OpenAgent: A Modular AI Agent Framework Supporting 10+ Large Model Providers

OpenAgent is an open-source AI Agent framework maintained by cheche089 (GitHub link: https://github.com/cheche089/agent-framework). Its core features include: lightweight modular design, support for over 10 mainstream large language model providers (such as OpenAI, DeepSeek, Tongyi Qianwen, etc.), built-in tools and skill management system, web search functionality, Docker sandbox for secure execution, desktop-grade Web UI, local deployment and operation, and local data storage to ensure privacy.

With the improvement of large language model (LLM) capabilities, AI Agent development has become a popular direction. However, existing frameworks have problems such as complexity and difficulty in local deployment, or single functionality and lack of scalability. OpenAgent was born to provide a lightweight, modular, multi-LLM supported framework to help developers quickly build autonomous AI systems.

OpenAgent adopts a modular design, with key modules including:

1. Multi-provider LLM support (over 10 mainstream vendors);
2. Streaming dialogue and WebSocket real-time response;
3. Built-in tools (file reading/writing, Shell execution, DuckDuckGo web search) and skill management system;
4. Docker sandbox for secure execution;
5. Workflow engine (supports complex processes) and memory system (TF-IDF semantic retrieval + LLM summarization).

Deployment requires Python 3.10+. Steps are as follows:

1. Clone the repository: git clone https://github.com/cheche089/agent-framework.git
2. Install dependencies: cd agent-framework && pip install httpx fastapi uvicorn websockets
3. Configure LLM API keys (e.g., OpenAI/DeepSeek): export OPENAI_API_KEY="sk-xxx"
4. Start the Web UI: python web_ui/main.py, then visit http://127.0.0.1:8080 to use it.

OpenAgent applicable scenarios include:

• Personal knowledge management assistant: Read local documents, extract information, and maintain knowledge;
• Automated workflow orchestration: Monitor web page changes → extract information → generate reports → send emails;
• Multi-step code generation and verification: Generate code → execute in Docker sandbox → error analysis and iteration.

Compared to similar projects, OpenAgent's advantages are:

• Localization first: Data stored locally, API keys not uploaded to the cloud;
• Developer-friendly UI: Codex-style dark theme;
• Conversation history management: Supports saving, browsing, and restoring chat records;
• Fine-grained parameter control: Adjustable temperature and token parameters.

OpenAgent is currently in the early development stage. Features to be improved include: plugin system, file upload preview, code interpreter sandbox, custom skill writing, multi-user support, and mobile adaptation. Enterprise-level multi-tenant deployment will need to wait for subsequent versions.

OpenAgent provides developers with a choice that balances feature richness and deployment simplicity, suitable for individual developers, small teams, and data privacy-sensitive scenarios. With the enrichment of the skill ecosystem and functional iterations, it is expected to become a strong competitor in the open-source Agent framework field.