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Agent Harnesses

Query a curated catalog of AI agent harnesses, orchestration frameworks, techniques, and decision guides.

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Install Agent Harnesses

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "agent-harnesses": {
      "command": "uvx",
      "args": [
        "agent-harnesses-mcp"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

We couldn’t confirm it starts correctly

The install command started, but didn't respond the way we expected when we tried to talk to it.

uvx agent-harnesses-mcp

initialize succeeded but no response to tools/list.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 1mo ago.

Overview

The agent-harnesses MCP server lets agents query a curated catalog of AI agent harnesses, orchestration frameworks, and harness techniques. It provides recommendation-oriented operations such as `recommend` and `pick_harness`, using list data organized around model and task fit. Reach for it when an agent needs to compare harness options or select one instead of relying on an arbitrary default.

Use cases

  • Compare agent harnesses for a coding workflow
  • Recommend a harness for a selected model and task
  • Filter projects by autonomy and recovery behavior
  • Research orchestration, memory, browser, or evaluation options

Key features

  • MCP-accessible harness catalog
  • Harness recommendations
  • Model- and task-oriented selection
  • Capability, autonomy, and recovery filtering
  • Comparison guides across agent categories
  • llms.txt and JSON data exports

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

54/58 checks healthy over the last 92d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 7d ago via OSV.dev · agent-harnesses-mcp (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars1.5kStargazers on the source repository.
Last commit2d agoMost recent push to the default branch.
Install checkInconclusiveStarted but did not complete the MCP handshake in our test.
Directory activity5 viewsConfig copies, upvotes, and views on AllMCPs.

How Agent Harnesses works

What agent-harnesses MCP server does

The agent-harnesses MCP server exposes a curated reference list of AI agent harnesses, orchestration frameworks, and harness techniques to MCP-compatible agents. The underlying project also publishes a searchable website, an llms.txt file, and JSON data for other forms of access. Its catalog covers projects concerned with agent environments, orchestration, lifecycle management, guardrails, memory, browser use, evaluation, sandboxing, and related areas described in the repository.

The list is intended to support decisions rather than provide a runtime for executing agents. It combines project listings with rankings, capability-oriented filtering, and comparison material. The repository describes the data as refreshed with weekly rescoring, while the displayed project count may change as the list is updated.

How it works

An agent connects to the MCP server and queries the catalog through recommendation functions.

Recommendations are meant to account for the model and task being considered. The project specifically presents harness selection as a pairing decision: a harness that performs well with one model may not rank the same way with another. The surrounding guides help turn the catalog into a more detailed choice using factors such as autonomy, recovery behavior, environment, orchestration, lifecycle, and guardrails.

Setup and configuration

Refer to the repository's current agent-facing instructions before configuring a client.

The same catalog is also available through llms.txt and harnesses.json.

Tools and capabilities

The agent-harnesses MCP server supports these documented capabilities:

  • Query the curated harness catalog from an agent.
  • Request a harness recommendation with recommend.
  • Select a harness with pick_harness.
  • Use information organized around harness capabilities and decision criteria.
  • Consult comparisons covering coding agents, multi-agent orchestration, memory layers, sandboxed execution, browser agents, evaluations, observability, and context files.

The repository also provides a searchable web presentation with one page per harness and filters for capability, autonomy, and recovery.

Limitations and notes

The agent-harnesses MCP server catalogs and recommends projects; it is not described as an execution layer for those projects. It also does not replace testing a candidate harness in the target environment. The repository includes a test-drive guide with a trial process and measurements, indicating that recommendations should be validated against the user's own tasks.

Ranking results should not be treated as universal or permanent. The project notes that harness rankings can vary by model, so changing the model may require revisiting the choice. Counts, scores, and recommendations can also change as the list is refreshed.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Agent Harnesses

Connect an agent to the server and use the documented `recommend` or `pick_harness` operations to query the curated list and select a candidate based on the model and task.

It recommends AI agent harnesses, orchestration frameworks, and harness techniques from the curated project list.

Yes.

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