OHarness

Local-first agent harness that works with any AI model provider

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Screenshot of OHarness, Local-first agent harness that works with any AI model provider

What is OHarness?

OHarness is a transparent, local-first agent harness with no model lock-in. It switches between Anthropic, OpenAI, Gemini, DeepSeek and local runtimes mid-session, with tools, MCP, hooks and permissions kept inspectable across CLI, browser and desktop.

OHarness is an agent harness: the layer that runs the agent loop, tool calls, permissions, memory and task history around a language model. Its central design choice is that the model is a replaceable backend. Anthropic, OpenAI, Google Gemini and DeepSeek are supported natively, along with Ollama, LM Studio, vLLM and any OpenAI-compatible endpoint, and the homepage counts 34 providers in total. The /model command changes the model during a session, while provider-neutral history, tool results, permissions, memory and artifacts stay with the harness instead of being converted. The project is aimed at developers and teams who want to see what an agent is doing instead of trusting opaque autonomy. Sessions, memory, artifacts, audit records and credentials are kept as readable files on the user's own machine. While work runs, tool calls, file changes, permissions, usage and cost can be inspected. Tools are defined with zod, which generates JSON Schema and validates arguments. A built-in MCP client covers stdio and Streamable HTTP without an MCP SDK. Permissions follow a deny-beats-allow model with five modes, and scheduled runs use an injected prompt so they never wait on a person. Subagents work in isolated contexts and return conclusions rather than full transcripts. In practice, OHarness installs as an npm CLI (`npm install -g oharness`) that runs on macOS, Linux and Windows with Node 22.6 or newer. A browser frontend and a desktop app drive the same engine, so one session can be continued across terminal, browser and desktop. The desktop build is currently published for macOS on Apple silicon. Using a personal provider key or a local model needs no account, and requests go directly from the machine to the chosen provider. An optional hosted plan routes traffic through a metering gateway and bills model usage in credits. Among alternatives, the homepage positions OHarness against Claude Code: a similar architecture of agent loop, tools, permissions, MCP and subagents, but with provider choice and extra frontends. The npm packages are MIT licensed, though the source repository is private and open sourcing has no announced date.

How do you use OHarness?

  1. 1Install the CLI or desktop app
    Run npm install -g oharness on a machine with Node 22.6 or newer, or download the desktop build, which is published for macOS on Apple silicon.
    OHarness — Install the CLI or desktop app
  2. 2Connect a provider
    Add an API key for Anthropic, OpenAI, Gemini or DeepSeek, or point the harness at a local Ollama, LM Studio or vLLM server. Model lists are fetched from each vendor.
    OHarness — Connect a provider
  3. 3Start a session and set permissions
    Open a session in the terminal, browser or desktop frontend and choose a permission mode so tool calls and file changes follow rules you can inspect.
    OHarness — Start a session and set permissions
  4. 4Switch models mid-session
    Use the /model command to change provider or model while history, tool results, memory and artifacts stay with the harness.
  5. 5Extend with tools and MCP servers
    Define custom tools with zod or connect MCP servers over stdio or Streamable HTTP, then use the guides for deeper setup and common questions.
    OHarness — Extend with tools and MCP servers

Pros and cons

Pros

  • Switch between hosted and local models mid-session without rebuilding the task or converting historyAI
  • Sessions, memory, audit records and credentials stay in readable files on the user's machineAI
  • Built-in MCP client, zod-defined tools, subagents and a deny-beats-allow permission systemAI
  • Same engine behind terminal, browser and desktop, so sessions carry across all threeAI
  • Free with a personal provider key or local model, and no account is requiredAI

Cons

  • Source repository is private; only the npm packages are MIT licensed and open sourcing has no dateAI
  • Desktop app is published only for macOS on Apple silicon, so Windows users rely on the CLIAI
  • Requires Node 22.6 or newer and comfort with a CLI-centered, developer-oriented workflowAI
  • Bringing your own provider keys means managing costs and rate limits across vendors yourselfAI
  • The optional hosted plan sends requests through the vendor's metering gateway, weakening the local-only privacy storyAI

How much does OHarness cost?

Pricing

The harness is free with your own provider key or a local model and needs no account. An optional hosted plan bills model usage in credits; no credit prices are listed on the homepage.

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Support

Documentation and guides are published on the site, with a common-questions section on the homepage. No dedicated support channel is described.

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Integrations

Works with Anthropic, OpenAI, Google Gemini, DeepSeek, Kimi, GLM, Qwen and OpenRouter, plus Ollama, LM Studio, vLLM and any OpenAI-compatible endpoint. MCP servers connect over stdio or Streamable HTTP.

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Features

Native support for 34 providers including Anthropic, OpenAI, Gemini and local runtimes; zod-defined tool calling; built-in MCP client; safe-point context compaction with resumable sessions; five permission modes; parallel subagents; CLI, browser and desktop frontends.

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Frequently asked questions about OHarness

  • How much does OHarness cost?
    The harness is free with your own provider key or a local model and needs no account. An optional hosted plan bills model usage in credits; no credit prices are listed on the homepage.
  • How do you use OHarness?
    The walkthrough on this page covers 5 steps: 1. Install the CLI or desktop app 2. Connect a provider 3. Start a session and set permissions 4. Switch models mid-session 5. Extend with tools and MCP servers.
  • What platforms does OHarness support?
    OHarness is available on MacOS, Windows, Linux and Web App.
  • What does OHarness integrate with?
    Works with Anthropic, OpenAI, Google Gemini, DeepSeek, Kimi, GLM, Qwen and OpenRouter, plus Ollama, LM Studio, vLLM and any OpenAI-compatible endpoint. MCP servers connect over stdio or Streamable HTTP.
  • What are the limitations of OHarness?
    Source repository is private; only the npm packages are MIT licensed and open sourcing has no date. Desktop app is published only for macOS on Apple silicon, so Windows users rely on the CLI. Requires Node 22.6 or newer and comfort with a CLI-centered, developer-oriented workflow.

Status

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Added10/18/2026

Platforms

MacOSWindowsLinuxWeb App

Pricing

FreeFreemium