{"id":"agentailor-fullstack-langgraph-nextjs-agent","name":"fullstack-langgraph-nextjs-agent","af_score":48.8,"security_score":46.2,"reliability_score":26.2,"what_it_does":"A production-oriented Next.js/TypeScript template for building LangGraph.js-based AI agents with dynamic tool loading via Model Context Protocol (MCP), optional human-in-the-loop tool approval, persistent thread-based conversation memory backed by PostgreSQL (LangGraph checkpointer), and real-time streaming responses via SSE. It also includes multimodal file upload/storage using S3-compatible backends (e.g., MinIO for dev).","best_when":"You want a self-hosted starter that combines agent orchestration (LangGraph), dynamic tool wiring (MCP), approval gating, persistent memory (Postgres), and a streaming web interface (Next.js/SSE).","avoid_when":"You cannot afford the operational/security overhead of securely deploying MCP servers and controlling tool execution, or you require turnkey governance features that aren’t documented here.","last_evaluated":"2026-03-30T13:48:21.097179+00:00","has_mcp":true,"has_api":true,"auth_methods":["OpenAI API key / Google AI API key (for model provider access)","Optional OAuth2/Bearer token for HTTP-based MCP servers (as noted in docs)","No end-user authentication/authorization mechanism described in provided README"],"has_free_tier":false,"known_gotchas":["Dynamic tool loading increases attack surface: tools and parameters may be user-configured or externally hosted via MCP; needs careful allowlisting and validation.","Human-in-the-loop approval can introduce UX latency and require robust state management for streaming interruptions.","Tool execution retries (if implemented) must be coordinated with non-idempotent tools to avoid side effects.","SSE streaming and interrupted connections can leave partial outputs unless carefully handled."],"error_quality":0.0}