Documentation Index
Fetch the complete documentation index at: https://docs.monk.io/llms.txt
Use this file to discover all available pages before exploring further.
Overview
This template provides a production-ready OpenClaw AI assistant gateway as a Monk runnable. You can:
- Run it directly to get a managed AI gateway for messaging platforms
- Inherit it in your own applications to add AI assistant capabilities
- Combine with Ollama for local LLM inference
OpenClaw is an AI assistant gateway that connects to messaging platforms (WhatsApp, Telegram, Discord, Slack, Signal) and provides AI-powered responses using various LLM providers.
What this template manages
- OpenClaw gateway container (
alpine/openclaw image)
- HTTP API service on port 18789
- Bridge service on port 18790
- Configuration and workspace persistence
- Health checks (readiness and liveness)
- Optional Ollama integration for local LLMs
Quick start (run directly)
- Load templates
- Add required secrets
monk secrets add -g openclaw-gateway-token="$(openssl rand -hex 32)"
- Run OpenClaw gateway
monk run openclaw/gateway
localhost:18789.
With Ollama integration
For local LLM inference without external API dependencies:
- Load both templates
- Add secrets
monk secrets add -g openclaw-gateway-token="$(openssl rand -hex 32)"
- Run the combined stack
# CPU version
monk run openclaw/stack-ollama
# GPU version (requires NVIDIA GPU)
monk run openclaw/stack-ollama-gpu
- Pull a model
# Fast model for CPU
monk do openclaw/ollama/pull-model model=qwen2.5:0.5b
# Or larger model for GPU
monk do openclaw/ollama/pull-model model=llama3.3
Configuration
Key variables you can customize:
variables:
openclaw-image: "alpine/openclaw:2026.2.1" # container image
gateway-port: 18789 # HTTP API port
bridge-port: 18790 # bridge port
gateway-bind: "lan" # bind mode (lan, loopback, public)
# Gateway authentication token (required)
monk secrets add -g openclaw-gateway-token="$(openssl rand -hex 32)"
# Optional: Claude session keys
monk secrets add -g claude-ai-session-key="your-key"
Changing the Default Model
The template comes with a pre-configured default model (qwen2.5:0.5b for CPU, llama3.3 for GPU). If you want to use a different Ollama model, you need to:
- Pull the new model:
monk do openclaw/ollama/pull-model model=your-model-name
- Update the gateway configuration by modifying
openclaw.yaml:
Find the gateway-ollama section and update the models list and primary model:
# In the files.ollama-config.contents JSON:
"models": [
{ "id": "your-model-name", "name": "Your Model Name" },
# ... other models
]
# And update the default:
"agents": {
"defaults": {
"model": {
"primary": "ollama/your-model-name"
}
}
}
- Reload and restart:
monk load MANIFEST
monk stop openclaw/stack-ollama
monk run openclaw/stack-ollama
Example: Using Mistral
# Pull the model
monk do openclaw/ollama/pull-model model=mistral
# Edit openclaw.yaml to add mistral to models list and set as primary:
# "models": [{ "id": "mistral", "name": "Mistral 7B" }, ...]
# "primary": "ollama/mistral"
# Reload
monk load MANIFEST
monk stop openclaw/stack-ollama
monk run openclaw/stack-ollama
Note: The model ID in the configuration must exactly match the model name in Ollama (as shown by monk do openclaw/ollama/list-models).
Use by inheritance
Inherit the OpenClaw runnable in your application:
namespace: myapp
ai-gateway:
defines: runnable
inherits: openclaw/gateway
variables:
gateway-port:
value: 8080
backend:
defines: runnable
containers:
api:
image: myorg/backend
connections:
openclaw:
runnable: ai-gateway
service: http
variables:
openclaw-host:
value: <- connection-hostname("openclaw")
openclaw-port:
value: "18789"
Ports and connectivity
- Service
http: TCP port 18789 (Gateway API)
- Service
bridge: TCP port 18790 (Bridge connections)
- Health endpoint:
GET /health
From other runnables, use connection-hostname("\<connection-name>") to resolve the OpenClaw host.
Persistence
- Configuration:
${monk-volume-path}/openclaw/config:/home/node/.openclaw
- Workspace:
${monk-volume-path}/openclaw/workspace:/home/node/.openclaw/workspace
- Ollama models:
${monk-volume-path}/ollama:/root/.ollama
Features
- Multi-platform messaging support (WhatsApp, Telegram, Discord, Slack, Signal)
- Multiple LLM provider support (OpenAI, Anthropic, Ollama, and more)
- Local LLM inference with Ollama integration
- Persistent configuration and workspaces
- Health monitoring with readiness/liveness checks
- CLI tools for channel management
Available Actions
| Action | Description | Command |
|---|
onboard | Run onboarding wizard | monk do openclaw/cli/onboard |
channels-login | Login to WhatsApp | monk do openclaw/cli/channels-login |
channels-status | Check channels status | monk do openclaw/cli/channels-status |
health | Gateway health check | monk do openclaw/cli/health |
pull-model | Pull Ollama model | monk do openclaw/ollama/pull-model model=llama3.3 |
list-models | List Ollama models | monk do openclaw/ollama/list-models |
Recommended Ollama Models
For CPU (no GPU):
| Model | Size | Speed | Use Case |
|---|
| qwen2.5:0.5b | 398MB | ⚡⚡⚡ | Testing, fast responses |
| tinyllama | 637MB | ⚡⚡⚡ | Basic tasks |
| llama3.2:1b | 1.3GB | ⚡⚡ | General purpose |
| gemma2:2b | 1.6GB | ⚡⚡ | Quality responses |
| Model | Size | Speed | Use Case |
|---|
| llama3.3 | 4.7GB | ⚡⚡⚡ | High quality |
| qwen2.5-coder:32b | 19GB | ⚡⚡ | Code generation |
| deepseek-r1:32b | 19GB | ⚡⚡ | Reasoning tasks |
- Combine with
ollama/ollama for local LLM inference
- Use with database templates for persistent storage
- Integrate with monitoring templates for observability
Troubleshooting
monk logs -f openclaw/gateway
monk logs -f openclaw/ollama
curl http://localhost:18789/health
monk shell openclaw/gateway
- For Ollama 404 errors, ensure a model is pulled:
monk do openclaw/ollama/pull-model model=qwen2.5:0.5b
Links
