Lobe Chat

Active
GitHub TypeScript NOASSERTION

Description

Lobe Chat is an open-source ChatGPT-style chat application with a plugin system and multi-model support, suitable as an agent conversation interface.

Key Features

  • Agents as the unit of work - Organize workflows around agents, supporting hiring, scheduling, and managing entire AI teams
  • Unified intelligence - Seamlessly access any model and modality with full user control over model selection and switching
  • 10,000+ skill ecosystem - Over ten thousand tools and MCP-compatible plugins connecting agents to everyday tools
  • Agent group collaboration - Multi-agent parallel collaboration with automatic assembly of suitable agents for complex tasks
  • IM gateway integration - Use agents directly within existing instant messaging tools without switching windows
  • Pages collaborative editing - Multiple agents co-write and refine content in one place with shared context

Use Cases

💡 Personal AI team management, organizing specialized agents into a 24/7 operating AI workforce
💡 Content creation workflows with multi-agent collaboration for writing, editing, translation, and review tasks
💡 Enterprise intelligent assistant deployment via IM gateway integration into Slack, Discord, and team messaging tools
💡 Research and analysis tasks using agent groups and 10,000+ skills for information gathering, analysis, and report generation
💡 Personal lifestyle automation, building agent teams to manage schedules, health, learning, and daily affairs

Strengths & Limitations

Strengths

  • Actively maintained, recent updates
  • High community interest (81.8k stars)
  • Established track record (3 years in production)
  • Responsive to issues, low backlog

⚠️ Limitations

  • No clear open-source license

Categories

Quick Start

# Deploy with Docker
docker run -d \
  --name lobe-chat \
  -p 3210:3210 \
  -e OPENAI_API_KEY=sk-xxxx \
  lobehub/lobe-chat

# Or deploy with Vercel (one-click)
# Visit https://vercel.com/new and import lobehub/lobe-chat repo

# Open http://localhost:3210 in browser
# Configure LLM provider and API key in settings to start using

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