Best Of Agent Harnesses

RyanAlberts1.4kstars

🏆 Ranked list of 187 AI agent harnesses, plus templates, playbooks, MCP, and learning resources. Rescored weekly.

1# Best Of Agent Harnesses

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Last checked: 2026-10-27

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How do I install Best Of Agent Harnesses?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

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Last checked 2026-10-27. 🏆 Ranked list of 187 AI agent harnesses, plus templates, playbooks, MCP, and learning resources. Rescored weekly.

From the publisher

Documentation

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.

The gap is widest on long-horizon work. A bare frontier model was verified at about 30% on ARC-AGI-3; Prime Agent's harness took Opus 5 to 95.5%, and the YC Paper Club talk on why the harness matters more than the model (September 2026) walks through how. The gains come from a new class of self-improving harnesses, not from piling on scaffolding: the best of them stay thin and expose only what the model cannot do for itself. And because rankings barely transfer across models, the harness choice is a pairing that must be re-asked whenever the model changes. Who says so, what they measured, and what the claim does not mean is its own page.

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:

Why the harness matters more than the model: the measurements (ARC-AGI-3 30% to 95.5% on the same weights), who says so, and what the claim does not mean
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
The best AI agent harnesses in 2026, ranked by category: the top three per category from this week's data, regenerated on every refresh
OpenClaw vs Hermes — the always-on personal-agent debate: presence vs discipline, plus what the field reports actually say
Managed vs self-hosted always-on agents: Grok Bot, Claude Managed Agents, QM, OpenClaw, Hermes, and OpenJarvis; who owns the computer and who pays for idle time
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)

Templates and Playbooks

The list tells you which harness to use. These tell you how to set it up. Templates are files you copy into your project; Playbooks walk you through one task, step by step.

Templates

One AGENTS.md for every coding agent: One briefing file that Codex, Claude Code, Cursor, OpenCode, GitHub Copilot, Gemini CLI, and Aider all read, so you write your build commands, conventions, and hard rules once instead of once per tool.
Safe Claude Code settings: permissions and a guard hook: Project settings that stop Claude Code from force-pushing, wiping work, reading secrets, or piping downloads into a shell, while leaving everyday commands alone. Two files, copy and commit.
Minimal agent harness in Python: A working coding-agent harness in about 180 lines of Python: the loop, three tools, a permission gate, a context file, a turn budget, and a transcript you can resume after a crash. Copy it to learn how harnesses work, or as the start of your own.

Playbooks

Build your own agent harness: Build a working coding agent in about an hour: a loop, three tools, permissions, a context file, a budget, and crash recovery, in one Python file you fully understand. You finish with the minimal harness template running on your own repo.
Write one AGENTS.md for every coding agent: Write one briefing file that Codex, Claude Code, Cursor, OpenCode, Copilot, Gemini CLI, and Aider all read, test that each tool actually loaded it, and keep it short enough to help instead of hurt. You finish with the AGENTS.md template filled in for your repo.

Skills

A skill is a folder your agent loads when a task calls for it. Each one below runs a script on your own setup and hands back a result you can check: what your agent can reach, which of its claims had no proof, where your tokens went. They run on your machine, and anything that changes a file or spends money waits for your yes.

Pick a harness

Test-drive coding agents on tasks from your own repo: Runs Claude Code, Codex, and Gemini CLI on tasks taken from your own git history, scores each one with your repository's tests, and reports how many tasks each passed, the dollars per pass, the minutes, and the size of each change.

Set it up

Check which instruction files your coding agents load: See which instruction files Claude Code, Codex, Gemini CLI, OpenCode, Cursor, GitHub Copilot, and Aider load from your repo, what each one cuts or skips, and whether the commands in those files still work.
Grade your MCP tools the way a model reads them: Point it at an MCP server or a saved tool list to grade each tool's purpose, parameters, annotations, and token size, or let it find the MCP servers configured across your coding agents and count what they load into every session.

Keep it safe

Test your agent's guardrails against dangerous commands: Checks whether the permission rules and hooks you already have in Claude Code, Codex, Gemini CLI, OpenCode, or Cursor stop about 90 dangerous commands, shows each one that gets through with a tested fix, and counts how often your rules interrupt real work.
Stop a runaway agent session: A hook for Claude Code and Codex that stops a live session when the agent repeats the same call, keeps failing, or spends past a dollar cap you set.
Check what your coding agent can reach: A one-minute probe that runs through your agent's own shell and shows which secret files, trust files, Docker, and sudo access it really has, then compares that with what your sandbox settings claim.

Check its work

Check your agent's 'tests pass' claims: Find out how often your coding agent said tests passed without a passing run to back it up, and whether the current change quietly weakened your tests. It reads your local session logs and sends nothing anywhere.
Turn the rules your agent breaks into hooks: Finds the rules in AGENTS.md, CLAUDE.md, and GEMINI.md that your coding agent breaks, counts every break in your recent sessions, and turns each broken rule into a hook (a check your agent runs before each tool call) that blocks it, tested against those real breaks.

See what it costs and what changed

Find the update where your coding agent started working differently: Splits your own Claude Code or Codex sessions by version, model, or week, measures how the agent works in each, and places the change at the update, or within the span of versions, where the numbers moved, along with anything else that changed then.
See where your coding agent wastes tokens and money: A report on your own Claude Code, Codex, Gemini CLI, and OpenCode sessions that shows which habits cost the most, such as re-read files, huge tool outputs, polling, and breaks that expire the prompt cache (the provider's cheap copy of the conversation), and which failures repeat, each with a fix.

Install one skill in any agent:

npx skills add https://github.com/RyanAlberts/best-of-Agent-Harnesses/tree/main/skills/<name>

Or all of them in Claude Code:

/plugin marketplace add RyanAlberts/best-of-Agent-Harnesses
/plugin install harness-skills@agent-harnesses

How the skills are built and tested.

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, Prime Agent · 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), QM, OpenJarvis · 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
I want a general LLM app framework — langgraph, langchain, llama-index, pydantic-ai, agno · see Frameworks
I want low-code / visual workflows — langflow, Dify, n8n · see Frameworks
I want browser-using agents — browser-use, Stagehand, WebVoyager, puppeteer-real-browser-mcp · see Plugins, MCPs, CLI tools
I want sandboxed code execution for agent-generated code — E2B, Agent Sandbox, smolagents, OpenHands · see Libraries and SDKs
I want to evaluate or benchmark agents — SWE-bench, Terminal-Bench, AgencyBench, inspect_ai, WebArena, VitaBench · see Evaluation and benchmarking harnesses
I want a deep research / autonomous research agent — deepagents, gpt-researcher, openagents · see Research and task-specific harnesses
I want a provider-agnostic LLM pipe (not a framework) — LiteLLM, vercel/ai · see 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 — every project with category, complexity tier, capability tags, stars, license signal, and a concrete example link, plus the full use-case index.
llms.txt — the entire list in one agent-readable file. Point any agent at the raw URL.
MCP server — recommend (one opinionated pick + alternatives + what to avoid, e.g. repos flagged for star manipulation), compare/compare_for (2–4 harnesses side by side — by id or by task — who leads on which axis incl. researched sandboxing/memory/hooks/prompt-optimization ratings, graveyard warnings, the matching decision guide), pick_harness (ranked, with complexity/autonomy/recovery filters), pick_infrastructure (picks at any level of the infra stack plus a live GitHub/Hacker News discovery pass, so answers aren't limited to this list), search_harnesses, get_harness, list_categories, whats_new (the weekly digest), plus list_comparisons/get_comparison for the decision guides and list_templates/get_template/list_playbooks/get_playbook/list_skills/get_skill so your agent can install a template or a skill for you. Published to PyPI and the official MCP registry as io.github.RyanAlberts/agent-harnesses. One-line install (needs uv):

claude mcp add agent-harnesses -- uvx agent-harnesses-mcp

Or hire a skeleton

Don't just read the list — agents/ ships three agent skeletons: open-source agents that run on the AI subscription you already pay for. Clone the file, customize the instructions, done. All three work against the current week's data and deliver to Slack or Notion when either is connected:

harness-scout — describe what you're building; it picks your harness, with evidence and a graveyard check.
stack-auditor — flags the harnesses in your codebase that died, and can trace your agent session logs to show how the harness steers your technical decisions.
harness-radar — weekly movement briefing: climbers, arrivals, deaths, graduations.

curl -fsSL https://raw.githubusercontent.com/RyanAlberts/best-of-Agent-Harnesses/main/agents/harness-scout.md -o .claude/agents/harness-scout.md

The landscape at a glance
How to Pick a Harness
Templates and Playbooks
Skills
Pick by use case
For agents: harnesses.json, llms.txt, MCP server, agent templates
FAQ
Progressive disclosure harnesses 8 projects
Coding agent products (IDEs, CLIs, full suites) 31 projects
Coding harness configs and SDKs 18 projects
Personal agent runtimes 14 projects
Frameworks 26 projects
Multi-agent and orchestration 22 projects
Plugins, MCPs, CLI tools 19 projects
Memory and state 6 projects
Evaluation and benchmarking harnesses 19 projects
Observability and eval-ops 4 projects
Research and task-specific harnesses 6 projects
Libraries and SDKs 14 projects

Guide to rankings

⭐ Stars — GitHub star count, captured 2026-10-25; tables sort by stars descending.
⚖️ Simplicity ↔ capability — adoption surface, 4 tiers: super simple (a format, one concept) → mostly simple (thin layer) → slightly complex (real SDK) → complex (product suite).
★ Headless-ready — designed for unattended runs, batches, and fleets (the top of the autonomy scale: step-gated → checkpoint-gated → bounded → headless).
✱ Durable — persisted execution state survives restarts mid-task (the top of the recovery scale: none → retry → resumable → durable).
archived upstream — the maintainers archived the repo, so it gets no new fixes. Kept in the table for reference, never in the use-case picks.
✅ Open source — ✅ standard OSS license · ⚠️ source-available/restricted · ❓ no or unclear license.
🏷️ Tags — capability chips auto-derived from descriptions; full cross-reference in TAGS.md.
🎯 Examples — one concrete "show me it in action" link per project, not a docs root.

Every project's full autonomy and recovery tier is plotted in the grid above and carried in harnesses.json and llms.txt; scores are editorial, from public docs — maintainer corrections via issue/PR are merged fast.

Progressive disclosure harnesses

Formats, runtimes, and patterns that reveal context, tools, or instructions in layers—index first, details on demand—to control tokens and improve agent focus (the "map, not encyclopedia" principle).

#	Project	⭐ Stars	Description	Open source	Simplicity ↔ capability	Examples
1	Headroom	77.8k	Compresses tool outputs, logs, files, and RAG chunks with content-aware compressors before they reach the model—claimed 20% fewer tokens for coding agents and 60–95% fewer for JSON, same answers. Ships as a library, HTTP proxy, or MCP server, so it drops in front of whatever harness you already run. `mcp` · `rag`	✅	mostly simple (compression library/proxy/MCP server)	Project README
2	awesome-cursorrules	41k	Curated .cursorrules and skills that leverage Cursor's index-then-load model; the canonical collection for rules-as-progressive-disclosure in the IDE. `ide`	✅	super simple (content bundle)	PyTorch cursorrules
3	context-mode	32.2k	Context-window optimization layer that sandboxes tool output before it reaches the model (claimed 98% reduction) and persists session memory across 17 agent platforms via MCP and hooks—progressive disclosure applied to tool results, not just instructions. `mcp` · `memory` · `sandbox`	⚠️ Elastic-2.0	mostly simple (output sandboxing, cross-platform)	Project README
4	agents.md	25.1k	Open format for repo-scoped agent briefings; nested AGENTS.md files scope instructions per directory, so agents get a map of what exists and load only what's relevant. Read by 20+ tools including Codex, Cursor, and Copilot. `ide` · `typescript`	✅	super simple (format only)	Self-hosting AGENTS.md
5	langgraph-bigtool ✱	559	Build LangGraph agents with large tool sets; retrieval and on-demand tool loading so agents scale beyond context without stuffing every schema upfront. `tool-discovery` · `python`	✅	slightly complex (large tool sets)	Math-library tool agent
6	MCP-Zero	517	Active tool discovery for autonomous agents: model requests tools by requirement; hierarchical semantic routing over 308 servers / 2,797 tools with ~98% token reduction (APIBank). `tool-discovery`	✅	complex (3k tools, full routing)	APIBank experiment
7	ToolGen	184	ICLR 2025: unified tool retrieval and calling via generation; 47k+ tools without context stuffing—retrieval and invocation in one generative step. `tool-discovery` · `python`	❓	complex (47k+ tools)	Full eval pipeline
8	ToolRAG	35	Semantic tool retrieval for LLMs; serves only the tools the user query demands (MCP-compatible), unlimited tool sets with zero context penalty. `mcp` · `tool-discovery`	✅	mostly simple (query-driven retrieval)	MCP server retrieval

Coding agent products (IDEs, CLIs, full suites)

Turnkey coding agents you install and run: IDE extensions, terminal CLIs, Dockerized workspaces. Each entry notes which part is the harness (the agent loop, tool wiring, approval model) versus the UI shell (VS Code extension, TUI, browser client).

#	Project	⭐ Stars	Description	Open source	Simplicity ↔ capability	Examples
1	DeepSeek Harness	261k	DeepSeek's official open-source agent harness (`dsh`), built so that everything is a plugin: the model adapter, tool registry, session log, sandbox and approval policy, and the agent loop itself are Cordis plugins you replace from configuration, and one plugin tree boots as a Web UI, a headless runner, a desktop app, an SDK server, or an ACP server. Developer preview, with breaking changes expected. `browser` · `sandbox` · `typescript`	✅	slightly complex (plugin-tree runtime, Web UI, headless, SDK)	Plugin architecture
2	opencode ★	221k	Open-source terminal coding agent (formerly `sst/opencode`; transferred to anomalyco). The harness is a multi-provider tool-call loop (Claude, OpenAI, Gemini, local) with strong plugin and MCP support; the TUI is the shell. 100% OSS, very actively shipped. `mcp` · `provider-agnostic` · `cli` · `tui` · `typescript`	✅	slightly complex (multi-provider, plugins, MCP)	Agent system page
3	Codex	131k	OpenAI's terminal coding agent. The harness is the sandboxed tool-call loop with multi-provider support; the CLI is the shell. Reference implementation for "official CLI that ships code." `sandbox` · `provider-agnostic` · `cli`	✅	slightly complex (reference CLI, sandboxed)	Sandboxing concept
4	pi	118k	The upstream AI agent toolkit behind this list's oh-my-pi fork: a unified multi-provider LLM API, agent loop, and TUI shell providing the harness that oh-my-pi's Rust rewrite builds on. `provider-agnostic` · `tui` · `rust`	❓	slightly complex (multi-provider agent loop, TUI)	Project README
5	Gemini CLI	108k	Google's first-party terminal agent for Gemini. The harness is the plugin/MCP tool-call loop; the terminal is the shell—Google's parallel to Claude Code / Codex, not just an API. `mcp` · `cli` · `typescript`	✅	slightly complex (official CLI, plugins, MCP)	MCP server setup
6	OpenHands ★	91.8k	Dockerized software-engineering agent. The harness is the bash/editor/browser toolset with micro-agents and event-stream session bridging; Docker is the sandbox. Main OSS choice for teams self-hosting autonomous repo work. `memory` · `browser` · `sandbox` · `python`	⚠️ (multi-license)	complex (Docker runtime, multi-surface agent — product suite)	Repository microagents
7	Cline	70.9k	VS Code extension whose harness is a plan-then-act loop with per-step human approval and cost transparency; the VS Code integration is the UI shell. Open-source counterweight to Cursor. `ide` · `typescript`	✅	slightly complex (plan-then-act, approval gates)	Plan & Act mode
8	Open Interpreter	68.7k	Lightweight terminal coding agent oriented to open models (DeepSeek, Kimi, Qwen). The harness is a code-execution loop — the model writes code, the harness executes it with confirmation gates; the CLI is the shell. The original "let the LLM run code on my machine" project, reborn for open weights. `cli` · `python`	✅	mostly simple (lean code-exec loop)	Quick start
9	goose ★	55.7k	Block-originated Rust agent, now stewarded by the Linux Foundation's Agentic AI Foundation (`aaif-goose/goose`). The harness is the MCP/ACP extension model with recipes and provider choice; there's no fixed UI slot—you bolt it into whatever shell you use. `mcp` · `rust`	✅	slightly complex (extensions, MCP/ACP)	Goose recipes guide
10	Codewhale	41.1k	Open-source Rust terminal coding agent for any model (40+ providers, local models included). The harness pairs a goal-driven work loop with permission modes (Ask, Auto-Review by a reviewer model, Full Access) and an optional workspace-write sandbox, and `/receipts` lists every file it changed, command it ran, and web or MCP call it made; the TUI is the shell. `mcp` · `sandbox` · `cli` · `tui` · `rust`	✅	slightly complex (Rust CLI, any model, permission modes, MCP)	Modes and permissions
11	oh-my-pi	36.6k	Terminal coding agent (fork of Pi) that wires the IDE into the harness: hash-anchored edits, a 32-tool loop tuned per-model, LSP rename/references/diagnostics on every write, a real DAP debugger (lldb/dlv/debugpy), long-lived Python + Bun execution kernels that call back into the agent's tools, browser control, and 40+ providers (Claude/OpenAI/Gemini/local). ~55k-line Rust core. `browser` · `provider-agnostic` · `cli` · `ide` · `rust`	✅	slightly complex (terminal agent, LSP/DAP, multi-provider)	LSP wired into edits
12	DeepSeek-Reasonix	36k	DeepSeek-native terminal coding agent. The harness is engineered around prefix-cache stability for long-running sessions; the TUI is the shell. `memory` · `cli` · `tui` · `typescript`	❓	slightly complex (terminal agent, prefix-cache tuned)	Project README
13	crush	28.8k	Charm's terminal coding agent (Charm's fork of the original OpenCode). The harness is the tool-calling loop with session persistence; the Bubble Tea TUI is the shell. `memory` · `cli` · `tui`	⚠️ FSL-1.1-MIT	slightly complex (terminal agent, TUI)	Crush launch post
14	qwen-code	28.7k	Alibaba's official terminal coding agent, forked from Gemini CLI's agent loop and retuned for Qwen models. The harness is the same sandboxed tool-call loop as its upstream; the terminal is the shell. `sandbox` · `cli` · `typescript`	❓	slightly complex (official CLI, Gemini-CLI fork)	Project README
15	vibe-kanban	28.4k	Kanban-style fleet manager for running Claude Code, Codex, or any coding agent across many tasks at once. The harness contribution is the task-queue/review layer on top of whichever agent executes; not an agent loop itself.	❓	slightly complex (task-fleet manager)	Project README
16	Symphony ★	27.8k	OpenAI's harness for fanning a task out into many isolated, autonomous coding-agent implementation runs and surfacing the ones that pass, so a team manages outcomes instead of supervising each session. `sandbox`	❓	complex (parallel isolated runs — product suite)	Project README
17	Kilo Code	27.7k	VS Code extension and CLI in the Cline/Roo-Code lineage — a natural pick now that Roo-Code is archived upstream. The harness is an approval-gated autonomous-mode loop with a provider/tool marketplace; the IDE is the shell. `mcp` · `cli` · `ide` · `typescript`	❓	slightly complex (IDE extension + CLI, MCP)	Project README
18	Grok Build	27.5k	SpaceXAI's terminal coding agent. The harness is a Rust agent runtime that edits files, runs shell commands, searches the web, and keeps workspace checkpoints; it runs in a full-screen TUI, headless for scripts and CI, or inside editors over the Agent Client Protocol. The public repo is synced from the company monorepo. `browser` · `cli` · `tui` · `rust`	✅	slightly complex (official CLI, Rust runtime, headless and ACP)	Project README
19	Roo Code archived upstream 2026-07-03	24.3k	VS Code/Cursor extension in the Cline lineage. The harness is the approval-gated agent with custom modes and a strong MCP story; the IDE is the UI. Popular community fork when you want that workflow without the upstream extension. `mcp` · `workflow` · `ide` · `typescript`	✅	slightly complex (IDE extension, MCP-first)	Custom modes guide
20	Prime Agent ★	22.3k	Prime Intellect's self-improving coding and research agent. The harness is a recursive language model loop (context as a variable, sub-agents as function calls inside a persistent IPython REPL) plus a Continual Harness that lets the agent create, read, update, and delete its own prompts, skills, memory, and sub-agents mid-run; daemon-backed sessions survive a closed terminal, and autonomous mode runs on turn, token, and time budgets. 95.5% on ARC-AGI-3 with Opus 5; the terminal UI is the shell. `memory` · `multi-agent` · `cli` · `typescript`	✅	slightly complex (terminal agent, daemon sessions, RLM + Continual Harness)	YC Paper Club talk (18:00)
21	jcode	21.2k	Rust terminal coding agent pitched as the most RAM-efficient harness in its class; MCP support, multi-provider (Claude/OpenAI). `mcp` · `memory` · `provider-agnostic` · `cli` · `rust`	❓	slightly complex (terminal agent, low-memory)	Project README
22	eigent	15.7k	Open-source desktop harness positioned as a local, free alternative to Claude Cowork and Codex: multi-agent workspace orchestration in a self-hosted app rather than a hosted product. `multi-agent` · `local`	❓	complex (desktop multi-agent workspace — product suite)	Project README
23	cc-haha	15.2k	Local-first desktop workspace harness for Claude Code and other agents: multi-agent sessions, Git worktrees, code diffs, a skill marketplace, and chat-app access (WeChat, Telegram, WhatsApp). `memory` · `multi-agent` · `typescript`	❓	complex (desktop workspace, multi-agent — product suite)	Project README
24	Empryo	1.5k	Coding agent for the desktop, the terminal, and CI. The harness builds a repo map before each task (tree-sitter symbols, import graph, git co-change), edits by symbol through AST and LSP, then runs typecheck, lint, and tests in the same turn. This repo holds the public SoulForge v2 core under BUSL-1.1; the current Empryo v3 app is developed privately under its own license. `workflow` · `cli` · `typescript`	⚠️ BUSL-1.1	slightly complex (repo map, AST/LSP symbol edits, any model)	Repo map concept
25	Tianshu Harness	1.2k	TypeScript coding-agent runtime shared by a terminal TUI, a desktop app, and a VS Code extension. The harness keeps each task's goal, scope, and success criteria in a task contract outside the chat history, blocks a claim of done until tests, a diff, or a verification command back it, and takes a git checkpoint before each turn's first edit. The main README is Chinese; an English README is linked. `cli` · `ide` · `tui` · `typescript`	✅	slightly complex (evidence-gated completion, task contracts, multi-surface)	English README
26	AgentBox	558	Runs multiple coding agents in parallel, each in its own sandboxed VM, locally or in the cloud, from one command. The harness contribution is the VM-per-agent isolation and fleet fan-out layer; whichever agent runs inside owns the loop. `sandbox` · `typescript`	✅	slightly complex (VM-per-agent sandbox, parallel fan-out)	Parallel agents quick start
27	claw-code-agent	547	Python reimplementation of the Claude Code agent architecture with zero external dependencies; interactive chat, streaming, plugin runtime, nested agent delegation, cost tracking, MCP transport—portable harness without the Rust/TS toolchain. `mcp` · `rust` · `python` · `typescript`	❓	slightly complex (pure Python, plugin runtime)	Quick Start guide
28	Proliferate	518	Open-source AI IDE for Claude Code, Codex, OpenCode, and more. The harness contribution is the workspace/session orchestration layer: run multiple coding agents in parallel, locally or in the cloud, with isolated workspaces, reusable workflows, and shared team context. `multi-agent` · `sandbox` · `ide` · `typescript`	✅	complex (multi-agent workspace orchestration — product suite)	Product README
29	Orbi	231	Self-hosted runner that takes a GitHub issue labeled `ai-ready` to a reviewed, merged pull request and a tagged release: each run gets its own branch and worktree, Pi writes the change, a separate review session checks it against the issue's acceptance criteria, and only the reviewed head merges. The harness contribution is the issue-to-release delivery and review gate on top of Pi; it does not own an agent loop. `python`	✅	slightly complex (issue-driven runner, review gate, systemd/launchd timer)	One issue to release, recorded
30	rpi ✱	148	Rust coding-agent runtime that began as a reimplementation of Pi and now develops on its own. The embeddable harness owns a provider-agnostic streaming agent loop, tool execution, JSONL sessions that resume after a crash from the last committed frame, and a native plugin ABI; the terminal TUI is a shell over the same crates, and a deterministic offline provider covers tests. `memory` · `provider-agnostic` · `cli` · `tui` · `rust`	✅	slightly complex (embeddable Rust runtime, terminal agent, native plugins)	Offline embedded agent example
31	YYLO	66	Command-line orchestrator for coding agents (drives Pi and Codex subagents): every task gets a dedicated branch/worktree and a typed lifecycle — read-only preflight, risk-scaled merge-queue review, release-readiness gates — with receipt-backed changes. The harness contribution is the git-native task/merge orchestration layer on top of whichever agent executes; not an agent loop itself. `multi-agent` · `typescript`	✅	slightly complex (git-native task fleet, merge queue)	Project README

Coding harness configs and SDKs

Skill packs, slash-command libraries, meta-prompting frameworks, and official SDKs that give you the harness (the agent loop, planning, memory, hooks) without bundling a specific IDE or CLI shell.

#	Project	⭐ Stars	Description	Open source	Simplicity ↔ capability	Examples
1	superpowers	305k	Performance-oriented harness pack for Claude Code and 13 other harnesses (Codex, Cursor, OpenCode, Gemini CLI, more): skills, instincts, memory, security, research-first workflows. Treats harness engineering itself as the performance lever. `memory` · `cli` · `ide`	✅	complex (multi-IDE skill stack — product suite)	TDD skill
2	Anthropic Skills	184k	Anthropic's official Agent Skills repository: SKILL.md-based folders (instructions, scripts, resources) Claude dynamically loads on Claude Code, Claude.ai, and the API. The reference for progressive-disclosure skill packs in 2026.	⚠️ Anthropic terms	mostly simple (official skills format)	docx skill
3	GStack	138k	Garry Tan's Claude Code skill stack: 23 slash-command modes (CEO/eng/design review, QA, ship, browse, retro, …) that structure one assistant as a virtual engineering team. Daily driver while running YC. `typescript`	✅	slightly complex (multi-role slash-command harness)	/ship SKILL.md
4	addyosmani/agent-skills	110k	Addy Osmani's production-grade skill pack: 24 engineering skills and 4 specialist agent personas that encode senior-dev workflows (spec through deploy) across 70+ coding agents including Claude Code, Cursor, and Copilot. The harness contribution is the skill/workflow layer, not a new agent loop. `workflow` · `ide`	✅	mostly simple (skills bundle, cross-agent)	Project README
5	awesome-claude-code	56.5k	Large community-curated index of Claude Code skills, slash commands, status lines, and plugins—resources for extending the harness, not a harness itself, but the most-followed catalog of the genre.	❓	super simple (curated resource index)	Project README
6	wshobson/agents	41.7k	Cross-harness marketplace of drop-in subagents and skills for Claude Code, Codex CLI, Cursor, OpenCode, and Copilot; specialized, production-ready agent definitions you install rather than hand-write. `multi-agent` · `cli` · `ide`	✅	super simple (drop-in agent packs)	Agent catalog
7	planning-with-files	27.6k	Skill for persistent, file-based planning across long-running coding-agent sessions: crash-proof markdown plans, session recovery after `/clear`/compaction, and a deterministic completion gate—Manus-style planning as a drop-in harness layer via the Agent Skills standard. `memory`	❓	mostly simple (skill, file-based state)	Project README
8	SWE-agent ★	20.6k	LM-driven harness built for SWE-bench: edit state, command execution, and issue-focused loop—the reference agent stack next to the benchmark itself. `memory` · `evals` · `python`	✅	slightly complex (SWE-bench pairing, stateful edits)	Default agent config
9	get-shit-done	10.8k	Goal-backward planning and wave-based execution over fresh context windows; avoids context rot by design. Python/JS meta-prompting for Claude Code, OpenCode, Gemini CLI. `cli` · `python`	✅	mostly simple (meta-prompting, you own stack)	gsd:ship command
10	Claude Agent SDK ★	8.3k	Official Anthropic SDK (Python + TypeScript, demos, quickstarts): built-in tools, MCP, long-running coding agents with session bridging. `mcp` · `memory` · `python` · `typescript`	✅	complex (full SDK, session bridging — product suite)	Research agent demo
11	Ouroboros (Q00) ★ ✱	6.3k	Spec-first layer that wraps an existing coding agent (Claude Code, Codex CLI, OpenCode, and ten more runtime families) instead of replacing it. The harness pins an acceptance spec before the run and checks it afterwards itself, keeps verify commands and expected outputs out of the worker's contract so the agent is asked for the outcome rather than handed the test, and runs its evolution loop unattended, with lineage kept in an event store that survives restarts. `mcp` · `cli` · `python`	✅	slightly complex (MCP server, spec, run, evaluate, evolve loop)	Project README
12	agents-cli	6.2k	Google's official CLI and skill pack that layers agent-creation, evaluation, and deployment skills on top of whatever coding assistant you already run, rather than shipping its own agent loop—the harness as a config/skills add-on, not a new runtime. `evals` · `cli`	❓	mostly simple (skills/CLI layer, no new runtime)	Project README
13	skillhub	5.3k	iFlytek's self-hosted registry for publishing, versioning, and governing agent skill packages—the harness config layer treated as an enterprise artifact store rather than a CLI or IDE shell. `local` · `cli` · `ide`	❓	mostly simple (skill registry/governance)	Project README
14	Meta-Harness	1.7k	Reference implementation from the Meta-Harness paper: an academic testbed for harness-engineering research, not a product—useful as a citation-grade baseline rather than something you'd run in production.	❓	slightly complex (research reference implementation)	Project README
15	RepoMaster ★	556	Repo-scoped research harness: builds function-call and module-dependency graphs to explore only what's needed; large relative gains on MLE-bench and GitTaskBench with lower token use. `workflow` · `python`	❓	slightly complex (graph-based exploration)	PDF-parse case study
16	AutoHarness	456	Lightweight governance harness: wraps any LLM client in ~2 lines for automated harness engineering—6–14 step pipeline, YAML constitution, risk-pattern matching, session persistence with cost tracking, multi-agent profiles. `memory` · `multi-agent` · `provider-agnostic` · `python`	✅	super simple (2-line wrapper, YAML gov)	Full pipeline demo
17	LoopTroop	164	Config layer that chains LLM councils for planning, Ralph loops for iterative refinement, and OpenCode worktrees for shipping. The harness contribution is the council → loop → worktree pipeline; OpenCode underneath executes. `typescript`	✅	mostly simple (config pipeline over OpenCode)	Council → loop → worktree pipeline
18	pmstack	9	Claude Code config for AI product managers: CLAUDE.md plus skills for competitive analysis, PRD-from-signal, metric frameworks, stakeholder briefs, and agent eval design. "GStack for PMs." `evals`	✅	super simple (skills bundle, PM-focused)	PRD-from-signal skill

Personal agent runtimes

Always-on, self-hosted agents you run as a daemon and talk to from chat apps: gateway runtimes, second brains, and self-improving assistants. The agent as a product you operate, not a library you build with.

#	Project	⭐ Stars	Description	Open source	Simplicity ↔ capability	Examples
1	OpenClaw ★	393k	Self-hosted, always-on personal agent (formerly Clawdbot/Moltbot): a gateway + event-loop runtime that treats messages, heartbeats, crons, and webhooks as one input queue, persists state to local files, and lives in your chat apps (WhatsApp, Telegram, Slack, Discord). 13,700+ community skills; the fastest-growing repo in GitHub history. `typescript` · `multi-agent`	✅	complex (always-on runtime, channels, skill ecosystem — product suite)	Agent runtime architecture
2	Hermes ★	257k	Nous Research's self-improving agent: a learning loop turns experience into reusable skills, builds a persistent user model across sessions, and checkpoints state to disk with rollback; lean enough for a $5 VPS, driven from chat, and model-agnostic (Nous Portal, OpenRouter, OpenAI, or any endpoint). `memory` · `python` · `provider-agnostic`	✅	slightly complex (lean runtime, learning loop, disk-first memory)	Built-in skills
3	AnythingLLM ★	67.5k	Self-hosted "AI second brain" harness: chat with your documents, run built-in agent skills (web search, code execution, browsing), and manage multi-user workspaces with a bundled vector DB, so there is no separate RAG stack to wire up. `rag` · `typescript`	✅	complex (server + desktop + multi-user; product suite)	Project README
4	nanobot	49.3k	Ultra-lightweight, self-hosted personal agent framework: the harness is a Python daemon wiring tools, memory, and MCP into chat/webhook front ends (Telegram, Discord, web); minimal footprint alternative to heavier personal-runtime stacks. `mcp` · `memory` · `local` · `python`	❓	mostly simple (lightweight daemon, chat/MCP)	Project README
5	CowAgent	47.6k	Self-hosted harness (formerly chatgpt-on-wechat) that plans tasks, runs tools/skills, and self-evolves via memory; multi-model, multi-channel (WeChat, Telegram, etc.), one-line install. `memory` · `python`	❓	slightly complex (multi-channel, self-evolving)	Project README
6	Khoj ★	37.8k	Self-hostable "AI second brain": answers over your docs and the web, custom agents, scheduled automations, and multi-client reach (web, Obsidian, Emacs, WhatsApp). A personal-agent harness with retrieval at the core. `python`	✅	complex (server + clients — product suite)	Feature tour
7	Eliza ★	19.7k	Open "agentic operating system" (elizaOS): persistent multi-agent runtime with character files, a plugin ecosystem, and social/platform integrations — the harness behind a large share of autonomous social agents. `memory` · `multi-agent` · `typescript`	✅	complex (runtime + plugin ecosystem — product suite)	Agent quickstart
8	Agent Zero	19.6k	Organic, prompt-defined personal agent framework: hierarchical sub-agents, persistent memory, browser and code tools, and self-modifying behavior; runs in Docker with a web UI. `memory` · `multi-agent` · `browser` · `sandbox` · `python`	❓	slightly complex (prompt-defined, Docker + web UI)	Framework tour
9	OpenHarness (HKUDS)	16.1k	Open agent harness with a built-in personal agent ("Ohmo") that runs across Feishu, Slack, Telegram, and Discord; core tool-use, skills, memory, multi-agent coordination with auto-compaction for multi-day sessions. `memory` · `multi-agent`	✅	complex (personal agent + multi-channel — product suite)	harness-eval skill
10	QM ★ ✱	15.5k	Y Combinator's multiplayer agent harness for work, open-sourced from months of internal use: every person and room gets scoped memory, files, credentials, permissions, crons, web apps, and a durable sandbox; sessions, memory, and the work queue live in Postgres, sandboxes are a resource the agent reaches for rather than the place it lives, and Pi, OpenCode, Codex, or Claude Code can drive the same core. Slack and the web app are the shells. `memory` · `sandbox` · `typescript`	✅	complex (multi-tenant runtime, Postgres state, sandboxes; product suite)	YC Paper Club talk (45:30)
11	OpenJarvis ★	12k	Stanford Hazy Research's local-first personal AI harness: on-device model inference (Ollama built in), agent execution, memory, and learning as shared primitives, with on-demand, scheduled, and continuous agents and evaluations that count energy, latency, and dollars alongside accuracy; a cloud model can tune the local configuration once so run time stays on your own hardware. `memory` · `local` · `python`	✅	slightly complex (local-first runtime, scheduled and continuous agents)	YC Paper Club talk (37:20)
12	Ouroboros (razzant) ★	1.6k	Self-modifying personal agent that keeps its identity, durable memory, and history across tasks and restarts. The harness owns the agent loop, tool execution, and specialist-subagent coordination, and it changes its own code only through review gates, with restart checks and rollback kept in git history; the desktop app and headless CLI are interfaces to the same runtime. `memory` · `multi-agent` · `browser` · `cli` · `python`	✅	slightly complex (self-modifying runtime, desktop + headless)	Benchmarks
13	AIlice	1.4k	Fully autonomous general-purpose agent; one binary, Docker-ready, for when you want "set goal and walk away" without a framework. `sandbox` · `python`	✅	slightly complex (autonomous, one binary)	Task showcase
14	Talon ★	74	Multi-platform personal agent living in Telegram, Discord, Teams, and the terminal. The harness is a pluggable-backend loop (Claude, Kilo, OpenCode, Codex, OpenAI Agents) with full MCP tool access and persistent background agents (Goals, Heartbeat, Dream); the chat apps are shells. `mcp` · `memory` · `cli` · `typescript`	✅	slightly complex (multi-platform, pluggable backends, MCP)	Multi-platform setup

Frameworks

General-purpose agent and LLM application frameworks (the app layer, not harnesses per se).

#	Project	⭐ Stars	Description	Open source	Simplicity ↔ capability	Examples
1	n8n ★ ✱	209k	Fair-code workflow engine with 400+ nodes and native AI nodes; the self-hosted Zapier that actually does agents and LangChain. `workflow` · `local` · `typescript`	⚠️ Fair-code	complex (400+ nodes, workflow engine — product suite)	Agent vs chain workflow
2	AutoGPT ★	188k	The original autonomous loop: goal in, agent iterates with tools and memory; Forge is the dev framework, Benchmark the eval harness. `memory` · `evals` · `python`	⚠️ Polyform-SU	complex (autonomous loop, tools, memory — product suite)	Medium blogger graph
3	Dify ★	160k	One-stop LLM app platform: visual workflows, RAG pipeline, 50+ tools, model management; "ship from prototype to prod" in a single UI. `low-code` · `rag` · `python`	⚠️ Fair-code	complex (one-stop platform — product suite)	Customer-service bot
4	langflow ★	156k	Low-code UI to build and deploy LangChain/LangGraph flows; visual DAG editor and one-click run. `low-code` · `python`	✅	complex (low-code, visual — product suite)	Chat with RAG flow
5	langchain	148k	Chains, tools, retrievers, and agents; the usual entry point for "add tools to an LLM" in Python/JS. `python`	✅	complex (kitchen-sink ecosystem — product suite)	Build an agent notebook
6	browser-use	119k	Python web-agent harness: natural-language goals become browser actions, driven directly over the Chrome DevTools Protocol (it dropped Playwright in August 2025). The biggest community in the browser-agent category. `browser` · `python`	✅	slightly complex (LLM + browser, CDP)	Grocery shopping agent
7	Flowise ★ archived upstream 2026-08-16	55.5k	Drag-and-drop LangChain UI; deploy flows without code. The low-code sibling to Langflow, with a different component and hosting story. `low-code` · `typescript`	⚠️ Apache+CLA	complex (low-code, drag-drop — product suite)	Agentic RAG flow
8	llama-index	52.7k	Data-centric: indexing, RAG, and query engines; agent abstractions sit on top of your data pipelines. `rag` · `python`	✅	complex (RAG + agents — product suite)	Research assistant workflow
9	langgraph ★ ✱	43.9k	State-machine graphs over LLM steps; checkpointing, human-in-the-loop, and durable execution so workflows survive restarts. `workflow` · `python`	✅	slightly complex (graphs, checkpointing, durable exec)	Customer support agent
10	agno	42.9k	Python agents with memory, knowledge bases, tools, and structured outputs; continues the PhiData-era product line under the Agno name—production apps, evals, and pipelines. `memory` · `evals` · `python`	✅	complex (memory, KB, observability — product suite)	Agent with tools
11	mastra ✱	29.1k	TypeScript-first; agents, tools, and workflows with a single runtime and minimal boilerplate. `typed` · `typescript`	⚠️ Elastic-2.0	slightly complex (TS-first, minimal boilerplate)	Durable research agent
12	semantic-kernel	28.7k	Microsoft's plugin and planner layer for LLMs; C#, Python, Java; strong on enterprise auth and orchestration. `python`	✅	complex (enterprise, multi-language — product suite)	Chat completion agent
13	Haystack	26.8k	Open-source orchestration framework for context-engineered LLM apps: modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation—closer to LangChain's territory than a coding-agent harness. `memory` · `rag` · `python`	✅	complex (modular pipelines, RAG + agents — product suite)	Project README
14	Stagehand	25.8k	Browserbase's SDK for browser agents: natural-language actions (act, extract, observe) and deterministic Playwright code mix in one script, so agent flexibility and repeatable automation live in the same harness. `browser` · `typescript`	✅	slightly complex (NL + code browser SDK)	Act/extract/observe quickstart
15	letta ★ ✱	25.3k	Python agent runtime with tool use and control flow; lean API; stateful agents with long-horizon memory. `memory` · `python`	✅	mostly simple (lean API)	Loop .af agent file
16	Google ADK ★	22k	Google's official Agent Development Kit: code-first Python toolkit for building, evaluating, and deploying agents. Optimized for Gemini but model-agnostic; deploys to Cloud Run / Vertex AI; ships a dev UI with eval and a code-execution sandbox. `evals` · `sandbox` · `python`	✅	complex (official Google SDK, eval, deploy — product suite)	Travel concierge agent
17	rasa ★	21.4k	Conversational AI stack (NLU, dialogue, actions); long-standing OSS choice for chat and voice bots. `voice` · `python`	✅	complex (full stack — product suite)	Sara conversational demo
18	botpress ★	15k	Visual bot builder and runtime; multi-channel, open-source alternative to commercial bot platforms. `low-code` · `typescript`	✅	complex (visual builder, multi-channel — product suite)	Inter-bot delegation
19	R2R ★	8k	RAG-first: hybrid search, knowledge graphs, multimodal; the framework for "production RAG" when you care more about retrieval than chat UI. `vision` · `rag` · `workflow` · `python`	✅	complex (production RAG — product suite)	hello_r2r RAG example
20	agent-squad	7.8k	AWS-originated orchestrator (now under 2FastLabs): intent classification, streaming, SupervisorAgent; "agent-as-tools" so one agent delegates to a squad. `multi-agent`	✅	slightly complex (squad orchestration)	E-commerce support sim
21	AgentVerse ★	5.2k	Task-solving and simulation envs for multi-LLM agents; deploy many ag

Project

⭐ Stars

Description

Open source

Simplicity ↔ capability

Examples

Headroom

77.8k

Compresses tool outputs, logs, files, and RAG chunks with content-aware compressors before they reach the model—claimed 20% fewer tokens for coding agents and 60–95% fewer for JSON, same answers. Ships as a library, HTTP proxy, or MCP server, so it drops in front of whatever harness you already run. mcp · rag

✅

mostly simple (compression library/proxy/MCP server)

Project README

awesome-cursorrules

41k

Curated .cursorrules and skills that leverage Cursor's index-then-load model; the canonical collection for rules-as-progressive-disclosure in the IDE. ide

✅

super simple (content bundle)

PyTorch cursorrules

context-mode

32.2k

Context-window optimization layer that sandboxes tool output before it reaches the model (claimed 98% reduction) and persists session memory across 17 agent platforms via MCP and hooks—progressive disclosure applied to tool results, not just instructions. mcp · memory · sandbox

⚠️ Elastic-2.0

mostly simple (output sandboxing, cross-platform)

Project README

agents.md

25.1k

Open format for repo-scoped agent briefings; nested AGENTS.md files scope instructions per directory, so agents get a map of what exists and load only what's relevant. Read by 20+ tools including Codex, Cursor, and Copilot. ide · typescript

✅

super simple (format only)

Self-hosting AGENTS.md

langgraph-bigtool ✱

559

Build LangGraph agents with large tool sets; retrieval and on-demand tool loading so agents scale beyond context without stuffing every schema upfront. tool-discovery · python

✅

slightly complex (large tool sets)

Math-library tool agent

MCP-Zero

517

Active tool discovery for autonomous agents: model requests tools by requirement; hierarchical semantic routing over 308 servers / 2,797 tools with ~98% token reduction (APIBank). tool-discovery

✅

complex (3k tools, full routing)

APIBank experiment

ToolGen

184

ICLR 2025: unified tool retrieval and calling via generation; 47k+ tools without context stuffing—retrieval and invocation in one generative step. tool-discovery · python

❓

complex (47k+ tools)

Full eval pipeline

ToolRAG

Semantic tool retrieval for LLMs; serves only the tools the user query demands (MCP-compatible), unlimited tool sets with zero context penalty. mcp · tool-discovery

✅

mostly simple (query-driven retrieval)

MCP server retrieval

Project

⭐ Stars

Description

Open source

Simplicity ↔ capability

Examples

DeepSeek Harness

261k

DeepSeek's official open-source agent harness (dsh), built so that everything is a plugin: the model adapter, tool registry, session log, sandbox and approval policy, and the agent loop itself are Cordis plugins you replace from configuration, and one plugin tree boots as a Web UI, a headless runner, a desktop app, an SDK server, or an ACP server. Developer preview, with breaking changes expected. browser · sandbox · typescript

✅

slightly complex (plugin-tree runtime, Web UI, headless, SDK)

Plugin architecture

opencode ★

221k

Open-source terminal coding agent (formerly sst/opencode; transferred to anomalyco). The harness is a multi-provider tool-call loop (Claude, OpenAI, Gemini, local) with strong plugin and MCP support; the TUI is the shell. 100% OSS, very actively shipped. mcp · provider-agnostic · cli · tui · typescript

✅

slightly complex (multi-provider, plugins, MCP)

Agent system page

Codex

131k

OpenAI's terminal coding agent. The harness is the sandboxed tool-call loop with multi-provider support; the CLI is the shell. Reference implementation for "official CLI that ships code." sandbox · provider-agnostic · cli

✅

slightly complex (reference CLI, sandboxed)

Sandboxing concept

118k

The upstream AI agent toolkit behind this list's oh-my-pi fork: a unified multi-provider LLM API, agent loop, and TUI shell providing the harness that oh-my-pi's Rust rewrite builds on. provider-agnostic · tui · rust

❓

slightly complex (multi-provider agent loop, TUI)

Project README

Gemini CLI

108k

Google's first-party terminal agent for Gemini. The harness is the plugin/MCP tool-call loop; the terminal is the shell—Google's parallel to Claude Code / Codex, not just an API. mcp · cli · typescript

✅

slightly complex (official CLI, plugins, MCP)

MCP server setup

OpenHands ★

91.8k

Dockerized software-engineering agent. The harness is the bash/editor/browser toolset with micro-agents and event-stream session bridging; Docker is the sandbox. Main OSS choice for teams self-hosting autonomous repo work. memory · browser · sandbox · python

⚠️ (multi-license)

complex (Docker runtime, multi-surface agent — product suite)

Repository microagents

Cline

70.9k

VS Code extension whose harness is a plan-then-act loop with per-step human approval and cost transparency; the VS Code integration is the UI shell. Open-source counterweight to Cursor. ide · typescript

✅

slightly complex (plan-then-act, approval gates)

Plan & Act mode

Open Interpreter

68.7k

Lightweight terminal coding agent oriented to open models (DeepSeek, Kimi, Qwen). The harness is a code-execution loop — the model writes code, the harness executes it with confirmation gates; the CLI is the shell. The original "let the LLM run code on my machine" project, reborn for open weights. cli · python

✅

mostly simple (lean code-exec loop)

Quick start

goose ★

55.7k

Block-originated Rust agent, now stewarded by the Linux Foundation's Agentic AI Foundation (aaif-goose/goose). The harness is the MCP/ACP extension model with recipes and provider choice; there's no fixed UI slot—you bolt it into whatever shell you use. mcp · rust

✅

slightly complex (extensions, MCP/ACP)

Goose recipes guide

Codewhale

41.1k

Open-source Rust terminal coding agent for any model (40+ providers, local models included). The harness pairs a goal-driven work loop with permission modes (Ask, Auto-Review by a reviewer model, Full Access) and an optional workspace-write sandbox, and /receipts lists every file it changed, command it ran, and web or MCP call it made; the TUI is the shell. mcp · sandbox · cli · tui · rust

✅

slightly complex (Rust CLI, any model, permission modes, MCP)

Modes and permissions

oh-my-pi

36.6k

Terminal coding agent (fork of Pi) that wires the IDE into the harness: hash-anchored edits, a 32-tool loop tuned per-model, LSP rename/references/diagnostics on every write, a real DAP debugger (lldb/dlv/debugpy), long-lived Python + Bun execution kernels that call back into the agent's tools, browser control, and 40+ providers (Claude/OpenAI/Gemini/local). ~55k-line Rust core. browser · provider-agnostic · cli · ide · rust

✅

slightly complex (terminal agent, LSP/DAP, multi-provider)

LSP wired into edits

DeepSeek-Reasonix

36k

DeepSeek-native terminal coding agent. The harness is engineered around prefix-cache stability for long-running sessions; the TUI is the shell. memory · cli · tui · typescript

❓

slightly complex (terminal agent, prefix-cache tuned)

Project README

crush

28.8k

Charm's terminal coding agent (Charm's fork of the original OpenCode). The harness is the tool-calling loop with session persistence; the Bubble Tea TUI is the shell. memory · cli · tui

⚠️ FSL-1.1-MIT

slightly complex (terminal agent, TUI)

Crush launch post

qwen-code

28.7k

Alibaba's official terminal coding agent, forked from Gemini CLI's agent loop and retuned for Qwen models. The harness is the same sandboxed tool-call loop as its upstream; the terminal is the shell. sandbox · cli · typescript

❓

slightly complex (official CLI, Gemini-CLI fork)

Project README

vibe-kanban

28.4k

Kanban-style fleet manager for running Claude Code, Codex, or any coding agent across many tasks at once. The harness contribution is the task-queue/review layer on top of whichever agent executes; not an agent loop itself.

❓

slightly complex (task-fleet manager)

Project README

Symphony ★

27.8k

OpenAI's harness for fanning a task out into many isolated, autonomous coding-agent implementation runs and surfacing the ones that pass, so a team manages outcomes instead of supervising each session. sandbox

❓

complex (parallel isolated runs — product suite)

Project README

Kilo Code

27.7k

VS Code extension and CLI in the Cline/Roo-Code lineage — a natural pick now that Roo-Code is archived upstream. The harness is an approval-gated autonomous-mode loop with a provider/tool marketplace; the IDE is the shell. mcp · cli · ide · typescript

❓

slightly complex (IDE extension + CLI, MCP)

Project README

Grok Build

27.5k

SpaceXAI's terminal coding agent. The harness is a Rust agent runtime that edits files, runs shell commands, searches the web, and keeps workspace checkpoints; it runs in a full-screen TUI, headless for scripts and CI, or inside editors over the Agent Client Protocol. The public repo is synced from the company monorepo. browser · cli · tui · rust

✅

slightly complex (official CLI, Rust runtime, headless and ACP)

Project README

Roo Code archived upstream 2026-07-03

24.3k

VS Code/Cursor extension in the Cline lineage. The harness is the approval-gated agent with custom modes and a strong MCP story; the IDE is the UI. Popular community fork when you want that workflow without the upstream extension. mcp · workflow · ide · typescript

✅

slightly complex (IDE extension, MCP-first)

Custom modes guide

Prime Agent ★

22.3k

Prime Intellect's self-improving coding and research agent. The harness is a recursive language model loop (context as a variable, sub-agents as function calls inside a persistent IPython REPL) plus a Continual Harness that lets the agent create, read, update, and delete its own prompts, skills, memory, and sub-agents mid-run; daemon-backed sessions survive a closed terminal, and autonomous mode runs on turn, token, and time budgets. 95.5% on ARC-AGI-3 with Opus 5; the terminal UI is the shell. memory · multi-agent · cli · typescript

✅

slightly complex (terminal agent, daemon sessions, RLM + Continual Harness)

YC Paper Club talk (18:00)

jcode

21.2k

Rust terminal coding agent pitched as the most RAM-efficient harness in its class; MCP support, multi-provider (Claude/OpenAI). mcp · memory · provider-agnostic · cli · rust

❓

slightly complex (terminal agent, low-memory)

Project README

eigent

15.7k

Open-source desktop harness positioned as a local, free alternative to Claude Cowork and Codex: multi-agent workspace orchestration in a self-hosted app rather than a hosted product. multi-agent · local

❓

complex (desktop multi-agent workspace — product suite)

Project README

cc-haha

15.2k

Local-first desktop workspace harness for Claude Code and other agents: multi-agent sessions, Git worktrees, code diffs, a skill marketplace, and chat-app access (WeChat, Telegram, WhatsApp). memory · multi-agent · typescript

❓

complex (desktop workspace, multi-agent — product suite)

Project README

Empryo

1.5k

Coding agent for the desktop, the terminal, and CI. The harness builds a repo map before each task (tree-sitter symbols, import graph, git co-change), edits by symbol through AST and LSP, then runs typecheck, lint, and tests in the same turn. This repo holds the public SoulForge v2 core under BUSL-1.1; the current Empryo v3 app is developed privately under its own license. workflow · cli · typescript

⚠️ BUSL-1.1

slightly complex (repo map, AST/LSP symbol edits, any model)

Repo map concept

Tianshu Harness

1.2k

TypeScript coding-agent runtime shared by a terminal TUI, a desktop app, and a VS Code extension. The harness keeps each task's goal, scope, and success criteria in a task contract outside the chat history, blocks a claim of done until tests, a diff, or a verification command back it, and takes a git checkpoint before each turn's first edit. The main README is Chinese; an English README is linked. cli · ide · tui · typescript

✅

slightly complex (evidence-gated completion, task contracts, multi-surface)

English README

AgentBox

558

Runs multiple coding agents in parallel, each in its own sandboxed VM, locally or in the cloud, from one command. The harness contribution is the VM-per-agent isolation and fleet fan-out layer; whichever agent runs inside owns the loop. sandbox · typescript

✅

slightly complex (VM-per-agent sandbox, parallel fan-out)

Parallel agents quick start

claw-code-agent

547

Python reimplementation of the Claude Code agent architecture with zero external dependencies; interactive chat, streaming, plugin runtime, nested agent delegation, cost tracking, MCP transport—portable harness without the Rust/TS toolchain. mcp · rust · python · typescript

❓

slightly complex (pure Python, plugin runtime)

Quick Start guide

Proliferate

518

Open-source AI IDE for Claude Code, Codex, OpenCode, and more. The harness contribution is the workspace/session orchestration layer: run multiple coding agents in parallel, locally or in the cloud, with isolated workspaces, reusable workflows, and shared team context. multi-agent · sandbox · ide · typescript

✅

complex (multi-agent workspace orchestration — product suite)

Product README

Orbi

231

Self-hosted runner that takes a GitHub issue labeled ai-ready to a reviewed, merged pull request and a tagged release: each run gets its own branch and worktree, Pi writes the change, a separate review session checks it against the issue's acceptance criteria, and only the reviewed head merges. The harness contribution is the issue-to-release delivery and review gate on top of Pi; it does not own an agent loop. python

✅

slightly complex (issue-driven runner, review gate, systemd/launchd timer)

One issue to release, recorded

rpi ✱

148

Rust coding-agent runtime that began as a reimplementation of Pi and now develops on its own. The embeddable harness owns a provider-agnostic streaming agent loop, tool execution, JSONL sessions that resume after a crash from the last committed frame, and a native plugin ABI; the terminal TUI is a shell over the same crates, and a deterministic offline provider covers tests. memory · provider-agnostic · cli · tui · rust

✅

slightly complex (embeddable Rust runtime, terminal agent, native plugins)

Offline embedded agent example

YYLO

Command-line orchestrator for coding agents (drives Pi and Codex subagents): every task gets a dedicated branch/worktree and a typed lifecycle — read-only preflight, risk-scaled merge-queue review, release-readiness gates — with receipt-backed changes. The harness contribution is the git-native task/merge orchestration layer on top of whichever agent executes; not an agent loop itself. multi-agent · typescript

✅

slightly complex (git-native task fleet, merge queue)

Project README

Project

⭐ Stars

Description

Open source

Simplicity ↔ capability

Examples

superpowers

305k

Performance-oriented harness pack for Claude Code and 13 other harnesses (Codex, Cursor, OpenCode, Gemini CLI, more): skills, instincts, memory, security, research-first workflows. Treats harness engineering itself as the performance lever. memory · cli · ide

✅

complex (multi-IDE skill stack — product suite)

TDD skill

Anthropic Skills

184k

Anthropic's official Agent Skills repository: SKILL.md-based folders (instructions, scripts, resources) Claude dynamically loads on Claude Code, Claude.ai, and the API. The reference for progressive-disclosure skill packs in 2026.

⚠️ Anthropic terms

mostly simple (official skills format)

docx skill

GStack

138k

Garry Tan's Claude Code skill stack: 23 slash-command modes (CEO/eng/design review, QA, ship, browse, retro, …) that structure one assistant as a virtual engineering team. Daily driver while running YC. typescript

✅

slightly complex (multi-role slash-command harness)

/ship SKILL.md

addyosmani/agent-skills

110k

Addy Osmani's production-grade skill pack: 24 engineering skills and 4 specialist agent personas that encode senior-dev workflows (spec through deploy) across 70+ coding agents including Claude Code, Cursor, and Copilot. The harness contribution is the skill/workflow layer, not a new agent loop. workflow · ide

✅

mostly simple (skills bundle, cross-agent)

Project README

awesome-claude-code

56.5k

Large community-curated index of Claude Code skills, slash commands, status lines, and plugins—resources for extending the harness, not a harness itself, but the most-followed catalog of the genre.

❓

super simple (curated resource index)

Project README

wshobson/agents

41.7k

Cross-harness marketplace of drop-in subagents and skills for Claude Code, Codex CLI, Cursor, OpenCode, and Copilot; specialized, production-ready agent definitions you install rather than hand-write. multi-agent · cli · ide

✅

super simple (drop-in agent packs)

Agent catalog

planning-with-files

27.6k

Skill for persistent, file-based planning across long-running coding-agent sessions: crash-proof markdown plans, session recovery after /clear/compaction, and a deterministic completion gate—Manus-style planning as a drop-in harness layer via the Agent Skills standard. memory

❓

mostly simple (skill, file-based state)

Project README

SWE-agent ★

20.6k

LM-driven harness built for SWE-bench: edit state, command execution, and issue-focused loop—the reference agent stack next to the benchmark itself. memory · evals · python

✅

slightly complex (SWE-bench pairing, stateful edits)

Default agent config

get-shit-done

10.8k

Goal-backward planning and wave-based execution over fresh context windows; avoids context rot by design. Python/JS meta-prompting for Claude Code, OpenCode, Gemini CLI. cli · python

✅

mostly simple (meta-prompting, you own stack)

gsd:ship command

Claude Agent SDK ★

8.3k

Official Anthropic SDK (Python + TypeScript, demos, quickstarts): built-in tools, MCP, long-running coding agents with session bridging. mcp · memory · python · typescript

✅

complex (full SDK, session bridging — product suite)

Research agent demo

Ouroboros (Q00) ★ ✱

6.3k

Spec-first layer that wraps an existing coding agent (Claude Code, Codex CLI, OpenCode, and ten more runtime families) instead of replacing it. The harness pins an acceptance spec before the run and checks it afterwards itself, keeps verify commands and expected outputs out of the worker's contract so the agent is asked for the outcome rather than handed the test, and runs its evolution loop unattended, with lineage kept in an event store that survives restarts. mcp · cli · python

✅

slightly complex (MCP server, spec, run, evaluate, evolve loop)

Project README

agents-cli

6.2k

Google's official CLI and skill pack that layers agent-creation, evaluation, and deployment skills on top of whatever coding assistant you already run, rather than shipping its own agent loop—the harness as a config/skills add-on, not a new runtime. evals · cli

❓

mostly simple (skills/CLI layer, no new runtime)

Project README

skillhub

5.3k

iFlytek's self-hosted registry for publishing, versioning, and governing agent skill packages—the harness config layer treated as an enterprise artifact store rather than a CLI or IDE shell. local · cli · ide

❓

mostly simple (skill registry/governance)

Project README

Meta-Harness

1.7k

Reference implementation from the Meta-Harness paper: an academic testbed for harness-engineering research, not a product—useful as a citation-grade baseline rather than something you'd run in production.

❓

slightly complex (research reference implementation)

Project README

RepoMaster ★

556

Repo-scoped research harness: builds function-call and module-dependency graphs to explore only what's needed; large relative gains on MLE-bench and GitTaskBench with lower token use. workflow · python

❓

slightly complex (graph-based exploration)

PDF-parse case study

AutoHarness

456

Lightweight governance harness: wraps any LLM client in ~2 lines for automated harness engineering—6–14 step pipeline, YAML constitution, risk-pattern matching, session persistence with cost tracking, multi-agent profiles. memory · multi-agent · provider-agnostic · python

✅

super simple (2-line wrapper, YAML gov)

Full pipeline demo

LoopTroop

164

Config layer that chains LLM councils for planning, Ralph loops for iterative refinement, and OpenCode worktrees for shipping. The harness contribution is the council → loop → worktree pipeline; OpenCode underneath executes. typescript

✅

mostly simple (config pipeline over OpenCode)

Council → loop → worktree pipeline

pmstack

Claude Code config for AI product managers: CLAUDE.md plus skills for competitive analysis, PRD-from-signal, metric frameworks, stakeholder briefs, and agent eval design. "GStack for PMs." evals

✅

super simple (skills bundle, PM-focused)

PRD-from-signal skill

Project

⭐ Stars

Description

Open source

Simplicity ↔ capability

Examples

OpenClaw ★

393k

Self-hosted, always-on personal agent (formerly Clawdbot/Moltbot): a gateway + event-loop runtime that treats messages, heartbeats, crons, and webhooks as one input queue, persists state to local files, and lives in your chat apps (WhatsApp, Telegram, Slack, Discord). 13,700+ community skills; the fastest-growing repo in GitHub history. typescript · multi-agent

✅

complex (always-on runtime, channels, skill ecosystem — product suite)

Agent runtime architecture

Hermes ★

257k

Nous Research's self-improving agent: a learning loop turns experience into reusable skills, builds a persistent user model across sessions, and checkpoints state to disk with rollback; lean enough for a $5 VPS, driven from chat, and model-agnostic (Nous Portal, OpenRouter, OpenAI, or any endpoint). memory · python · provider-agnostic

✅

slightly complex (lean runtime, learning loop, disk-first memory)

Built-in skills

AnythingLLM ★

67.5k

Self-hosted "AI second brain" harness: chat with your documents, run built-in agent skills (web search, code execution, browsing), and manage multi-user workspaces with a bundled vector DB, so there is no separate RAG stack to wire up. rag · typescript

✅

complex (server + desktop + multi-user; product suite)

Project README

nanobot

49.3k

Ultra-lightweight, self-hosted personal agent framework: the harness is a Python daemon wiring tools, memory, and MCP into chat/webhook front ends (Telegram, Discord, web); minimal footprint alternative to heavier personal-runtime stacks. mcp · memory · local · python

❓

mostly simple (lightweight daemon, chat/MCP)

Project README

CowAgent

47.6k

Self-hosted harness (formerly chatgpt-on-wechat) that plans tasks, runs tools/skills, and self-evolves via memory; multi-model, multi-channel (WeChat, Telegram, etc.), one-line install. memory · python

❓

slightly complex (multi-channel, self-evolving)

Project README

Khoj ★

37.8k

Self-hostable "AI second brain": answers over your docs and the web, custom agents, scheduled automations, and multi-client reach (web, Obsidian, Emacs, WhatsApp). A personal-agent harness with retrieval at the core. python

✅

complex (server + clients — product suite)

Feature tour

Eliza ★

19.7k

Open "agentic operating system" (elizaOS): persistent multi-agent runtime with character files, a plugin ecosystem, and social/platform integrations — the harness behind a large share of autonomous social agents. memory · multi-agent · typescript

✅

complex (runtime + plugin ecosystem — product suite)

Agent quickstart

Agent Zero

19.6k

Organic, prompt-defined personal agent framework: hierarchical sub-agents, persistent memory, browser and code tools, and self-modifying behavior; runs in Docker with a web UI. memory · multi-agent · browser · sandbox · python

❓

slightly complex (prompt-defined, Docker + web UI)

Framework tour

OpenHarness (HKUDS)

16.1k

Open agent harness with a built-in personal agent ("Ohmo") that runs across Feishu, Slack, Telegram, and Discord; core tool-use, skills, memory, multi-agent coordination with auto-compaction for multi-day sessions. memory · multi-agent

✅

complex (personal agent + multi-channel — product suite)

harness-eval skill

QM ★ ✱

15.5k

Y Combinator's multiplayer agent harness for work, open-sourced from months of internal use: every person and room gets scoped memory, files, credentials, permissions, crons, web apps, and a durable sandbox; sessions, memory, and the work queue live in Postgres, sandboxes are a resource the agent reaches for rather than the place it lives, and Pi, OpenCode, Codex, or Claude Code can drive the same core. Slack and the web app are the shells. memory · sandbox · typescript

✅

complex (multi-tenant runtime, Postgres state, sandboxes; product suite)

YC Paper Club talk (45:30)

OpenJarvis ★

12k

Stanford Hazy Research's local-first personal AI harness: on-device model inference (Ollama built in), agent execution, memory, and learning as shared primitives, with on-demand, scheduled, and continuous agents and evaluations that count energy, latency, and dollars alongside accuracy; a cloud model can tune the local configuration once so run time stays on your own hardware. memory · local · python

✅

slightly complex (local-first runtime, scheduled and continuous agents)

YC Paper Club talk (37:20)

Ouroboros (razzant) ★

1.6k

Self-modifying personal agent that keeps its identity, durable memory, and history across tasks and restarts. The harness owns the agent loop, tool execution, and specialist-subagent coordination, and it changes its own code only through review gates, with restart checks and rollback kept in git history; the desktop app and headless CLI are interfaces to the same runtime. memory · multi-agent · browser · cli · python

✅

slightly complex (self-modifying runtime, desktop + headless)

Benchmarks

AIlice

1.4k

Fully autonomous general-purpose agent; one binary, Docker-ready, for when you want "set goal and walk away" without a framework. sandbox · python

✅

slightly complex (autonomous, one binary)

Task showcase

Talon ★

Multi-platform personal agent living in Telegram, Discord, Teams, and the terminal. The harness is a pluggable-backend loop (Claude, Kilo, OpenCode, Codex, OpenAI Agents) with full MCP tool access and persistent background agents (Goals, Heartbeat, Dream); the chat apps are shells. mcp · memory · cli · typescript

✅

slightly complex (multi-platform, pluggable backends, MCP)

Multi-platform setup

Project

⭐ Stars

Description

Open source

Simplicity ↔ capability

Examples

n8n ★ ✱

209k

Fair-code workflow engine with 400+ nodes and native AI nodes; the self-hosted Zapier that actually does agents and LangChain. workflow · local · typescript

⚠️ Fair-code

complex (400+ nodes, workflow engine — product suite)

Agent vs chain workflow

AutoGPT ★

188k

The original autonomous loop: goal in, agent iterates with tools and memory; Forge is the dev framework, Benchmark the eval harness. memory · evals · python

⚠️ Polyform-SU

complex (autonomous loop, tools, memory — product suite)

Medium blogger graph

Dify ★

160k

One-stop LLM app platform: visual workflows, RAG pipeline, 50+ tools, model management; "ship from prototype to prod" in a single UI. low-code · rag · python

⚠️ Fair-code

complex (one-stop platform — product suite)

Customer-service bot

langflow ★

156k

Low-code UI to build and deploy LangChain/LangGraph flows; visual DAG editor and one-click run. low-code · python

✅

complex (low-code, visual — product suite)

Chat with RAG flow

langchain

148k

Chains, tools, retrievers, and agents; the usual entry point for "add tools to an LLM" in Python/JS. python

✅

complex (kitchen-sink ecosystem — product suite)

Build an agent notebook

browser-use

119k

Python web-agent harness: natural-language goals become browser actions, driven directly over the Chrome DevTools Protocol (it dropped Playwright in August 2025). The biggest community in the browser-agent category. browser · python

✅

slightly complex (LLM + browser, CDP)

Grocery shopping agent

Flowise ★ archived upstream 2026-08-16

55.5k

Drag-and-drop LangChain UI; deploy flows without code. The low-code sibling to Langflow, with a different component and hosting story. low-code · typescript

⚠️ Apache+CLA

complex (low-code, drag-drop — product suite)

Agentic RAG flow

llama-index

52.7k

Data-centric: indexing, RAG, and query engines; agent abstractions sit on top of your data pipelines. rag · python

✅

complex (RAG + agents — product suite)

Research assistant workflow

langgraph ★ ✱

43.9k

State-machine graphs over LLM steps; checkpointing, human-in-the-loop, and durable execution so workflows survive restarts. workflow · python

✅

slightly complex (graphs, checkpointing, durable exec)

Customer support agent

agno

42.9k

Python agents with memory, knowledge bases, tools, and structured outputs; continues the PhiData-era product line under the Agno name—production apps, evals, and pipelines. memory · evals · python

✅

complex (memory, KB, observability — product suite)

Agent with tools

mastra ✱

29.1k

TypeScript-first; agents, tools, and workflows with a single runtime and minimal boilerplate. typed · typescript

⚠️ Elastic-2.0

slightly complex (TS-first, minimal boilerplate)

Durable research agent

semantic-kernel

28.7k

Microsoft's plugin and planner layer for LLMs; C#, Python, Java; strong on enterprise auth and orchestration. python

✅

complex (enterprise, multi-language — product suite)

Chat completion agent

Haystack

26.8k

Open-source orchestration framework for context-engineered LLM apps: modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation—closer to LangChain's territory than a coding-agent harness. memory · rag · python

✅

complex (modular pipelines, RAG + agents — product suite)

Project README

Stagehand

25.8k

Browserbase's SDK for browser agents: natural-language actions (act, extract, observe) and deterministic Playwright code mix in one script, so agent flexibility and repeatable automation live in the same harness. browser · typescript

✅

slightly complex (NL + code browser SDK)

Act/extract/observe quickstart

letta ★ ✱

25.3k

Python agent runtime with tool use and control flow; lean API; stateful agents with long-horizon memory. memory · python

✅

mostly simple (lean API)

Loop .af agent file

Google ADK ★

22k

Google's official Agent Development Kit: code-first Python toolkit for building, evaluating, and deploying agents. Optimized for Gemini but model-agnostic; deploys to Cloud Run / Vertex AI; ships a dev UI with eval and a code-execution sandbox. evals · sandbox · python

✅

complex (official Google SDK, eval, deploy — product suite)

Travel concierge agent

rasa ★

21.4k

Conversational AI stack (NLU, dialogue, actions); long-standing OSS choice for chat and voice bots. voice · python

✅

complex (full stack — product suite)

Sara conversational demo

botpress ★

15k

Visual bot builder and runtime; multi-channel, open-source alternative to commercial bot platforms. low-code · typescript

✅

complex (visual builder, multi-channel — product suite)

Inter-bot delegation

R2R ★

RAG-first: hybrid search, knowledge graphs, multimodal; the framework for "production RAG" when you care more about retrieval than chat UI. vision · rag · workflow · python

✅

complex (production RAG — product suite)

hello_r2r RAG example

agent-squad

7.8k

AWS-originated orchestrator (now under 2FastLabs): intent classification, streaming, SupervisorAgent; "agent-as-tools" so one agent delegates to a squad. multi-agent

✅

slightly complex (squad orchestration)

E-commerce support sim

AgentVerse ★

5.2k

Task-solving and simulation envs for multi-LLM agents; deploy many ag

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Add this server

Host a copy with our keys and logs.

Server facts

Tools

Questions

How do I install Best Of Agent Harnesses?

Does Best Of Agent Harnesses need an API key?

What transport does Best Of Agent Harnesses use?

When was Best Of Agent Harnesses last checked?

Documentation

Why harnesses matter

The landscape at a glance

How to Pick a Harness

Templates and Playbooks

Skills

Pick by use case

For agents

Or hire a skeleton

Contents

Guide to rankings

Progressive disclosure harnesses

Coding agent products (IDEs, CLIs, full suites)

Coding harness configs and SDKs

Personal agent runtimes

Frameworks