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LambChat

LambChat — A pluggable, multi-tenant AI conversation system. Skills + MCP dual-engine driven, modular by design. SSE streaming, multi-modal document processing, JWT/RBAC auth, and GitHub-synced Skills.

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🐑 LambChat

An open-source, production-ready AI Agent platform for building, running, and sharing real tool-using agents

English · 简体中文 · Documentation · Contributing

📸 Screenshots


Login	Chat	Streaming
Skills	MCP Config	Share
Roles	Settings	Feedback
Mobile	Tablet	Shared Session

🌟 Why LambChat

LambChat is built for teams who want more than a chatbot UI. It gives you a complete AI Agent system with model management, MCP connectivity, skills, storage, sharing, approvals, and deployment-ready backend/frontend infrastructure in one project.

Built for real execution — agents can reason, call tools, use sub-agents, stream progress, and work with human approval when needed.
Ready for operations — includes auth, RBAC, encrypted secrets, tracing, health checks, sandbox integration, and distributed config sync.
Designed for extensibility — custom agents, MCP tools, skills, model providers, channels, persona presets, and storage backends can all be extended cleanly.
Product-grade UX — polished chat UI, file previews, project folders, sharing, feedback, responsive layouts, and multilingual support.

🎬 Use Cases

#	Case	Description	Demo
1	Supply Chain PDF Report	Generates a polished PDF efficiency report with charts, benchmark comparisons, and delivery, inventory, fulfillment, and logistics analysis from a single prompt.	View Session
2	Godfather Fan Website	Builds a responsive English promo site for The Godfather trilogy with a cinematic visual direction, marquee hero section, generated images, and multi-device polish.	View Session
3	Story Breakdown from Image	Understands visual input, identifies the stories shown in an image, and produces detailed plot-by-plot explanations with multimodal reasoning.	View Session
4	EV Market Trend Analysis	Turns recent 2025-2026 electric vehicle data into a structured market analysis covering growth, regional performance, and key industry takeaways.	View Session

🏗️ Architecture

Architecture

✨ Features

🤖 Agent Runtime

deepagents Architecture — Compiled graph runtime with fine-grained state management
Multi-Agent Types — Core, fast, and search agents
Plugin System — @register_agent("id") decorator for custom agents
Streaming Output — Native SSE support
Sub-agents — Multi-level delegation
Thinking Mode — Extended thinking for Anthropic models
Human-in-the-Loop — Approval system with countdown timer, auto-extension, and urgent-state styling
Persona Presets — Reusable persona configuration with permissions and runtime binding

🧠 Model, Memory, and Skills

Multi-Provider Models — OpenAI, Anthropic, Google Gemini, and Kimi
Full CRUD — Create, edit, delete, reorder, and batch import models via UI
Channel Routing — Route the same model through different channels with model_id
Role-based Access — MODEL_ADMIN permission and per-role model visibility
Cross-session Memory — Native MongoDB-backed memory system
Dual Skills Storage — File system plus MongoDB backup
GitHub Sync — Import custom skills from GitHub
Skill Marketplace — Browse, install, publish, and manage skills in bulk

🔌 Tools, MCP, and Execution

System + User MCP — Global and per-user MCP configuration
Encrypted Storage — API keys encrypted at rest
Dynamic Tool Caching — Cache MCP tools with manual refresh
Multiple Transports — SSE and HTTP
Permission Control — Transport-level access policies
Sandbox Integration — Daytona and E2B execution support
Built-in Tools — File reveal, project reveal, upload URL, env vars, audio transcription, persona preset tools, and more

📁 Product Features

File Library — Browse revealed files with code preview, favorites, and project-based filtering
Rich Previews — PDF, Word, Excel, PPT, Markdown, Mermaid, Excalidraw, images, and video playback
Project Folders — Organize sessions into projects with drag-and-drop
Session Sharing — Generate public share links for conversations
Feedback — Thumbs rating, text comments, session linking, and run-level stats
Notifications — In-app notification storage and delivery hooks

🔐 Infra, Realtime, and Frontend

Realtime — Redis + MongoDB dual-write, WebSocket, auto-reconnect, and shared-session updates
Security — JWT, RBAC, bcrypt, OAuth (Google/GitHub/Apple), email verification, CAPTCHA, and sandbox controls
Observability — LangSmith tracing, structured logging, health checks, and distributed memory diagnostics
Channels — Native Feishu integration plus an extensible multi-channel architecture
Frontend Stack — React 19, Vite 6, TailwindCSS 3.4, dark/light theme, rich content rendering, and responsive multi-device layouts
i18n — English, Chinese, Japanese, Korean, and Russian

⚙️ Configuration

Multiple setting categories can be configured through the UI or environment variables:

Category	Description
Frontend	Default agent, welcome suggestions, UI preferences
Agent	Debug mode, logging level
Model	Multi-provider model management, per-model config, channel routing
Session	Session management, message history, SSE cache
Database	MongoDB connection, optional PostgreSQL
Storage	Persistent storage, S3/OSS/MinIO/COS
Security	Encryption and security policies
Sandbox	Code sandbox settings (Daytona / E2B)
Skills	Skill system config
Tools	Tool system settings
Tracing	LangSmith and tracing
User	User management, registration, default role
Memory	Memory system (native)

🚀 Quick Start

Prerequisites

Python 3.12+ · Node.js 18+ · pnpm · MongoDB · Redis

Setup

git clone https://github.com/Yanyutin753/LambChat.git
cd LambChat

# Docker (recommended)
cd deploy && cp .env.example .env   # Edit with your config
docker compose up -d

# Or local development
cp .env.example .env   # Edit with your config
make install-pnpm      # Install pnpm if not present
make install && make dev

📝 Required Configuration

Edit the .env file with the following recommended settings:

# Recommended: Set a stable JWT secret (auto-generated on each restart if unset, invalidating existing sessions)
JWT_SECRET_KEY=your-stable-secret-key

# Recommended: Set MCP encryption salt (auto-generated on each restart if unset, invalidating saved MCP configs)
MCP_ENCRYPTION_SALT=your-stable-encryption-salt

# Optional: Configure MongoDB connection
MONGODB_URL=mongodb://localhost:27017
MONGODB_DB=agent_state
MONGODB_USERNAME=admin
MONGODB_PASSWORD=your-mongo-password

# Optional: Configure Redis connection
REDIS_URL=redis://localhost:6379/0
REDIS_PASSWORD=your-redis-password

::: tip LLM models are configured through the Model Config UI after deployment — no environment variables needed. :::

Open http://localhost:8000

Code Quality

make format       # Format (ruff format)
make lint         # Lint (ruff check)
make typecheck    # Type check (mypy)
make check-all    # Run all checks (lint + typecheck + test)

Project Structure

src/
├── agents/         # Agent implementations and runtime graphs
├── api/            # FastAPI routes, admin endpoints, middleware
├── infra/          # Core services: auth, llm, mcp, tools, storage, tasks, sharing, memory
├── kernel/         # Schemas, config, constants, and shared types
└── skills/         # Built-in skills
frontend/
├── src/components/ # UI components, panels, and landing sections
├── src/hooks/      # Frontend hooks
├── src/i18n/       # Locale files
└── src/styles/     # Shared styles and design tokens
tests/              # Backend and integration tests
deploy/             # Docker deployment assets

⭐ Star History

📄 License

MIT — Project name "LambChat" and its logo may not be changed or removed.

_{LambChat is built for people who want AI agents that can actually do the work.}

Created by Clivia

GitHub · Email · 中文 README

_{WeChat for deployment help, product feedback, and collaboration}