OpenClaw Integration

This guide explains how to use OpenClaw with Lynkr as its AI backend, enabling you to route OpenClaw's requests through any LLM provider.

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Overview

OpenClaw is an open-source AI agent framework that supports multiple channels (terminal, Slack, Discord, Telegram, etc.). By connecting it to Lynkr, you can:

• Use any model (Ollama, Bedrock, OpenRouter, Moonshot, etc.) with OpenClaw
• Benefit from Lynkr's complexity-based routing — simple tasks go to fast/cheap models, complex tasks go to powerful ones
• See the actual provider/model used in each response via OpenClaw mode
• Get token optimization (60-80% savings) and prompt caching for free

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Quick Start

1. Start Lynkr

npm install -g lynkr
lynkr start

2. Configure OpenClaw

Add Lynkr as a provider in your OpenClaw configuration (openclaw.json or via the dashboard):

{
  "models": {
    "providers": [
      {
        "name": "lynkr",
        "type": "openai-compatible",
        "base_url": "http://localhost:8081/v1",
        "api_key": "any-value",
        "models": ["auto"]
      }
    ]
  },
  "agents": {
    "defaults": {
      "models": {
        "primary": "lynkr/auto",
        "fallback": "lynkr/auto"
      }
    }
  }
}

3. Enable OpenClaw Mode in Lynkr

Add to your Lynkr .env:

OPENCLAW_MODE=true

This rewrites the generic model: "auto" in responses with the actual provider/model that handled the request (e.g., moonshot/kimi-k2-thinking, ollama/qwen2.5-coder:7b). OpenClaw can then display which model answered each query.

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Tier Routing

Lynkr's tier routing works seamlessly with OpenClaw. Configure your tiers in .env:

# Simple questions → cheap/fast model
TIER_SIMPLE=ollama:llama3.2

# Code reading, research → mid-tier
TIER_MEDIUM=openrouter:anthropic/claude-sonnet-4

# Complex multi-file changes → powerful model
TIER_COMPLEX=bedrock:anthropic.claude-sonnet-4-20250514-v1:0

# Deep reasoning tasks → most capable
TIER_REASONING=bedrock:anthropic.claude-opus-4-20250514-v1:0

OpenClaw sends all requests to lynkr/auto. Lynkr analyzes complexity and routes to the right tier automatically. With OPENCLAW_MODE=true, the response includes the actual model used.

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Supported Endpoints

Lynkr exposes these endpoints for OpenClaw:

Endpoint	Description
POST /v1/chat/completions	Chat API (primary endpoint for OpenClaw)
POST /v1/responses	OpenAI Responses API
GET /v1/models	List available models
POST /v1/embeddings	Embeddings for semantic search
GET /v1/health	Health check
POST /v1/files	File upload
GET /v1/files/:id	File retrieval

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Tool Calling

Lynkr supports full tool calling passthrough for OpenClaw agents. It also handles models that output tool calls as raw XML/text (common with Ollama models like Minimax, Qwen, GLM) by automatically extracting and converting them to structured tool calls.

Supported extraction formats:

• Minimax <invoke> XML
• Hermes/Qwen <tool_call> JSON
• GLM <arg_key>/<arg_value> XML
• Llama <|python_tag|> JSON
• Mistral [TOOL_CALLS] prefix
• DeepSeek Unicode tokens
• GPT-OSS Harmony <|call|>
• Raw JSON fallback

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Extended Thinking

When using models that support extended thinking (Claude 4+, Moonshot K2-thinking), Lynkr passes through thinking blocks and reasoning_content. OpenClaw can display these for transparency.

# No additional config needed — thinking passthrough is automatic

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Self-Hosting with Ollama (Free)

For zero-cost operation, use Ollama as your only provider:

MODEL_PROVIDER=ollama
OLLAMA_MODEL=qwen2.5-coder:latest
OPENCLAW_MODE=true

TIER_SIMPLE=ollama:qwen2.5-coder:7b
TIER_MEDIUM=ollama:qwen2.5-coder:32b
TIER_COMPLEX=ollama:qwen2.5-coder:32b
TIER_REASONING=ollama:qwen2.5-coder:32b

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Troubleshooting

Issue	Solution
OpenClaw can't connect	Verify Lynkr is running: curl http://localhost:8081/health
Model shows "auto" instead of actual model	Enable OPENCLAW_MODE=true in Lynkr .env
Tool calls appearing as raw text	Lynkr's XML tool extractor handles this automatically — update to latest version
Slow responses	Check tier config — simple queries may be going to expensive cloud models. Use Ollama for TIER_SIMPLE
Rate limiting	Lynkr has built-in rate limiting. Adjust RATE_LIMIT_* env vars if needed

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Docker Deployment

# docker-compose.yml
services:
  lynkr:
    image: lynkr:latest
    ports:
      - "8081:8081"
    environment:
      - MODEL_PROVIDER=ollama
      - OLLAMA_ENDPOINT=http://ollama:11434
      - OPENCLAW_MODE=true
    depends_on:
      - ollama

  ollama:
    image: ollama/ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama

volumes:
  ollama_data:

Then configure OpenClaw to point at http://lynkr:8081/v1 (or http://localhost:8081/v1 if running outside Docker).