{"id":"langroid-langroid","name":"langroid","af_score":60.2,"security_score":51.5,"reliability_score":47.5,"what_it_does":"Langroid is a Python multi-agent framework for building LLM-powered applications. It provides agent/task abstractions where agents collaborate via message exchange, with optional components like LLM backends, tools/functions, and vector stores, plus support for structured extraction/RAG patterns and MCP server tool adaptation.","best_when":"You want flexible multi-agent workflows in Python with pluggable LLM/tool/vector-store components, including MCP tool support and structured extraction patterns.","avoid_when":"You need a standardized REST/GraphQL API surface with OpenAPI specs or managed authentication for end users; Langroid is primarily an SDK used inside your application runtime.","last_evaluated":"2026-03-29T15:02:51.673823+00:00","has_mcp":true,"has_api":false,"auth_methods":["API keys for underlying LLM providers (e.g., OpenAI/OpenAI-compatible, Groq, Cerebras, etc., configured via provider SDKs)","OAuth flows may be supported indirectly via requests-oauthlib and provider integrations (not evidenced as a first-class Langroid auth layer)"],"has_free_tier":false,"known_gotchas":["LLM-driven tool calling may fail when the model does not invoke tools as expected (there is mention of configuration for handling such cases, but behavior may still vary by model/provider).","Multi-agent loops can produce non-terminating or long-running conversations if termination conditions are misconfigured.","Tool results may be large; truncation is supported but incorrect settings can degrade extraction/answers.","Security boundaries around tool execution are on the integrator; MCP/tool servers may expose powerful operations."],"error_quality":0.0}