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OpenHarness delivers core lightweight agent infrastructure: tool-use, skills, memory, and multi-agent coordination.

Join the community: contribute Harness for open agent development.

One Command (oh) to Launch OpenHarness and Unlock All Agent Harnesses.

Supports CLI agent integration including OpenClaw, nanobot, Cursor, and more.

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🔄 Agent Loop • Streaming Tool-Call Cycle • API Retry with Exponential Backoff • Parallel Tool Execution • Token Counting & Cost Tracking

🔧 Harness Toolkit • 43 Tools (File, Shell, Search, Web, MCP) • On-Demand Skill Loading (.md) • Plugin Ecosystem (Skills + Hooks + Agents) • Compatible with anthropics/skills & plugins

🧠 Context & Memory • CLAUDE.md Discovery & Injection • Context Compression (Auto-Compact) • MEMORY.md Persistent Memory • Session Resume & History

🛡️ Governance • Multi-Level Permission Modes • Path-Level & Command Rules • PreToolUse / PostToolUse Hooks • Interactive Approval Dialogs

🤝 Swarm Coordination • Subagent Spawning & Delegation • Team Registry & Task Management • Background Task Lifecycle • ClawTeam Integration (Roadmap)

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An Agent Harness is the complete infrastructure that wraps around an LLM to make it a functional agent. The model provides intelligence; the harness provides hands, eyes, memory, and safety boundaries.

OpenHarness is an open-source Python implementation designed for researchers, builders, and the community:

• Understand how production AI agents work under the hood
• Experiment with cutting-edge tools, skills, and agent coordination patterns
• Extend the harness with custom plugins, providers, and domain knowledge
• Build specialized agents on top of proven architecture

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• 2026-04-06 🚀 v0.1.2 — Unified setup flows and ohmo personal-agent app:
  • oh setup now guides provider selection as workflows instead of exposing raw auth/provider internals
  • Compatible API setup is now profile-scoped, so Anthropic/OpenAI-compatible endpoints can keep separate keys
  • ohmo ships as a packaged app with ~/.ohmo workspace, gateway, bootstrap prompts, and channel config flow
• 2026-04-01 🎨 v0.1.0 — Initial OpenHarness open-source release featuring complete Harness architecture:

Start here: Quick Start · Provider Compatibility · Showcase · Contributing · Changelog

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The fastest way to get started — a single command handles OS detection, dependency checks, and installation:

curl -fsSL https://raw.githubusercontent.com/HKUDS/OpenHarness/main/scripts/install.sh | bash

Options:

Flag	Description
--from-source	Clone from GitHub and install in editable mode (pip install -e .)
--with-channels	Also install IM channel dependencies (slack-sdk, python-telegram-bot, discord.py)

# Install from source (for contributors / latest code)
curl -fsSL https://raw.githubusercontent.com/HKUDS/OpenHarness/main/scripts/install.sh | bash -s -- --from-source

# Install with IM channel support
curl -fsSL https://raw.githubusercontent.com/HKUDS/OpenHarness/main/scripts/install.sh | bash -s -- --with-channels

# Or run locally after cloning
bash scripts/install.sh --from-source --with-channels

The script will:

1. Detect your OS (Linux / macOS / WSL)
2. Verify Python ≥ 3.10 and Node.js ≥ 18
3. Install OpenHarness via pip
4. Set up the React TUI (npm install) if Node.js is available
5. Create ~/.openharness/ config directory
6. Confirm with oh --version

• Python 3.10+ and uv
• Node.js 18+ (optional, for the React terminal UI)
• An LLM API key

ANTHROPIC_API_KEY=your_key uv run oh -p "Inspect this repository and list the top 3 refactors"

# Clone and install
git clone https://github.com/HKUDS/OpenHarness.git
cd OpenHarness
uv sync --extra dev

# Example: use Kimi as the backend
export ANTHROPIC_BASE_URL=https://api.moonshot.cn/anthropic
export ANTHROPIC_API_KEY=your_kimi_api_key
export ANTHROPIC_MODEL=kimi-k2.5

# Launch
oh                    # if venv is activated
uv run oh             # without activating venv

Use the unified setup flow instead of manually thinking about auth -> provider -> model:

uv run oh setup

oh setup walks through:

1. Choose a workflow:
   • Anthropic-Compatible API
   • Claude Subscription
   • OpenAI-Compatible API
   • Codex Subscription
   • GitHub Copilot
2. For compatible API families, choose a concrete backend preset
3. If needed, authenticate the selected workflow
4. Pick or confirm the model
5. Save and activate the profile

Compatible API families currently guide you through presets such as:

• Anthropic-Compatible API:
  • Claude official
  • Moonshot / Kimi
  • Zhipu / GLM
  • MiniMax
• OpenAI-Compatible API:
  • OpenAI official
  • OpenRouter

Arbitrary compatible endpoints are still supported through advanced profile commands:

oh provider add my-endpoint \
  --label "My Endpoint" \
  --provider anthropic \
  --api-format anthropic \
  --auth-source anthropic_api_key \
  --model my-model \
  --base-url https://example.com/anthropic

OpenHarness stores API-key-backed compatible profiles with profile-scoped credentials when appropriate, so different compatible endpoints do not have to share one global key.

# Single prompt → stdout
oh -p "Explain this codebase"

# JSON output for programmatic use
oh -p "List all functions in main.py" --output-format json

# Stream JSON events in real-time
oh -p "Fix the bug" --output-format stream-json

OpenHarness treats providers as workflows backed by named profiles. In day-to-day use, prefer:

oh setup
oh provider list
oh provider use <profile>

Workflow	What it is	Typical backends
Anthropic-Compatible API	Anthropic-style request format	Claude official, Kimi, GLM, MiniMax, internal Anthropic-compatible gateways
Claude Subscription	Claude CLI subscription bridge	Local ~/.claude/.credentials.json
OpenAI-Compatible API	OpenAI-style request format	OpenAI official, OpenRouter, DashScope, DeepSeek, SiliconFlow, Groq, Ollama, GitHub Models
Codex Subscription	Codex CLI subscription bridge	Local ~/.codex/auth.json
GitHub Copilot	Copilot OAuth workflow	GitHub Copilot device-flow login

Typical examples:

Backend	Base URL	Example models
Claude official	https://api.anthropic.com	claude-sonnet-4-6, claude-opus-4-6
Moonshot / Kimi	https://api.moonshot.cn/anthropic	kimi-k2.5
Zhipu / GLM	custom Anthropic-compatible endpoint	glm-4.5
MiniMax	custom Anthropic-compatible endpoint	minimax-m1

Any provider implementing the OpenAI /v1/chat/completions style API works:

Backend	Base URL	Example models
OpenAI	https://api.openai.com/v1	gpt-5.4, gpt-4.1
OpenRouter	https://openrouter.ai/api/v1	provider-specific
Alibaba DashScope	https://dashscope.aliyuncs.com/compatible-mode/v1	qwen3.5-flash, qwen3-max, deepseek-r1
DeepSeek	https://api.deepseek.com	deepseek-chat, deepseek-reasoner
GitHub Models	https://models.inference.ai.azure.com	gpt-4o, Meta-Llama-3.1-405B-Instruct
SiliconFlow	https://api.siliconflow.cn/v1	deepseek-ai/DeepSeek-V3
Groq	https://api.groq.com/openai/v1	llama-3.3-70b-versatile
Ollama (local)	http://localhost:11434/v1	any local model

# List saved workflows
oh provider list

# Switch the active workflow
oh provider use codex

# Add your own compatible endpoint
oh provider add my-endpoint \
  --label "My Endpoint" \
  --provider openai \
  --api-format openai \
  --auth-source openai_api_key \
  --model my-model \
  --base-url https://example.com/v1

For custom compatible endpoints, OpenHarness can bind credentials per profile instead of forcing every Anthropic-compatible or OpenAI-compatible backend to share the same API key.

Use your existing GitHub Copilot subscription as the LLM backend. Authentication uses GitHub's OAuth device flow — no API keys needed.

# One-time login (opens browser for GitHub authorization)
oh auth copilot-login

# Then launch with Copilot as the provider
uv run oh --api-format copilot

# Or via environment variable
export OPENHARNESS_API_FORMAT=copilot
uv run oh

# Check auth status
oh auth status

# Remove stored credentials
oh auth copilot-logout

Feature	Details
Auth method	GitHub OAuth device flow (no API key needed)
Token management	Automatic refresh of short-lived session tokens
Enterprise	Supports GitHub Enterprise via --github-domain flag
Models	Uses Copilot's default model selection
API	OpenAI-compatible chat completions under the hood

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OpenHarness implements the core Agent Harness pattern with 10 subsystems:

openharness/
  engine/          # 🧠 Agent Loop — query → stream → tool-call → loop
  tools/           # 🔧 43 Tools — file I/O, shell, search, web, MCP
  skills/          # 📚 Knowledge — on-demand skill loading (.md files)
  plugins/         # 🔌 Extensions — commands, hooks, agents, MCP servers
  permissions/     # 🛡️ Safety — multi-level modes, path rules, command deny
  hooks/           # ⚡ Lifecycle — PreToolUse/PostToolUse event hooks
  commands/        # 💬 54 Commands — /help, /commit, /plan, /resume, ...
  mcp/             # 🌐 MCP — Model Context Protocol client
  memory/          # 🧠 Memory — persistent cross-session knowledge
  tasks/           # 📋 Tasks — background task management
  coordinator/     # 🤝 Multi-Agent — subagent spawning, team coordination
  prompts/         # 📝 Context — system prompt assembly, CLAUDE.md, skills
  config/          # ⚙️ Settings — multi-layer config, migrations
  ui/              # 🖥️ React TUI — backend protocol + frontend

The heart of the harness. One loop, endlessly composable:

while True:
    response = await api.stream(messages, tools)
    
    if response.stop_reason != "tool_use":
        break  # Model is done
    
    for tool_call in response.tool_uses:
        # Permission check → Hook → Execute → Hook → Result
        result = await harness.execute_tool(tool_call)
    
    messages.append(tool_results)
    # Loop continues — model sees results, decides next action

The model decides what to do. The harness handles how — safely, efficiently, with full observability.

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Category	Tools	Description
File I/O	Bash, Read, Write, Edit, Glob, Grep	Core file operations with permission checks
Search	WebFetch, WebSearch, ToolSearch, LSP	Web and code search capabilities
Notebook	NotebookEdit	Jupyter notebook cell editing
Agent	Agent, SendMessage, TeamCreate/Delete	Subagent spawning and coordination
Task	TaskCreate/Get/List/Update/Stop/Output	Background task management
MCP	MCPTool, ListMcpResources, ReadMcpResource	Model Context Protocol integration
Mode	EnterPlanMode, ExitPlanMode, Worktree	Workflow mode switching
Schedule	CronCreate/List/Delete, RemoteTrigger	Scheduled and remote execution
Meta	Skill, Config, Brief, Sleep, AskUser	Knowledge loading, configuration, interaction

Every tool has:

• Pydantic input validation — structured, type-safe inputs
• Self-describing JSON Schema — models understand tools automatically
• Permission integration — checked before every execution
• Hook support — PreToolUse/PostToolUse lifecycle events

Skills are on-demand knowledge — loaded only when the model needs them:

Available Skills:
- commit: Create clean, well-structured git commits
- review: Review code for bugs, security issues, and quality
- debug: Diagnose and fix bugs systematically
- plan: Design an implementation plan before coding
- test: Write and run tests for code
- simplify: Refactor code to be simpler and more maintainable
- pdf: PDF processing with pypdf (from anthropics/skills)
- xlsx: Excel operations (from anthropics/skills)
- ... 40+ more

Compatible with anthropics/skills — just copy .md files to ~/.openharness/skills/.

Compatible with claude-code plugins. Tested with 12 official plugins:

Plugin	Type	What it does
commit-commands	Commands	Git commit, push, PR workflows
security-guidance	Hooks	Security warnings on file edits
hookify	Commands + Agents	Create custom behavior hooks
feature-dev	Commands	Feature development workflow
code-review	Agents	Multi-agent PR review
pr-review-toolkit	Agents	Specialized PR review agents

# Manage plugins
oh plugin list
oh plugin install <source>
oh plugin enable <name>

OpenHarness is useful as a lightweight harness layer around Claude-style tooling conventions:

• OpenClaw-oriented workflows can reuse Markdown-first knowledge and command-driven collaboration patterns.
• Claude-style plugins and skills stay portable because OpenHarness keeps those formats familiar.
• ClawTeam-style multi-agent work maps well onto the built-in team, task, and background execution primitives.

For concrete usage ideas instead of generic claims, see docs/SHOWCASE.md.

Multi-level safety with fine-grained control:

Mode	Behavior	Use Case
Default	Ask before write/execute	Daily development
Auto	Allow everything	Sandboxed environments
Plan Mode	Block all writes	Large refactors, review first

Path-level rules in settings.json:

{
  "permission": {
    "mode": "default",
    "path_rules": [{"pattern": "/etc/*", "allow": false}],
    "denied_commands": ["rm -rf /", "DROP TABLE *"]
  }
}

React/Ink TUI with full interactive experience:

• Command picker: Type / → arrow keys to select → Enter
• Permission dialog: Interactive y/n with tool details
• Mode switcher: /permissions → select from list
• Session resume: /resume → pick from history
• Animated spinner: Real-time feedback during tool execution
• Keyboard shortcuts: Shown at the bottom, context-aware

oh [OPTIONS] COMMAND [ARGS]

Session:     -c/--continue, -r/--resume, -n/--name
Model:       -m/--model, --effort, --max-turns
Output:      -p/--print, --output-format text|json|stream-json
Permissions: --permission-mode, --dangerously-skip-permissions
Context:     -s/--system-prompt, --append-system-prompt, --settings
Advanced:    -d/--debug, --mcp-config, --bare

Subcommands: oh setup | oh provider | oh auth | oh mcp | oh plugin

ohmo is a personal-agent app built on top of OpenHarness. It is packaged alongside oh, with its own workspace and gateway:

# Initialize personal workspace
ohmo init

# Configure gateway channels and pick a provider profile
ohmo config

# Run the personal agent
ohmo

# Run the gateway in foreground
ohmo gateway run

# Check or restart the gateway
ohmo gateway status
ohmo gateway restart

Key concepts:

• ~/.ohmo/
  • personal workspace root
• soul.md
  • long-term agent personality and behavior
• identity.md
  • who ohmo is
• user.md
  • user profile and preferences
• BOOTSTRAP.md
  • first-run landing ritual
• memory/
  • personal memory
• gateway.json
  • selected provider profile and channel configuration

ohmo config uses the same workflow language as oh setup, so you can point the personal-agent gateway at:

• Anthropic-Compatible API
• Claude Subscription
• OpenAI-Compatible API
• Codex Subscription
• GitHub Copilot

ohmo init creates the home workspace once. After that, use ohmo config to update provider and channel settings; if the gateway is already running, the config flow can restart it for you.

Currently ohmo init / ohmo config can guide channel setup for:

• Telegram
• Slack
• Discord
• Feishu

---

Suite	Tests	Status
Unit + Integration	114	✅ All passing
CLI Flags E2E	6	✅ Real model calls
Harness Features E2E	9	✅ Retry, skills, parallel, permissions
React TUI E2E	3	✅ Welcome, conversation, status
TUI Interactions E2E	4	✅ Commands, permissions, shortcuts
Real Skills + Plugins	12	✅ anthropics/skills + claude-code/plugins

# Run all tests
uv run pytest -q                           # 114 unit/integration
python scripts/test_harness_features.py     # Harness E2E
python scripts/test_real_skills_plugins.py  # Real plugins E2E

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from pydantic import BaseModel, Field
from openharness.tools.base import BaseTool, ToolExecutionContext, ToolResult

class MyToolInput(BaseModel):
    query: str = Field(description="Search query")

class MyTool(BaseTool):
    name = "my_tool"
    description = "Does something useful"
    input_model = MyToolInput

    async def execute(self, arguments: MyToolInput, context: ToolExecutionContext) -> ToolResult:
        return ToolResult(output=f"Result for: {arguments.query}")

Create ~/.openharness/skills/my-skill.md:

---
name: my-skill
description: Expert guidance for my specific domain
---

# My Skill

## When to use
Use when the user asks about [your domain].

## Workflow
1. Step one
2. Step two
...

Create .openharness/plugins/my-plugin/.claude-plugin/plugin.json:

{
  "name": "my-plugin",
  "version": "1.0.0",
  "description": "My custom plugin"
}

Add commands in commands/*.md, hooks in hooks/hooks.json, agents in agents/*.md.

---

OpenHarness is most useful when treated as a small, inspectable harness you can adapt to a real workflow:

• Repo coding assistant for reading code, patching files, and running checks locally.
• Headless scripting tool for json and stream-json output in automation flows.
• Plugin and skill testbed for experimenting with Claude-style extensions.
• Multi-agent prototype harness for task delegation and background execution.
• Provider comparison sandbox across Anthropic-compatible backends.

See docs/SHOWCASE.md for short, reproducible examples.

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OpenHarness is a community-driven research project. We welcome contributions in:

Area	Examples
Tools	New tool implementations for specific domains
Skills	Domain knowledge .md files (finance, science, DevOps...)
Plugins	Workflow plugins with commands, hooks, agents
Providers	Support for more LLM backends (OpenAI, Ollama, etc.)
Multi-Agent	Coordination protocols, team patterns
Testing	E2E scenarios, edge cases, benchmarks
Documentation	Architecture guides, tutorials, translations

# Development setup
git clone https://github.com/HKUDS/OpenHarness.git
cd OpenHarness
uv sync --extra dev
uv run pytest -q  # Verify everything works

Useful contributor entry points:

• CONTRIBUTING.md for setup, checks, and PR expectations
• CHANGELOG.md for user-visible changes
• docs/SHOWCASE.md for real-world usage patterns worth documenting

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MIT — see LICENSE.

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Oh my Harness!
The model is the agent. The code is the harness.

Thanks for visiting ✨ OpenHarness!