dingtalk-agent-sdk

# DingTalk Agent Development SDK Supports the integration of various MCP-Server for rapid development of DingTalk AI assistants  ## Overview This project provides a strong foundation for building AI assistants on DingTalk, utilizing the following technologies: - OpenAI Agent SDK provides AI capabilities and reasoning functions - DingTalk Streaming API enables real-time message processing - MCP-server integrates access to DingTalk organizational data, allowing for expanded capabilities - Employee information query Agent example demonstrates platform capabilities ## Key Features - **OpenAI Agent Integration**: Seamless integration with OpenAI's Agent framework - **DingTalk Streaming Client**: Implements robust connection retry and health monitoring mechanisms - **Message Processing Pipeline**: Well-structured message reception and processing system - **MCP Tool Integration**: Employee query functionality via DingTalk API - **Scalable Architecture**: Easy to add new Agents or features ## Architecture The system consists of the following key components: - **Streaming Client Manager**: Manages WebSocket connections to DingTalk Streaming API - **Message Processor**: Handles incoming messages and routes them to the appropriate Agent - **Agent Manager**: Manages OpenAI Agents and tool integrations - **Employee Agent**: An example Agent that can query employee information ## Prerequisites - Python 3.10+ - DingTalk developer account and corresponding permissions - Obtain the Client_ID and Client_Secret for the AI assistant  - Enable access permissions for basic API calls   - API key for LLM calls (default uses the Qwen-max service from Bai Lian, configurable) ## Installation 1. Clone the repository: ```bash git clone git@github.com:darrenyao/dingtalk-agent-client.git cd DingtalkAgentClient ``` 2. Install dependencies: ```bash pip install -r requirements.txt ``` 3. Create a `.env` file containing the configuration: ``` # DingTalk API Configuration DINGTALK_CLIENT_ID=your_client_id DINGTALK_CLIENT_SECRET=your_client_secret # LLM API Configuration LLM_API_KEY=your_llm_api_key LLM_API_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1 # or your custom endpoint LLM_API_MODEL=qwen-max # or your preferred model ``` ## Running the Application Use the following command to start the application: ```bash python main.py ``` The system will: 1. Initialize the Agent with the necessary tools 2. Connect to DingTalk's Streaming API 3. Listen for incoming messages 4. Process messages through the appropriate Agent ## Employee Agent Example The included `employee_agent.py` demonstrates how to: 1. Create an Agent integrated with the DingTalk organizational API 2. Use the MCP-tool to query employee information 3. Build dynamic instructions based on user context 4. Handle and return results in a conversational format ## Docker Deployment You can also run the application using Docker: ```bash docker-compose up -d ``` ## Extending the Framework To create a new Agent: 1. Create a new Agent file in the `app/agent` directory 2. Define the instructions and tools for the Agent 3. Register the Agent with the AgentManager 4. Update message processing to route appropriate requests to your Agent