🦐 Xiajiao IM

Your AI Agent Team — One npm start Away

A lightweight, self-hosted, open-source platform to run AI agents as a team — group chat, @mention routing, tool calling, persistent memory, and collaboration flows.
No Docker. No PostgreSQL. No Redis. Just npm start.

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Quick Start · Why Xiajiao · Features · Docs · Changelog · Contributing

@mention an agent → Get a real-time response → Hand off to another agent — all in one chat.

🎬 Watch it in action (35s demo)

One message triggers an entire agent collaboration chain — writing, editing, and translating in real time.

📸 More screenshots

Agent writes a poem	Another agent translates it
Tool Calling in action	Collaboration flow panel
Agent contacts	Clean login

Xiajiao (虾饺) — named after the Cantonese shrimp dumpling: small, delicate, but packed with flavor.
Minimal dependencies, maximum capability.

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⚡ Quick Start

Option 1: npm (recommended)

Prerequisite: Node.js >= 22.0.0

git clone https://github.com/moziio/xiajiao.git
cd xiajiao && npm install
npm start

Option 2: Docker

git clone https://github.com/moziio/xiajiao.git
cd xiajiao
docker build -t xiajiao .
docker run -d -p 18800:18800 \
  -v xiajiao-data:/app/data \
  -v xiajiao-uploads:/app/public/uploads \
  --name xiajiao xiajiao

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Open http://localhost:18800 → Log in with default password admin → Settings → Models to add your API key → Start chatting!

[!TIP] Password and API keys can all be changed in the web UI — no config files to edit.

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🤔 Why Xiajiao?

Most AI platforms require Docker + PostgreSQL + Redis + vector databases just to get started.
Xiajiao takes a radically different approach — it's the only IM platform where your agents actually collaborate as a team:

Who is this for?

• Solo developers who want an AI team without the DevOps overhead
• AI enthusiasts exploring multi-agent collaboration patterns
• Small teams needing a self-hosted AI workspace with zero vendor lock-in
• Researchers prototyping agent-to-agent communication and memory systems

	Xiajiao	Dify	Coze	FastGPT
Get started	npm start	docker compose up	Sign up for SaaS	docker compose up
External services	None	PostgreSQL + Redis + Weaviate	—	MongoDB + MySQL
npm dependencies	6	100+	—	80+
Multi-agent group chat	✅	❌	❌	❌
Inter-agent collaboration	✅ chains + visual flow	Workflow	Bot orchestration	❌
Persistent agent memory	✅ 3-class memory	❌	Variables	❌
Tool calling	✅ 7 built-in tools	✅	✅ 100+ plugins	✅
RAG knowledge base	✅ BM25 + vector hybrid	✅	✅	✅

The core difference: Dify and Coze are AI app development platforms. Xiajiao is an AI Agent team collaboration platform — treat agents as teammates, not tools.

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✨ Features

	Feature	Highlight
🤖	Multi-Agent Group Chat	@mention routing, agent-to-agent dialogue
🔧	Tool Calling	7 built-in tools, extensible
🧠	Persistent Memory	3-class memory, auto prompt injection
📚	RAG Knowledge Base	BM25 + vector hybrid, LLM reranking
🔗	Collaboration Flows	Chains, visual panel, workflow engine
🔌	Multi-Model	OpenAI / Claude / Qwen / Ollama / ...

🤖 Multi-Agent Group Chat

Create groups, add agents, use @mentions to route messages. Agents can talk to each other, hand off tasks, and collaborate — just like a real team.

🔧 Tool Calling

Agents don't just chat — they take action. 7 built-in tools with an extensible architecture:

Tool	What it does
web_search	Search the web (6 engines: auto / DuckDuckGo / Brave / Kimi / Perplexity / Grok)
rag_query	Semantic retrieval from your knowledge base
memory_write	Save to persistent memory
memory_search	Recall from past memories
call_agent	Invoke another agent (3-level nesting protection)
manage_channel	Create, start, stop external platform connectors (Feishu / DingTalk / WeCom / Telegram)
manage_schedule	Create and manage cron-driven scheduled tasks

Agents call memory_search to recall context, then memory_write to persist insights — all visible in real time.

🧠 Persistent Agent Memory

A 3-class memory system that makes agents smarter over time:

• Semantic memory — facts & knowledge ("User prefers Python")
• Episodic memory — conversation events ("We discussed deployment last time")
• Procedural memory — behavioral patterns ("Keep replies concise")

Embedding-based dedup + hybrid search + automatic prompt injection.

📚 Production-Grade RAG

Upload documents — agents auto-index and auto-retrieve:

• BM25 + vector hybrid retrieval
• Reciprocal Rank Fusion (RRF)
• LLM reranking
• Hierarchical chunking (200-char small chunks + 800-char large chunks)

🔗 Collaboration Flows

• Collaboration chains — agents relay within a group; one's output becomes the next's input
• Visual flow panel — real-time status, history replay, human intervention (approve / stop / edit)
• Workflow engine — multi-step, conditional branching, error handling (retry / skip / rollback), human approval

Visual flow panel: Novelist → Editor → Translator → Coder, with real-time progress and human intervention controls.

🔌 Multi-Model Support

Works with any OpenAI-compatible API provider:

OpenAI (GPT-4o) · Anthropic (Claude) · Qwen (通义) · GLM · Kimi · MiniMax · DeepSeek · Ollama

📋 And More

Capability	Description
Markdown rendering	Code highlighting + Mermaid diagrams + LaTeX
Full-text search	SQLite FTS5 message search
Scheduled tasks	Cron-driven, agents report on schedule
AI image generation	DashScope integration, auto-detects drawing intent
PWA offline	Service Worker + offline page
i18n	Chinese & English built-in
RBAC	owner > admin > member > guest
Security	CSRF + rate limiting + error sanitization + token revocation
MCP protocol	Model Context Protocol support
Channel connectors	Feishu (Lark) WebSocket + Webhook

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🏗️ Tech Stack

Layer	Technology	Why
Runtime	Node.js 22+	Native node:sqlite, zero native addons
HTTP	node:http	Zero framework overhead
WebSocket	ws	Real-time bidirectional communication
Database	SQLite (WAL + FTS5)	One .db file handles everything
Frontend	Vanilla JS + CSS	No build step — edit, refresh, done
Dependencies	6 total	ws · formidable · node-cron · pdf-parse · @larksuiteoapi/node-sdk · @modelcontextprotocol/sdk
Tests	53 unit tests	node:test + node:assert, zero test framework

Why so few? We treat every dependency as a liability, not a feature. If the standard library can do it, we don't add a package.

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⚙️ Configuration

Environment Variables

Variable	Description	Default
IM_PORT	Server port	18800
OWNER_KEY	Admin password	admin
LLM_MODE	LLM mode (direct / gateway)	direct

Model Configuration

cp models.example.json models.json

{
  "providers": {
    "openai": {
      "baseUrl": "https://api.openai.com/v1",
      "apiKey": "sk-your-api-key",
      "api": "openai-completions"
    }
  },
  "models": [
    {
      "id": "openai/gpt-4o",
      "name": "gpt-4o",
      "provider": "openai",
      "reasoning": false,
      "input": ["text", "image"],
      "contextWindow": 128000,
      "maxTokens": 4096
    }
  ]
}

Supported API types: openai-completions (Qwen / Kimi / GLM / DeepSeek / Ollama) · anthropic-messages · dashscope-image

[!TIP] You can also add models directly in the web UI under Settings → Models — no need to edit files manually.

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📁 Project Structure

xiajiao/
├── server/                   # Backend
│   ├── index.js              # HTTP + WebSocket entry
│   ├── middleware/            # Auth, logging, rate limiting
│   ├── routes/               # API routes
│   ├── services/             # Core services
│   │   ├── llm.js            # LLM + Tool Calling loop
│   │   ├── rag.js            # RAG retrieval engine
│   │   ├── memory.js         # Agent memory
│   │   ├── workflow.js       # Workflow engine
│   │   ├── collab-flow.js    # Collaboration flow state machine
│   │   ├── tool-registry.js  # Tool registry
│   │   └── database.js       # SQLite + migrations
│   ├── migrations/           # Database migrations
│   └── tests/                # Unit tests
├── public/                   # Frontend (Vanilla JS, no build step)
│   ├── js/                   # Feature modules
│   └── css/
├── data/_soul-templates/     # Default agent SOUL.md templates
├── models.example.json       # Model config example
├── im-settings.example.json  # System settings example
└── agents.example.json       # Agent config example (5 built-in agents)

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🗺️ Roadmap

🚀 Xiajiao is under active development. Here's what's shipped and what's coming:

Phase	Status	What
P1 Core IM	✅ Shipped	Conversations, group chat, @mention, Markdown
P2 Agent Collaboration	✅ Shipped	Workflows, scheduled meetings, community event stream
P3 Advanced Capabilities	✅ Shipped	Tool Calling, RAG, Agent memory, image gen, SQLite
P4 Reliability	✅ Shipped	Security hardening, rate limiting, RBAC, PWA, structured logging
P5 Channel System	🚧 Building	WeCom, Feishu (Lark), DingTalk, Telegram
P6 Enterprise	📋 Planned	Multi-process, external state, Electron desktop app
P7 Plugin Ecosystem	💭 Exploring	Plugin marketplace, community tools, one-click install
P8 Agent Store	💭 Exploring	Pre-built agent templates, one-click clone & customize
P9 Multimodal	💭 Exploring	Voice input, image understanding, video analysis
P10 Agent Negotiation	💭 Exploring	Structured multi-round dialogue between agents with turn limits

This is just the beginning. Got ideas? Open an Issue or join the discussion.

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🤝 Contributing

We welcome all contributions — bug reports, feature ideas, code, docs, and translations.

See CONTRIBUTING.md for details.

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⭐ Show Your Support

[!IMPORTANT] Star this repo — you'll receive notifications for every new release, and it helps others discover the project!

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🔒 Security

To protect users, please do not post security issues publicly on GitHub Issues. Instead, please email security concerns to the maintainers directly. We will respond promptly.

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📄 License

MIT

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Xiajiao (虾饺) — Your AI Agent team manager 🦐
_{Small, delicate, packed with flavor.}