best-of-Agent-Harnesses

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<h1 align="center">
    Best of Agent Harnesses and Harness Techniques
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<p align="center">
    <strong>🏆&nbsp; Curated list of AI agent harnesses, orchestration frameworks, and harness techniques for reliable agentic systems.</strong>
</p>

<p align="center">
    <a href="https://best-of.org" title="Best-of Badge"><img src="http://bit.ly/3o3EHNN"></a>
    <a href="#contents" title="Project Count"><img src="https://img.shields.io/badge/projects-110-blue.svg?color=5ac4bf"></a>
    <a href="#contribution" title="Contributions welcome"><img src="https://img.shields.io/badge/contributions-welcome-green.svg"></a>
    <a href="https://github.com/RyanAlberts/best-of-Agent-Harnesses/releases" title="Updates"><img src="https://img.shields.io/github/release-date/RyanAlberts/best-of-Agent-Harnesses?color=green&label=updated"></a>
</p>

## 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.

Every prior wave of automation was constrained by brittleness: you scripted exact behavior, and when the world deviated, the system broke. Foundation models inverted that problem—they're flexible but directionless, stateless, and disconnected from anything real. The agent harness exists to bridge that gap: it is the orchestration infrastructure that converts a model's per-turn reasoning into sustained, tool-using, error-recovering, goal-directed behavior across time. Architecturally, it plays the role the kernel played in operating systems or the controller played in industrial robotics—mediating between raw capability and a messy environment—but with a critical difference: the "capability" it governs is general-purpose cognition, which means the harness is simultaneously a scheduler, a permission system, a memory manager, and a policy enforcement layer, all under-specified and evolving in real time.

## 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 landscape at a glance

[![The Agent Harness Landscape — all projects plotted by adoption surface area against GitHub stars](assets/landscape.svg)](assets/landscape.svg)

_Every project in the list, plotted by adoption surface area (the [simplicity ↔ capability axis](#guide-to-rankings)) against GitHub stars. Colors are categories; the largest projects in each tier are labeled._

[![Autonomy × Recovery — every loop-owning project placed by designed autonomy regime and failure-recovery tier](assets/axes-grid.svg)](assets/axes-grid.svg)

_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**](comparisons/how-to-pick-a-harness.md) — six questions that turn this list into a decision, including the post–June 2026 billing reality
- [**OpenClaw vs Hermes**](comparisons/openclaw-vs-hermes.md) — 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](comparisons/terminal-coding-agents.md)
- [**Multi-agent orchestration** — OpenAI Agents SDK vs CrewAI vs AutoGen vs LangGraph](comparisons/multi-agent-orchestration.md)
- [**Agent memory layers** — Mem0 vs Letta vs claude-mem](comparisons/memory-layers.md)

## Pick by use case

_Reader's index: pick by what you want to do, not by category. Tag chips (e.g. <sup>`mcp` · `memory`</sup>) next to each row let you cross-filter by capability — see [TAGS.md](TAGS.md) for the full cross-reference._

- **I want a turnkey coding agent today** — [opencode](https://github.com/anomalyco/opencode), [Cline](https://github.com/cline/cline), [Codex](https://github.com/openai/codex), [Gemini CLI](https://github.com/google-gemini/gemini-cli), [OpenHands](https://github.com/OpenHands/OpenHands), [crush](https://github.com/charmbracelet/crush), [Roo Code](https://github.com/RooCodeInc/Roo-Code) · see [Coding agent products (IDEs, CLIs, full suites)](#coding-agent-products-ides-clis-full-suites)
- **I want an always-on personal agent that lives in my chat apps** — [OpenClaw](https://github.com/openclaw/openclaw), [Hermes](https://github.com/NousResearch/hermes-agent), [Khoj](https://github.com/khoj-ai/khoj), [Agent Zero](https://github.com/agent0ai/agent-zero), [OpenHarness (HKUDS)](https://github.com/HKUDS/OpenHarness) · see [Personal agent runtimes](#personal-agent-runtimes)
- **I want to extend Claude Code, Codex, or OpenCode with skills and slash commands** — [Anthropic Skills](https://github.com/anthropics/skills), [everything-claude-code](https://github.com/affaan-m/everything-claude-code), [superpowers](https://github.com/obra/superpowers), [GStack](https://github.com/garrytan/gstack), [pmstack](https://github.com/RyanAlberts/pmstack) · see [Coding harness configs and SDKs](#coding-harness-configs-and-sdks)
- **I want to build my own coding harness from scratch** — [Claude Agent SDK](https://github.com/anthropics/claude-agent-sdk-python), [Google ADK](https://github.com/google/adk-python), [AutoHarness](https://github.com/aiming-lab/AutoHarness), [SWE-agent](https://github.com/SWE-agent/SWE-agent), [RepoMaster](https://github.com/QuantaAlpha/RepoMaster), [claw-code-agent](https://github.com/HarnessLab/claw-code-agent) · see [Coding harness configs and SDKs](#coding-harness-configs-and-sdks)
- **I want a drop-in memory layer for agents** — [Mem0](https://github.com/mem0ai/mem0), [claude-mem](https://github.com/thedotmack/claude-mem), [agentlog](https://github.com/RyanAlberts/agentlog), [agno](https://github.com/agno-agi/agno), [letta](https://github.com/letta-ai/letta) · see [Plugins, MCPs, CLI tools](#plugins-mcps-cli-tools)
- **I want to plug hundreds to thousands of tools without context bloat** — [MCP-Zero](https://github.com/xfey/MCP-Zero), [ToolGen](https://github.com/Reason-Wang/ToolGen), [ToolRAG](https://github.com/antl3x/ToolRAG), [langgraph-bigtool](https://github.com/langchain-ai/langgraph-bigtool), [spring-ai-tool-search-tool](https://github.com/spring-ai-community/spring-ai-tool-search-tool) · see [Progressive disclosure harnesses](#progressive-disclosure-harnesses)
- **I want multi-agent orchestration** — [openai-agents-python](https://github.com/openai/openai-agents-python), [crewAI](https://github.com/crewAIInc/crewAI), [autogen](https://github.com/microsoft/autogen), [Microsoft Agent Framework](https://github.com/microsoft/agent-framework), [PraisonAI](https://github.com/MervinPraison/PraisonAI), [agent-squad](https://github.com/2FastLabs/agent-squad) · see [Multi-agent and orchestration](#multi-agent-and-orchestration)
- **I want a general LLM app framework** — [langgraph](https://github.com/langchain-ai/langgraph), [langchain](https://github.com/langchain-ai/langchain), [llama-index](https://github.com/run-llama/llama_index), [pydantic-ai](https://github.com/pydantic/pydantic-ai), [agno](https://github.com/agno-agi/agno) · see [Frameworks](#frameworks)
- **I want low-code / visual workflows** — [langflow](https://github.com/langflow-ai/langflow), [Flowise](https://github.com/FlowiseAI/Flowise), [Dify](https://github.com/langgenius/dify), [n8n](https://github.com/n8n-io/n8n) · see [Frameworks](#frameworks)
- **I want browser-using agents** — [browser-use](https://github.com/browser-use/browser-use), [WebVoyager](https://github.com/MinorJerry/WebVoyager), [puppeteer-real-browser-mcp](https://github.com/withLinda/puppeteer-real-browser-mcp-server) · see [Plugins, MCPs, CLI tools](#plugins-mcps-cli-tools)
- **I want sandboxed code execution for agent-generated code** — [E2B](https://github.com/e2b-dev/E2B), [Daytona](https://github.com/daytonaio/daytona), [smolagents](https://github.com/huggingface/smolagents), [OpenHands](https://github.com/OpenHands/OpenHands) · see [Libraries and SDKs](#libraries-and-sdks)
- **I want to evaluate or benchmark agents** — [SWE-bench](https://github.com/SWE-bench/SWE-bench), [AgencyBench](https://github.com/GAIR-NLP/AgencyBench), [inspect_ai](https://github.com/UKGovernmentBEIS/inspect_ai), [WebArena](https://github.com/web-arena-x/webarena), [ARC-AGI-2](https://github.com/arcprize/ARC-AGI-2), [VitaBench](https://github.com/meituan-longcat/vitabench) · see [Evaluation and benchmarking harnesses](#evaluation-and-benchmarking-harnesses)
- **I want a deep research / autonomous research agent** — [deepagents](https://github.com/langchain-ai/deepagents), [gpt-researcher](https://github.com/assafelovic/gpt-researcher), [openagents](https://github.com/OpenAgentsInc/openagents) · see [Research and task-specific harnesses](#research-and-task-specific-harnesses)
- **I want a provider-agnostic LLM pipe (not a framework)** — [LiteLLM](https://github.com/BerriAI/litellm), [vercel/ai](https://github.com/vercel/ai) · see [Libraries and SDKs](#libraries-and-sdks)

## For agents

This list is also published in machine-readable form, so coding agents and research agents can recommend harnesses — not just humans browsing GitHub:

- [**harnesses.json**](harnesses.json) — every project with category, complexity tier, capability tags, stars, license signal, and a concrete example link, plus the full use-case index.
- [**llms.txt**](llms.txt) — the entire list in one agent-readable file. Point any agent at