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https://github.com/zenwayne/agentfusion

autogen with chainlit
https://github.com/zenwayne/agentfusion

agent autogen chainlit workflow

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autogen with chainlit

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README 

          
# AgentFusion



A comprehensive AI agent management platform that provides multi-agent orchestration, agent building tools, prompt version management, and real-time chat interface. AgentFusion enables you to create, configure, and deploy AI agents through individual agents, group chats, and graph flows with a complete backend infrastructure.



## 🌟 Features



- **Multi-Agent Orchestration**: Deploy individual agents, group chats, or complex graph flows

- **Database Infrastructure**: PostgreSQL with SQLAlchemy ORM for persistent data storage

- **User Authentication**: Complete user management with bcrypt password hashing and activity logging

- **Web Interface**: Chainlit-powered real-time chat interface with WebSocket support

- **MCP Integration**: Model Context Protocol support for external tool integration

- **Prompt Management**: Version-controlled prompt system with built-in optimization agents

- **Activity Logging**: Comprehensive audit trail for all user actions and system events

- **Flexible Configuration**: JSON-based configuration system for agents, workflows, and integrations



## 🚀 Quick Start



### 1. Installation



```bash

# Create and activate virtual environment using uv

uv venv

source .venv/bin/activate  # On Windows: .venv\Scripts\activate



# Install dependencies

uv pip install -r requirements.txt



# Install the package in development mode

cd python/packages/agent_fusion

uv pip install -e .

```



### 2. Database Setup



```bash

# Set up PostgreSQL database (production)

# Or use SQLite for testing - automatically configured



# Run database migrations if needed

# Database schema is located in sql/progresdb.sql

```



### 3. Environment Setup



Create a `.env` file in the project root with your API keys:



```env

DEEPSEEK_API_KEY=your_deepseek_api_key_here

DASHSCOPE_API_KEY=your_aliyun_api_key_here

GEMINI_API_KEY=your_google_api_key_here



# Database configuration (optional - defaults to SQLite for testing)

DATABASE_URL=postgresql://user:password@localhost/agentfusion

```



### 4. Launch Web Interface



```bash

chainlit run python/packages/agent_fusion/src/chainlit_web/run.py

```



The web interface will be available at `http://localhost:8000`



## 📋 Project Structure



```

AgentFusion/

├── config/                          # Configuration files

│   ├── prompt/                      # Agent prompts organized by type

│   │   ├── agent/                   # Individual agent prompts

│   │   ├── group_chat/              # Group chat selectors

│   │   └── ui_design/               # UI design prompts

│   └── mem/                         # Memory configurations

├── python/packages/agent_fusion/    # Main Python package

│   └── src/

│       ├── data_layer/              # Database layer with SQLAlchemy ORM

│       │   ├── models/              # Business logic models

│       │   └── tables/              # Database table definitions

│       ├── schemas/                 # Pydantic data models

│       ├── builders/                # Core builders for agents/workflows

│       ├── chainlit_web/            # Web interface with user authentication

│       │   ├── user/                # User management and authentication

│       │   └── ui_hook/             # UI integration hooks

│       ├── model_client/            # Model client implementations

│       ├── base/                    # Base utilities and MCP support

│       ├── tools/                   # Agent tools and utilities

│       └── dump/                    # Configuration export utilities

├── sql/                            # Database schema and migration scripts

├── config.json                     # Main configuration file

├── dumped_config/                  # Exported configurations

├── CLAUDE.md                       # Project memory and guidelines

└── requirements.txt                # Python dependencies

```



## 🏗️ Architecture



### Technology Stack

- **Backend**: Python 3.11+ with FastAPI + Chainlit

- **Database**: PostgreSQL (production) / SQLite (testing)

- **ORM**: SQLAlchemy 2.0 with async support

- **Authentication**: Custom user authentication with bcrypt

- **Agent Framework**: AutoGen AgentChat

- **Frontend**: Chainlit with real-time WebSocket connections



### Database Architecture

- **User Management**: User accounts, authentication, and activity logging

- **Agent System**: Agent configurations and model clients

- **Chat System**: Threads, steps, elements, and feedback

- **Prompt Management**: Prompts with version control

- **Audit Trail**: Comprehensive activity logging with JSONB metadata



## 🤖 Agent Types



### Individual Agents

- **file_system**: File and directory operations via MCP

- **product_manager**: Product requirement documentation

- **prompt_refiner**: Prompt optimization and refinement

- **executor**: Task execution specialist

- **template_extractor**: Extract parameters from prompt templates

- **prompt_specialization**: Interactive prompt customization



### Group Chats

- **prompt_flow**: Collaborative prompt development workflow

- **hil**: Human-in-the-loop product management



### Graph Flows

- **prompt_specialization**: Template extraction → customization → execution workflow



## ⚙️ Configuration



### Agent Configuration



Agents are defined in `config.json` under the `agents` section:



```json

{

  "agents": {

    "your_agent": {

      "name": "your_agent",

      "description": "Agent description",

      "labels": ["tag1", "tag2"],

      "type": "assistant_agent",

      "prompt_path": "agent/your_prompt.md",

      "model_client": "deepseek-chat_DeepSeek",

      "mcp_tools": ["file_system"]

    }

  }

}

```



### Group Chat Configuration



```json

{

  "group_chats": {

    "your_group": {

      "name": "your_group",

      "description": "Group description",

      "type": "selector_group_chat",

      "selector_prompt": "group_chat/your_selector.md",

      "model_client": "deepseek-chat_DeepSeek",

      "participants": ["agent1", "agent2", "human_proxy"]

    }

  }

}

```



### Graph Flow Configuration



```json

{

  "graph_flows": {

    "your_flow": {

      "name": "your_flow",

      "description": "Workflow description",

      "type": "graph_flow",

      "participants": ["agent1", "agent2"],

      "nodes": [

        ["agent1", "agent2"],

        ["agent2", {"condition": "agent1"}]

      ],

      "start_node": "agent1"

    }

  }

}

```



## 🔧 Usage Examples



### Export Configuration



Use the dump utilities to export your configuration:



```python

from dump import dump_agents, dump_group_chats



# Export specific components

dump_agents(["file_system"], "dumped_config")

dump_group_chats(["prompt_flow"], "dumped_config")

```



## 🛠️ Development



### Database Development

```bash

# Run tests

python -m pytest python/packages/agent_fusion/tests/ -v



# Test specific model

python -m pytest python/packages/agent_fusion/tests/test_user_model.py -v

```



### Adding New Agents



1. Create a prompt file in `config/prompt/agent/`

2. Add agent configuration to `config.json`

3. Update database models if needed (data_layer/models/)

4. Add tests for new functionality

5. Test via the web interface



### Database Migrations



1. Update SQL schema in `sql/progresdb.sql`

2. Update SQLAlchemy models in `data_layer/models/tables/`

3. Update business logic models in `data_layer/models/`

4. Add comprehensive tests



### Custom MCP Tools



Define MCP servers in the `mcpServers` section of `config.json`:



```json

{

  "mcpServers": {

    "your_tool": {

      "command": "your_command",

      "args": ["arg1", "arg2"],

      "env": {},

      "read_timeout_seconds": 30

    }

  }

}

```



## 🤝 Contributing



1. Fork the repository

2. Create a feature branch: `git checkout -b feature/your-feature`

3. Make your changes

4. Update tests and documentation

5. Submit a pull request



## 📄 License



MIT License - see LICENSE file for details



## 🙏 Acknowledgments



Built on top of [AutoGen](https://github.com/microsoft/autogen) - a powerful framework for multi-agent AI applications. Special thanks to the AutoGen team for their pioneering work in multi-agent orchestration.



## 📞 Support



- Create an [issue](https://github.com/your-repo/issues) for bug reports or feature requests

- Check the documentation in the `config/prompt/` directory for prompt examples

- Review `config.json` for configuration patterns