openharness provides lightweight agent infrastructure that gives large language models tool-use, skills, memory, and multi-agent coordination so they can operate as functional assistants over long sessions. The project addresses the gap between raw model capability and the practical infrastructure needed for sustained agentic workflows.
Project overview
It bundles the core building blocks an LLM needs to function as a persistent agent—tools, skills, memory, and multi-agent coordination—into lightweight infrastructure that is self-hosted and local-runnable.
Project type
MCP · AI Agent
Use cases
Coding & Development
Deployment
Refer to project documentation
License
MIT
Best for
Developers who need an LLM-based agent with tool-use, skills, memory, and multi-agent coordination for long-session workflows
Key capabilities
Previews runtime settings, auth state, skills, and tools without executing the model or tools.
Includes an MCP client with HTTP transport and auto-reconnect.
Limitations and risks
On Windows PowerShell, use `openh` instead of `oh` because `oh` can resolve to the built-in `Out-Host` alias.
Getting started
Install with pip using `pip install openharness-ai`. An install script and an interactive setup wizard are also available; the setup process involves selecting and authenticating with a model provider such as Claude, OpenAI, or Ollama.
Evidence and sources
GitHub project description: OpenHarness: Open Agent Harness with a Built-in Personal Agent--Ohmo!
README: openharness/
README: An **Agent Harness** is the complete infrastructure that wraps around an LLM to make it a functional agent.