`oh` — OpenHarness & `ohmo`

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**OpenHarness** delivers core lightweight agent infrastructure: tool-use, skills, memory, and multi-agent coordination. **ohmo** is a personal AI agent built on OpenHarness — not another chatbot, but an assistant that actually works for you over long sessions. Chat with ohmo in Feishu / Slack / Telegram / Discord, and it forks branches, writes code, runs tests, and opens PRs on its own. ohmo runs on your existing Claude Code or Codex subscription — no extra API key needed. **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.

OpenHarness Terminal Demo

--- ## ✨ OpenHarness's Key Harness Features

🔄 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)

--- ## 🤔 What is an Agent Harness? 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**.

Harness = Tools + Knowledge + Observation + Action + Permissions

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 --- ## 📰 What's New - **Unreleased** 🔍 **Dry-run safe preview**: - `oh --dry-run` previews resolved runtime settings, auth state, skills, commands, tools, and configured MCP servers without executing the model, tools, or subagents. - Dry-run now reports a `ready` / `warning` / `blocked` readiness verdict with concrete next-step suggestions such as fixing auth, fixing MCP config, or running the prompt directly. - Prompt previews include likely matching skills and tools, while slash-command previews show whether the command is mostly read-only or stateful. - **2026-04-18** ⚙️ **v0.1.7** — Packaging & TUI polish: - Install script now links `oh`, `ohmo`, and `openharness` into `~/.local/bin` instead of prepending the virtualenv `bin` directory to `PATH`, which avoids clobbering Conda-managed shells. - React TUI now supports `Shift+Enter` to insert a newline while keeping plain `Enter` as submit. - Busy-state animation in the React TUI is quieter and less error-prone on Windows terminals, with conservative spinner frames and reduced flashing. - **2026-04-10** 🧠 **v0.1.6** — Auto-Compaction & Markdown TUI: - Auto-Compaction preserves task state and channel logs across context compression — agents can run multi-day sessions without manual compact/clear - Subprocess teammates run in headless worker mode; agent team creation stabilized - Assistant messages now render full Markdown in the React TUI - `ohmo` gains channel slash commands and multimodal attachment support - **2026-04-08** 🔌 **v0.1.5** — MCP HTTP transport & Swarm polling: - MCP protocol adds HTTP transport, auto-reconnect on disconnect, and tool-only server compatibility - JSON Schema types inferred for MCP tool inputs — no manual type mapping needed - `ohmo` channels support file attachments and multimodal gateway messages - Subprocess agents are now pollable in real runs; permission modals serialized to prevent input swallowing - **2026-04-08** 🌙 **v0.1.4** — Multi-provider auth & Moonshot/Kimi: - Native Moonshot/Kimi provider with `reasoning_content` support for thinking models - Auth overhaul: fixed provider-switching key mismatch, `OPENAI_BASE_URL` env override, profile-scoped credential priority - MCP gracefully handles disconnected servers in `call_tool` / `read_resource` - Security: built-in sensitive-path protection in PermissionChecker, hardened `web_fetch` URL validation - Stability: EIO crash recovery in Ink TUI, `--debug` logging, Windows cmd flash fix - **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

--- ## 🚀 Quick Start ### 1. Install #### Linux / macOS / WSL ```bash # One-click install curl -fsSL https://raw.githubusercontent.com/HKUDS/OpenHarness/main/scripts/install.sh | bash # Or via pip pip install openharness-ai ``` #### Windows (Native) ```powershell # One-click install (PowerShell) iex (Invoke-WebRequest -Uri 'https://raw.githubusercontent.com/HKUDS/OpenHarness/main/scripts/install.ps1') # Or via pip pip install openharness-ai ``` **Note**: Windows support is now native. In PowerShell, use `openh` instead of `oh` because `oh` can resolve to the built-in `Out-Host` alias. ### 2. Configure ```bash oh setup # interactive wizard — pick a provider, authenticate, done # On Windows PowerShell, use: openh setup ``` Supports **Claude / OpenAI / Copilot / Codex / Moonshot(Kimi) / GLM / MiniMax / NVIDIA NIM** and any compatible endpoint. ### 3. Run ```bash oh # On Windows PowerShell, use: openh ```

OpenHarness Landing Screen

### 4. Set up ohmo (Personal Agent) Want an AI agent that works for you from Feishu / Slack / Telegram / Discord? ```bash ohmo init # initialize ~/.ohmo workspace ohmo config # configure channels and provider ohmo gateway start # start the gateway — ohmo is now live in your chat app ``` ohmo runs on your existing **Claude Code subscription** or **Codex subscription** — no extra API key needed. ### Non-Interactive Mode (Pipes & Scripts) ```bash # 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 ``` ### Dry Run (Safe Preview) Use `--dry-run` when you want to inspect what OpenHarness would use before any live execution starts. ```bash # Preview an interactive session setup oh --dry-run # Preview one prompt without executing the model or tools oh --dry-run -p "Review this bug fix and grep for failing tests" # Preview a slash command path oh --dry-run -p "/plugin list" # Get structured output for scripts or channels oh --dry-run -p "Explain this repository" --output-format json ``` Dry-run is intentionally static: - It does **not** call the model - It does **not** execute tools or spawn subagents - It does **not** connect to MCP servers - It **does** resolve settings, auth status, prompt assembly, skills, commands, tools, and obvious MCP config problems Readiness levels: - `ready`: configuration looks usable; the next suggested action is usually to run the prompt directly - `warning`: OpenHarness can resolve the session, but something important still looks wrong, such as broken MCP config or missing auth for later model work - `blocked`: the requested path will not run successfully as-is, for example an unknown slash command or a prompt that cannot resolve a runtime client `next actions` in the dry-run output tell you the shortest fix or follow-up step, such as: - run `oh auth login` - fix or disable broken MCP configuration - run the prompt directly with `oh -p "..."` or open the interactive UI with `oh` ## 🔌 Provider Compatibility OpenHarness treats providers as **workflows** backed by named profiles. In day-to-day use, prefer: ```bash oh setup oh provider list oh provider use ``` ### Built-in Workflows | 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 | ### Compatible API Families #### Anthropic-Compatible API 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` | #### OpenAI-Compatible API 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` | | **NVIDIA NIM** | `https://integrate.api.nvidia.com/v1` | `openai/gpt-oss-120b`, `nvidia/llama-3.3-nemotron-super-49b-v1` | | **Google Gemini** | `https://generativelanguage.googleapis.com/v1beta/openai` | `gemini-2.5-flash`, `gemini-2.5-pro` | | **Groq** | `https://api.groq.com/openai/v1` | `llama-3.3-70b-versatile` | | **Ollama (local)** | `http://localhost:11434/v1` | any local model | ### Advanced Profile Management ```bash # 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. ### Ollama (Local Models) Run local models through Ollama's OpenAI-compatible endpoint: ```bash # Add an Ollama provider profile oh provider add ollama \ --label "Ollama" \ --provider Ollama \ --api-format openai \ --auth-source openai_api_key \ --model glm-4.7-flash:q8_0 \ --base-url http://localhost:11434/v1 ``` ``` Saved provider profile: ollama ``` ```bash # Activate and verify oh provider use ollama ``` ``` Activated provider profile: ollama ``` ```bash oh provider list ``` ``` claude-api: Anthropic-Compatible API [ready] ... moonshot: Moonshot (Kimi) [missing auth] auth=moonshot_api_key model=kimi-k2.5 base_url=https://api.moonshot.cn/v1 * ollama: Ollama [ready] auth=openai_api_key model=glm-4.7-flash:q8_0 base_url=http://localhost:11434/v1 ``` ### GitHub Copilot Format (`--api-format copilot`) Use your existing GitHub Copilot subscription as the LLM backend. Authentication uses GitHub's OAuth device flow — no API keys needed. ```bash # 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 | --- ## 🏗️ Harness Architecture 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 Agent Loop The heart of the harness. One loop, endlessly composable: ```python 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. ### Harness Flow ```mermaid flowchart LR U[User Prompt] --> C[CLI or React TUI] C --> R[RuntimeBundle] R --> Q[QueryEngine] Q --> A[Anthropic-compatible API Client] A -->|tool_use| T[Tool Registry] T --> P[Permissions + Hooks] P --> X[Files Shell Web MCP Tasks] X --> Q ``` --- ## ✨ Features ### 🔧 Tools (43+) | 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 System 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 ``` Skills can live in bundled, user, ohmo, project, or plugin locations. User-level skills are loaded from: ```text ~/.openharness/skills//SKILL.md ~/.claude/skills//SKILL.md ~/.agents/skills//SKILL.md ``` Project-level skills are enabled by default and are discovered from the current working directory up to the git root: ```text /.openharness/skills//SKILL.md /.agents/skills//SKILL.md /.claude/skills//SKILL.md ``` Disable project skills for untrusted repositories with: ```bash oh config set allow_project_skills false ``` Use `/skills` to list loaded skills with their source and path. User-invocable skills can be run directly as slash commands, for example `/deploy staging`. **Compatible with [anthropics/skills](https://github.com/anthropics/skills)** — use the `SKILL.md` directory layout above. ### 🌐 Web search and proxy settings Built-in `web_search` uses DuckDuckGo HTML search by default. In regions where that endpoint is unreachable, point OpenHarness at a trusted public HTML search endpoint or your own SearXNG instance: ```bash export OPENHARNESS_WEB_SEARCH_URL="https://your-searxng.example/search" ``` `web_search` and `web_fetch` keep `trust_env=False` for SSRF safety, so they do not automatically inherit `HTTP_PROXY` / `HTTPS_PROXY`. If you need a proxy, opt in with an OpenHarness-specific variable: ```bash export OPENHARNESS_WEB_PROXY="http://127.0.0.1:7890" ``` The proxy URL must be HTTP/HTTPS and cannot contain embedded credentials. ### 🔌 Plugin System **Compatible with [claude-code plugins](https://github.com/anthropics/claude-code/tree/main/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 | ```bash # Manage plugins oh plugin list oh plugin install oh plugin enable ``` ### 🤝 Ecosystem Workflows 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`](docs/SHOWCASE.md). ### 🛡️ Permissions 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`: ```json { "permission": { "mode": "default", "path_rules": [{"pattern": "/etc/*", "allow": false}], "denied_commands": ["rm -rf /", "DROP TABLE *"] } } ``` ### 🖥️ Terminal UI 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 ### 📡 CLI ``` 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 Personal Agent `ohmo` is a personal-agent app built on top of OpenHarness. It is packaged alongside `oh`, with its own workspace and gateway: ```bash # 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 --- ## 📊 Test Results | 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 | ```bash # 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 ``` --- ## 🔧 Extending OpenHarness ### Add a Custom Tool ```python 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}") ``` ### Add a Custom Skill Create `~/.openharness/skills/my-skill.md`: ```markdown --- 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 ... ``` ### Add a Plugin Create `.openharness/plugins/my-plugin/.claude-plugin/plugin.json`: ```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`. --- ## 🌍 Showcase 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`](docs/SHOWCASE.md) for short, reproducible examples. --- ## 🤝 Contributing 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 | ```bash # 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`](CONTRIBUTING.md) for setup, checks, and PR expectations - [`CHANGELOG.md`](CHANGELOG.md) for user-visible changes - [`docs/SHOWCASE.md`](docs/SHOWCASE.md) for real-world usage patterns worth documenting --- ## 🔧 Troubleshooting ### Backspace key in macOS Terminal.app OpenHarness handles both common terminal delete sequences, including the raw `DEL` byte (`0x7f`) that macOS Terminal.app sends for Backspace. If Backspace inserts spaces or visible control characters instead of deleting text, upgrade OpenHarness first. For older versions that do not include this fix, use a terminal that sends a standard Backspace sequence or adjust your terminal keyboard profile as a temporary workaround. --- ## 📄 License MIT — see [LICENSE](LICENSE). ---

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