Agent-harness picks, decision guides, setup templates, and skills; live GitHub/HN discovery.
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Best Of Agent Harnesses is an MCP server that agent-harness picks, decision guides, setup templates, and skills; live GitHub/HN discovery. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 91/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"best-of-agent-harnesses": {
"args": [
"agent-harnesses-mcp"
],
"command": "uvx"
}
}
}Are you the author?
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Agent-harness picks, decision guides, setup templates, and skills; live GitHub/HN discovery.
This server supports HTTP transport. Be the first to test it - help the community know if it works.
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No known CVEs.
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🏆 Curated list of AI agent harnesses, orchestration frameworks, and harness techniques for reliable agentic systems.
🌐 Browse the searchable site — one page per harness, filter by capability, autonomy & recovery.
📰 This week: new harnesses and the biggest star gains, every Sunday. To get it as a GitHub notification, click Watch, then Custom, then Releases.
🧰 Templates and Playbooks: copy-paste setup files and step-by-step guides for the harnesses in this list.
🧩 Skills: 10 installable skills that check your own agent setup, including what it can reach, what it claims, and what it spends.
🤖 Agents can query this list — an MCP server (recommend, pick_harness, …), llms.txt & JSON, so your agent recommends harnesses too.
A model answers; an agent acts. An agent harness is the runtime that turns one into the other: the model thinks, the harness decides what that thinking is allowed to touch.
Simon Willison's definition of the agent itself is the cleanest: "an LLM agent runs tools in a loop to achieve a goal." The harness is everything around that loop: which tools exist, what needs approval, what the model sees each turn, what survives a crash. Andrej Karpathy named the architecture back in 2023: the model is "the kernel process of a new Operating System", and the harness is the rest of that OS, its scheduler, permissions, and memory. The SWE-agent paper proved the stakes by coining the agent-computer interface: how tools and feedback are presented changes what a model can do, independent of the model. The field's advice has since converged on investing here rather than in framework plumbing, from Anthropic's build-simple guidance to Jerry Liu's argument that the framework era is over and the layers that matter now are skills, tools, and context quality. Those are the layers this list catalogs.
Better models make harnesses more important: more capabilities mean more failure modes, and production needs retry logic, fallbacks, and validation. Harness quality, not just model quality, determines whether agents actually ship. This list ranks projects by relevance to harness concerns (environment, orchestration,