LibreChat is a self-hosted, multi-tenant AI chat platform that unifies access to major LLM providers (OpenAI, Anthropic, Google, AWS Bedrock, Azure, and custom OpenAI-compatible endpoints) through a single web interface. The architecture is a monolithic Express.js/Node.js backend serving a React (Vite) SPA, backed by MongoDB (Mongoose), Redis for caching/rate-limiting, MeiliSearch for full-text search, and PostgreSQL (pgvector) for RAG embeddings. The codebase is organized as a monorepo with shared packages: `data-schemas` (Mongoose models/methods with multi-tenant isolation), `data-provider` (Zod schemas, API types, React Query hooks), `api` (MCP management, streaming, auth, SSRF protection), and `client` (reusable React component library). Key data flows include: user requests hitting Express routes, middleware performing JWT/OAuth/LDAP/SAML auth and RBAC checks, agent orchestration via `@librechat/agents` with tool execution (MCP, code interpreter, web search), and SSE streaming with resumable stream support via Redis-backed job management. The system implements a sophisticated multi-tenant isolation layer using Mongoose plugins (`tenantIsolation`) that automatically scope queries by tenant, an ACL/permissions system with bit-flag-based permission checks, and SSRF protection with DNS resolution validation for MCP server URLs. The MCP (Model Context Protocol) subsystem is particularly well-developed, featuring OAuth flow management with reconnection tracking, tool discovery, and both stdio and HTTP/SSE transport support. The streaming architecture supports horizontal scaling with Redis-backed event transport and job stores, enabling resumable streams across multiple server instances.
This repository is primarily a large collection of GPT prompt templates (markdown files) designed for use with AI chat platforms like FlowGPT or gptcall.net. Each prompt file defines a specific role or task for the AI, such as a resume editor, coding tutor, fitness planner, or creative writing assistant. The repository also includes an Android application (OpenClaw and Codex) referenced in the README, along with Node.js scripts (update.mjs, db.mjs) for managing the prompt data. The prompts are structured with placeholders for user input and often request specific output formats, enabling users to import them into chat interfaces to obtain specialized AI behavior without crafting prompts from scratch. While the prompt library is extensive, it contains significant duplication, minimal-effort entries, and some policy-risking content (e.g., jailbreaks, NSFW role-play), which affects overall quality and reuse potential.