OpenAgent
VerifiedOpen-source AI assistant blending LLMs, RAG, and autonomous agents into one binary.
What is OpenAgent?
OpenAgent is an open-source personal AI assistant that integrates large language models, retrieval-augmented generation from user documents, and autonomous agent loops in a single self-hostable package. It supports connections to over thirty model providers and allows agents to perform real-world actions such as web browsing and code execution through compatible tools.
The system runs as a standalone binary on Linux, macOS, and Windows, with optional Docker deployment. Users install via a simple script, access a web interface on port 14000, and configure models, knowledge bases, and agent behaviors without additional dependencies in most cases.
It targets individuals and developers seeking a private, customizable AI assistant that handles research, automation, and coding tasks while keeping data under local control.
What you can build with OpenAgent
Web Research Automation
Agents navigate websites, gather information, and summarize findings using built-in browser capabilities.
Code Generation and Execution
Users direct agents to write, run, and debug code snippets across supported languages and environments.
Personal Knowledge Management
Upload documents to create a RAG index that agents reference during conversations for accurate, context-aware responses.
Install OpenAgent
curl -fsSL https://raw.githubusercontent.com/the-open-agent/openagent/master/scripts/install.sh | bashcurl -fsSL https://raw.githubusercontent.com/the-open-agent/openagent/master/scripts/install.sh | bash- 1Download and run the install script for your platform to fetch the latest binary.
- 2Execute the binary, which starts the server on port 14000 by default.
- 3Open http://localhost:14000 in a browser to access the interface.
- 4Connect at least one model provider through the settings panel.
- 5Upload documents or configure tools to begin using agent features.
OpenAgent: pros & cons
Pros
- +Single binary deployment with no extra installation steps required on supported OSes.
- +Broad support for 30+ LLM providers and easy switching between models.
- +Built-in capabilities for browser control, code execution, and custom tool integration.
- +Self-hostable with usage analytics and activity monitoring included.
Cons
- –Source build requires Go 1.25+ and Node.js 20+ with Yarn.
- –Windows support is native but some advanced features may need additional configuration.
- –Agent loops depend on external model quality and MCP-compatible tools.
Frequently asked questions
No, pre-built binaries run directly on Linux, macOS, and Windows without Docker or WSL.
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