrieve).","RL training workflows can be non-deterministic and sensitive to environment/versions/hyperparameters."],"error_quality":0.0}