dingtalk-agent-client

# Dingtalk Agent Development SDK Supports integrating various MCP-Server to quickly develop Dingtalk AI assistants  ## Overview This project provides a powerful foundation for building AI assistants on Dingtalk, leveraging 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 and can extend more 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 receiving and processing system - **MCP Tool Integration**: Implements employee query function through Dingtalk API - **Extensible Architecture**: Easy to add new Agents or features ## Architecture The system consists of the following key components: - **Streaming Client Manager**: Manages WebSocket connections to the Dingtalk streaming API - **Message Processor**: Processes 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 - Get the Client_ID, Client_Secret of the AI assistant  - Activate the call permission of the basic interface   - api-key for LLM calls (defaults to Bailian's qwen-max service, 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 Start the application using the following command: ```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 organization API 2. Use MCP-tool to query employee information 3. Build dynamic instructions based on user context 4. Process and return results in a conversational manner ## Docker Deployment You can also run the application using Docker: ```bash docker-compose up -d ``` ## Extending the Framework Creating a new Agent: 1. Create a new Agent file in the `app/agent` directory 2. Define the Agent's instructions and tools 3. Register the Agent with the AgentManager 4. Update message processing to route appropriate requests to your Agent