best-of-Agent-Harnesses
by RyanAlberts
🏆 Curated, ranked list of AI agent harnesses (100+) — plus an MCP server, llms.txt & JSON so agents can recommend them too. Rescored weekly.
845stars
47forks
Python
Added 6/14/2026
⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
AI Agentsagent-frameworkagent-harnessagentic-aiagentsai-agentai-agent-frameworkai-agentsai-toolsawesome-listbest-of-listclaude-codecoding-agentsharnessharness-engineeringllmllm-agentsllm-frameworkllm-toolsmcpmodel-context-protocol
Installation
# Add to your Claude Code skills
git clone https://github.com/RyanAlberts/best-of-Agent-HarnessesGetting Started
Guides for using ai agents skills like best-of-Agent-Harnesses.
- Caveman: Cut Claude Token Use by 65%How agent-side prompt compression works, when to use it, and when not to.
- What is an AI Skills Marketplace?Definitions, how marketplaces work, and how to choose between them in 2026.
- Getting Started with AI SkillsFirst-time install walkthrough for Claude Code, Codex CLI, and ChatGPT.
Security ReportVerified
Last scanned: 6/14/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-06-14T08:16:38.877Z",
"npmAuditRan": true,
"pipAuditRan": true,
"promptInjectionRan": true
}Compare with similar skills
See how best-of-Agent-Harnesses compares with popular alternatives.
What is an agent harness?
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.
Why harnesses matter
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, lifecycle, guardrails) and by stars/activity.
The benchmark data now backs this up. On SWE-bench Pro, "swapping the agent harness changed pass@1 more than many model upgrades do" (AINews, Aug 8 2026, citing analysis by @joelniklaus). Same model, different harness: 23% to 52% pass@1 on GLM-5.2, and 15% to 36% on Gemma 4 26B. Harness rankings barely transfer across models (rank correlation -0.05), so a small model in the right harness can approach a much larger model in the wrong one.
That is the problem the MCP server in this repo solves. Point your agent at it and it can call recommend or pick_harness to choose a harness matched to your model and task, instead of inheriting whichever harness someone else benchmarked.
The landscape at a glance
Every project in the list, plotted by adoption surface area (the simplicity ↔ capability axis) against GitHub stars. Colors are categories; the largest projects in each tier are labeled.
The same projects placed by how much unsupervised rope they're designed to give (autonomy) and what happens when a run dies (recovery). In the tables below, ★ marks headless-ready projects and ✱ marks durable ones. Both charts regenerate from the list data on every refresh.
How to Pick a Harness
Start with the guide, then the head-to-head decision pages — grounded in the same data as the tables below:
- How to pick a harness: six questions that turn this list into a decision, plus the chart to internalize first (the harness moves scores more than the model)
- How to test-drive a harness: the two-week trial protocol, with a fair setup, tasks from your own repos, seven measurements, and the walk-away test
- OpenClaw vs Hermes — the always-on personal-agent debate: presence vs discipline, plus what the field reports actually say
- Terminal coding agents — opencode vs Codex vs Gemini CLI vs crush vs goose
- Multi-agent orchestration — OpenAI Agents SDK vs CrewAI vs AutoGen vs LangGraph
- Agent memory layers — Mem0 vs Letta vs claude-mem
- Agent sandboxing: what it is, the key concepts, and the field (E2B vs Daytona vs Modal and more)
- Agent evals (SWE-bench vs inspect_ai vs Terminal-Bench)
- Eval and observability platforms (Langfuse vs LangSmith vs Braintrust vs Phoenix)
- Browser agents (browser-use vs Stagehand vs Playwright MCP vs chrome-devtools-mcp)
- Browser infrastructure (Browserbase vs Steel vs Hyperbrowser)
- Claude Code skill packs (superpowers vs GStack vs get-shit-done vs Anthropic Skills)
- Context files for agents (AGENTS.md vs CLAUDE.md vs skills vs MCP tool search)
Pick by use case
Reader's index: pick by what you want to do, not by category. Tag chips (e.g. mcp · memory) next to each row let you cross-filter by capability — see TAGS.md for the full cross-reference.
- I want a turnkey coding agent today — opencode, Cline, Codex, Gemini CLI, OpenHands, crush · see Coding agent products (IDEs, CLIs, full suites)
- I want an always-on personal agent that lives in my chat apps — OpenClaw, Hermes, Khoj, Agent Zero, OpenHarness (HKUDS) · see Personal agent runtimes
- I want to extend Claude Code, Codex, or OpenCode with skills and slash commands — Anthropic Skills, wshobson/agents, superpowers, GStack, pmstack · see Coding harness configs and SDKs
- I want to build my own coding harness from scratch — Claude Agent SDK, Google ADK, AutoHarness, SWE-agent, RepoMaster, claw-code-agent · see Coding harness configs and SDKs
- I want a drop-in memory layer for agents — Mem0, Graphiti (Zep), claude-mem, agentlog, letta · see Plugins, MCPs, CLI tools
- I want to plug hundreds to thousands of tools without context bloat — MCP-Zero, ToolGen, ToolRAG, langgraph-bigtool · see Progressive disclosure harnesses
- I want multi-agent orchestration — openai-agents-python, crewAI, autogen, Microsoft Agent Framework, PraisonAI, agent-squad · see [Multi-agent and orchestration](#multi-agent-a
Frequently Asked Questions
What is best-of-Agent-Harnesses?
best-of-Agent-Harnesses is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by RyanAlberts. 🏆 Curated, ranked list of AI agent harnesses (100+) — plus an MCP server, llms.txt & JSON so agents can recommend them too. Rescored weekly. It has 845 GitHub stars.
Is best-of-Agent-Harnesses safe to use?
Yes. best-of-Agent-Harnesses passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
How do I install best-of-Agent-Harnesses?
Clone the repository with "git clone https://github.com/RyanAlberts/best-of-Agent-Harnesses" and add it to your Claude Code skills directory (see the Installation section above).
What programming language is best-of-Agent-Harnesses written in?
best-of-Agent-Harnesses is primarily written in Python. It is open-source under RyanAlberts on GitHub, so you can review or fork the full source.
Are there alternatives to best-of-Agent-Harnesses?
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh best-of-Agent-Harnesses against similar tools.
Comments (0)
to leave a comment.
No comments yet. Be the first to share your thoughts!
Related Skills
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
256,673
38,416
JavaScript
AI Agentsai-agentsanthropic
Skills for Real Engineers. Straight from my .agents directory.
250,518
21,171
Shell
AI Agents
An agentic skills framework & software development methodology that works.
234,966
20,863
Shell
AI Agentsaibrainstorming
Fair-code workflow automation platform with native AI capabilities. Combine visual building with custom code, self-host or cloud, 400+ integrations.
204,055
60,641
TypeScript
MCP Serversaiapis
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
185,940
28,768
JavaScript
AI Agentsai-agentsanthropic