🦞 ClawHarness

Make cheap models perform like Claude Code Opus

The harness layer reverse-engineered from the Claude Code leak — plug in any model, get top-tier agent capability

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On March 31, 2026, Claude Code's full source leaked via npm source maps — 515,000 lines of TypeScript.

We reverse-engineered every mechanism that makes it good: the agent loop, context compression, tool orchestration, security layer, prompt engineering — and rebuilt it as an open harness.

Now you can plug in DeepSeek, Doubao, Qwen, or any cheap model — and the harness does the heavy lifting to make it perform like Claude Code's Opus tier.

What is this?

When you use Claude Code, the model is only half the story. The other half is the engineering around it — the harness:

• How it decides which tools to call and in what order
• How it compresses context so conversations never break
• How it recovers from errors, truncation, and rate limits
• How it validates shell commands to prevent damage
• How the prompt is engineered with 100+ conditional sections

That harness is what makes Opus feel like Opus. Without it, even the best model is just a chatbot.

ClawHarness gives that same harness to any model. When you pair a strong-but-cheap model (DeepSeek at $0.14/M tokens) with production-grade agent engineering, you get remarkably close to Claude Code's top-tier output — at 1/10th the price.

This isn't theory. Every mechanism was reverse-engineered from the actual leaked Claude Code source:

Claude Code mechanism	ClawHarness implementation
while(tool_call) agent loop	✅ Full state machine with 6 transition types
4 context compression strategies	✅ 5 strategies (micro, snip, group, auto, reactive)
10,000-line Bash security system	✅ 1,030 lines: command semantics DB (130+ commands), path sandbox, readonly validation
Prompt cache boundary (__DYNAMIC_BOUNDARY__)	✅ Static/dynamic prompt split
Tool orchestration (parallel read, serial write)	✅ Automatic partitioning
max_output_tokens recovery	✅ Auto-continue on truncation
413 prompt-too-long recovery	✅ Reactive compact + retry
Sub-agent spawning	✅ Isolated context per sub-agent
CLAUDE.md project instructions	✅ Multi-level HARNESS.md (global → project → directory)
Session memory	✅ Auto-extract learnings, load next session

Plus what Claude Code doesn't have:

Feature	Claude Code	ClawHarness
Multi-model support	❌ Anthropic only	✅ Any OpenAI-compatible API
Smart model routing	❌	✅ Hard tasks → strong model, easy → cheap
Open source	❌	✅ MIT
Cost	$100+/mo	$5-20/mo

The killer feature: Smart Model Routing

# Hard tasks → DeepSeek (strong reasoning)
# Simple tasks → Doubao (10x cheaper)
# ClawHarness decides per-turn, automatically

DEEPSEEK_API_KEY=sk-xxx DOUBAO_API_KEY=yyy \
  npx tsx src/index.ts --model=deepseek --router=doubao

Result: Claude Code-tier quality at 1/10th the cost. The router analyzes each turn's complexity (keywords, message length, error context) and picks the right model.

Quick Start

git clone https://github.com/agenmod/claw-harness.git
cd claw-harness
npm install

# Any provider — just set the env var:
export DEEPSEEK_API_KEY="sk-..."
npx tsx src/index.ts

That's it. You're running a full coding agent with 22 tools, context compression, security analysis, and session persistence.

Why ClawHarness?

Feature	Claude Code	Other harnesses	ClawHarness
Model support	Anthropic only	Usually one	Any OpenAI-compatible API
Monthly cost	$100+	Varies	$5-20 with DeepSeek/Doubao
Smart routing	No	No	Yes — auto strong/cheap per turn
Tools	40+ (closed)	3-10 skeleton	22 production-ready
Bash security	10K lines (closed)	Minimal	1,030 lines: semantic analysis + path sandbox
Context management	4 strategies (closed)	Basic/none	5 strategies: micro→snip→group→auto→reactive
LSP integration	Yes (closed)	No	Yes — TS, Python, Rust, Go, Java, C++
Auto memory	Yes (closed)	No	Yes — cross-session learning
State machine	Complex (closed)	Simple loop	6 transition types with error recovery
Language	English	English	English + 中文
Source	Proprietary	Open	Open source, MIT

22 Built-in Tools

Core (6)

Tool	Description
Bash	Shell execution with 130+ command security rules, path sandboxing, sed analysis, readonly enforcement
Read	Smart file reading — binary detection, PDF text extraction, image handling, large file pagination
Write	File creation with auto-snapshot for undo
Edit	String replacement with fuzzy match hints, duplicate detection, diff preview, replace_all mode
Glob	File pattern search (proper glob library)
Grep	Content search with ripgrep acceleration

Web & Search (2)

Tool	Description
WebFetch	URL content fetching with HTML readability extraction, JSON pretty-printing
WebSearch	Web search via DuckDuckGo (no API key needed)

Agent & Planning (4)

Tool	Description
Agent	Spawn isolated sub-agents for parallel subtasks
EnterPlanMode	Switch to read-only planning (model can self-activate)
ExitPlanMode	Return to execution mode
TodoWrite	Structured task tracking for complex multi-step work

Code Intelligence (2)

Tool	Description
LSP	Go-to-definition, find references, hover info — auto-detects language server
NotebookEdit	Jupyter notebook cell editing

DevOps & Config (6)

Tool	Description
EnterWorktree	Git worktree isolation for safe experimentation
ExitWorktree	Exit worktree with optional merge back
Config	Runtime config management
Skill	Load domain-specific instruction files
ToolSearch	Discover tools by keyword
AskUser	Ask for clarification when genuinely needed

Architecture

User input
  │
  ▼
┌──────────────────────────────────────────────┐
│              AgentEngine                      │
│         (state machine, 6 transitions)        │
│                                               │
│  ┌──────────────────────────────────────────┐ │
│  │  while (tool_calls) {                    │ │
│  │    compress_if_needed()  // 5 strategies  │ │
│  │    stream = provider.chat(messages,tools)  │ │
│  │    for chunk in stream:                   │ │
│  │      yield text / tool_calls              │ │
│  │    execute_tools()      // parallel safe  │ │
│  │    run_hooks()          // pre/post       │ │
│  │    check_recovery()     // 413, truncate  │ │
│  │  }                                        │ │
│  └──────────────────────────────────────────┘ │
│                                               │
│  ┌────────────┐  ┌──────────────────────────┐ │
│  │ Provider   │  │ Security                 │ │
│  │ ┌────────┐ │  │ ┌──────────────────────┐ │ │
│  │ │OpenAI- │ │  │ │ 130+ cmd rules       │ │ │
│  │ │Compat  │ │  │ │ Path sandbox         │ │ │
│  │ ├────────┤ │  │ │ Readonly validation   │ │ │
│  │ │Retry   │ │  │ │ Permission rules     │ │ │
│  │ ├────────┤ │  │ └──────────────────────┘ │ │
│  │ │Router  │ │  └──────────────────────────┘ │
│  │ └────────┘ │                               │
│  └────────────┘  ┌──────────────────────────┐ │
│                  │ Context (5 strategies)    │ │
│  ┌────────────┐  │ micro → snip → group     │ │
│  │ 22 Tools   │  │ → auto → reactive        │ │
│  └────────────┘  └──────────────────────────┘ │
└──────────────────────────────────────────────┘

Supported Providers

Any OpenAI-compatible API works out of the box. Just set one env var:

Provider	Env Var	Default Model	Best For
DeepSeek	DEEPSEEK_API_KEY	deepseek-chat	Reasoning, thinking mode, value
Doubao (豆包)	DOUBAO_API_KEY	doubao-seed-code	Speed, cost, Chinese code
Qwen (通义)	QWEN_API_KEY	qwen-max	Long context (128K+)
OpenAI	OPENAI_API_KEY	gpt-4o	Proven quality
Custom	~/.clawharness/config.json	Any	Your infra

CLI

# Interactive REPL
npx tsx src/index.ts

# One-shot mode
npx tsx src/index.ts "create a REST API with Express"

# Smart routing
npx tsx src/index.ts --model=deepseek --router=doubao

# Flags
--model=NAME     # Choose provider
--router=NAME    # Enable smart routing (strong=model, cheap=router)
--trust          # Auto-approve all operations
--readonly       # Read-only mode
--verbose        # Show thinking tokens & cost
--resume         # Resume last session

Slash Commands

/model [name]    — show or switch model at runtime
/tools           — list all 22 tools
/cost            — token usage & cost estimate
/save            — persist session
/undo <path>     — restore file (pre-edit snapshot)
/allow <tool>    — always-allow rule
/deny <tool>     — always-deny rule
/clear           — reset conversation
/help            — all commands

Approaching Claude Code Capability

ClawHarness implements the core patterns that make Claude Code effective:

1. Agent loop — the while(tool_call) pattern with full state machine (not a simple retry loop)
2. Tool-first design — 22 tools the model can compose freely, no hardcoded workflows
3. Security in depth — command semantic analysis understands find -exec rm {} \; vs find -name "*.ts", not just regex matching
4. Context never dies — 5 compression strategies from cheap (snip tool results) to expensive (model summary) to emergency (413 recovery)
5. Memory persists — auto-extracts learnings from each session, loads them next time
6. Model-agnostic — swap the brain without changing the harness. Today's best model might not be tomorrow's

When you combine a strong model (DeepSeek-R1 for reasoning) with this harness layer, you get remarkably close to Claude Code's Opus-tier output — at a fraction of the cost.

The Open Claw 🦞 Harness

ClawHarness is the core harness for the Open Claw ecosystem — designed so that when you plug in cheap models, they perform as close to Claude Code's Opus tier as the underlying model allows.

The harness compensates for weaker models by:

1. Better prompt engineering — 224 lines of carefully structured system prompt with tool-specific behavioral guidance, length anchoring, and safety constraints
2. Smarter tool orchestration — read-only tools run in parallel, writes run serially, results are automatically budgeted (large outputs saved to disk)
3. Context that never dies — 5 compression strategies kick in automatically so the model always has relevant context, even in 100+ turn sessions
4. Error recovery — truncated output auto-continues, 413 errors trigger emergency compression, failed tools get retried
5. Smart routing — the hardest 20% of tasks go to a strong model, the easy 80% go to a cheap one

The better the harness, the less the model matters. That's the thesis.

# Strong model + cheap model = best of both worlds
DEEPSEEK_API_KEY=sk-xxx DOUBAO_API_KEY=yyy \
  npx tsx src/index.ts --model=deepseek --router=doubao

Use with any IDE (MCP Server)

ClawHarness runs as an MCP server — any MCP-compatible IDE or tool can connect instantly. No plugin development needed.

# Start MCP server
clh --mcp-server

Cursor

~/.cursor/mcp.json:

{
  "mcpServers": {
    "clawharness": {
      "command": "npx",
      "args": ["tsx", "/path/to/clawharness/src/entrypoints/mcp-server.ts"]
    }
  }
}

VSCode + Continue.dev

~/.continue/config.json:

{
  "experimental": {
    "modelContextProtocolServers": [{
      "transport": {
        "type": "stdio",
        "command": "npx",
        "args": ["tsx", "/path/to/clawharness/src/entrypoints/mcp-server.ts"]
      }
    }]
  }
}

VSCode + Cline

Settings → MCP Servers → Add Server:

• Command: npx
• Args: tsx /path/to/clawharness/src/entrypoints/mcp-server.ts

Cherry Studio

Settings → MCP Servers → Add:

• Type: stdio
• Command: npx tsx /path/to/clawharness/src/entrypoints/mcp-server.ts

Claude Desktop

~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "clawharness": {
      "command": "npx",
      "args": ["tsx", "/path/to/clawharness/src/entrypoints/mcp-server.ts"]
    }
  }
}

Windsurf

MCP settings → Add server with the same command.

MCP Tools Exposed

Tool	Description
claw_run	Full agent loop — give it a task, it uses all 22 tools
claw_search	Search code with ripgrep
claw_read	Read file with line numbers
claw_edit	Edit file (string replacement)
claw_bash	Run shell command

SDK / Programmatic API

Use ClawHarness from your own code:

import { createSession } from 'clawharness/sdk'

const session = createSession({
  provider: 'deepseek',
  apiKey: process.env.DEEPSEEK_API_KEY,
})

// Run a task
const result = await session.run("create a REST API with Express")
console.log(result.text)

// Or stream events
for await (const event of session.stream("fix the bug in app.ts")) {
  if (event.type === 'text') process.stdout.write(event.content)
  if (event.type === 'tool_done') console.log(`[${event.call.name}] done`)
}

CLI SDK Modes

# One-shot text output (like CC's -p flag)
clh --print "create hello.py"

# One-shot JSON output
clh --json "create hello.py"

# Pipe mode (stdin JSON → stdout JSON)
echo '{"prompt":"create hello.py"}' | clh --pipe

Contributing

PRs welcome. See CONTEXT.md for architecture details and the roadmap.

npm install
DEEPSEEK_API_KEY=sk-xxx npx tsx src/index.ts

License

MIT — do whatever you want with it.