How to Change the OpenClaw Model: A Complete Guide

What is an OpenClaw Model?

In OpenClaw, a model refers to the AI backend that processes your prompts and returns responses. OpenClaw acts as a routing layer — it doesn't generate responses itself. Instead, it delegates to a configured model provider, which could be a cloud API (like OpenAI or Anthropic) or a locally hosted model (like Ollama or LM Studio).

Changing the model means telling OpenClaw to route requests to a different provider, a different model version, or a different endpoint. This gives you fine-grained control over cost, speed, and output quality.

How Does Model Switching Work in OpenClaw?

OpenClaw resolves the active model through a configuration chain:

Environment variables — set at the system or process level
Config file — typically openClaw.config.json or .env in the project root
Runtime override — passed directly in the API request payload

When a request comes in, OpenClaw reads the model identifier, maps it to a provider, authenticates with the provider's API key, and forwards the prompt. The response is then normalized and returned to your application.

Swapping models doesn't require code changes in most cases — only config changes.

Key Ways to Change the OpenClaw Model

1. Edit the Config File

The most common approach. Open openClaw.config.json (or your equivalent config file) and update the model field:

{
  "model": "gpt-4o",
  "provider": "openai",
  "apiKey": "your-api-key-here"
}

To switch to a different model, change the "model" value:

{
  "model": "claude-3-5-sonnet-20241022",
  "provider": "anthropic",
  "apiKey": "your-anthropic-key"
}

Save the file and restart the OpenClaw server for changes to take effect.

2. Use Environment Variables

Environment variables override the config file and are the recommended approach for production:

OPENCLAW_MODEL=gemini-1.5-pro
OPENCLAW_PROVIDER=google
OPENCLAW_API_KEY=your-google-key

Set these in your .env file or your deployment environment (Docker, cloud platform, etc.).

3. Pass the Model at Runtime

For per-request model switching, include the model in your API call body:

{
  "model": "mistral-large-latest",
  "messages": [
    { "role": "user", "content": "Explain quantum entanglement simply." }
  ]
}

This is useful when different workflows in your application need different models simultaneously.

4. Switch to a Local Model (Ollama / LM Studio)

If you want to run a model locally:

Install Ollama and pull a model: ollama pull llama3
Update your OpenClaw config to point to the local endpoint:

{
  "model": "llama3",
  "provider": "ollama",
  "baseUrl": "http://localhost:11434"
}

No API key is needed for local models.

Benefits of Changing Your OpenClaw Model

Benefit	Details
Cost control	Switch to a cheaper model for low-stakes tasks
Performance tuning	Use a larger model for complex reasoning
Privacy	Route sensitive data to a local model
Redundancy	Fall back to an alternate provider if one is down
Specialization	Use a fine-tuned model for domain-specific tasks

Common Use Cases

🔧 Development vs. Production

Use a fast, cheap model during development; switch to a powerful model when deploying to production for best output quality.

🤖 Multi-Agent Pipelines

Assign different models to different agents based on task complexity — lightweight models for triage, powerful models for synthesis.

💰 Cost Optimization

Route simple classification tasks to a small model and complex generation tasks to a large one, dramatically cutting API spend.

🔒 Compliance & Privacy

Switch to a local model when processing regulated or confidential data to ensure nothing leaves your infrastructure.

Why EasyClaw Wins for Multi-Model Workflows

Managing model switching across config files, environment variables, and runtime overrides gets complex fast — especially in multi-agent pipelines. EasyClaw is a desktop-native AI agent platform that handles model routing, provider authentication, and per-task model assignment through a clean visual interface. No more hunting through .env files or restarting servers.

✅ Switch models per agent or per task from a single dashboard
✅ Supports OpenAI, Anthropic, Google, Mistral, Ollama, and more out of the box
✅ Local model routing with zero API key exposure
✅ Cost tracking across providers in real time
✅ Runs entirely on your machine — your data never leaves your device

Try EasyClaw Free →

Troubleshooting Common Issues

❌ Model not found error

Double-check the exact model identifier string against the provider's documentation. Identifiers are case-sensitive — for example, gpt-4o not GPT-4o.

🔑 Auth errors after switching

Each provider uses its own API key. Switching providers means updating the key, not just the model name. Verify both provider and apiKey are updated together.

⚙️ Changes not taking effect

Environment variables take precedence over config files. If you updated the config but have a conflicting env var set, the env var wins. Check your shell environment with printenv | grep OPENCLAW.

🖥️ Local model connection refused

Ensure the local server (Ollama, LM Studio) is running before starting OpenClaw. Verify the baseUrl port matches the local server's listening port.

Pro tip: Run OpenClaw with verbose logging enabled to see exactly which model identifier is being resolved on each request — this makes debugging config conflicts much faster.

Frequently Asked Questions

Q: Do I need to restart OpenClaw every time I change the model?

A: Only if you change the config file. Environment variable changes require a server restart, but runtime overrides (passing the model in the request body) take effect immediately without any restart.

Q: Can I use multiple models at the same time in OpenClaw?

A: Yes. By passing the model identifier in each individual API request body, different parts of your application — or different agents in a pipeline — can use completely different models simultaneously.

Q: Which model providers does OpenClaw support?

A: OpenClaw supports major cloud providers including OpenAI, Anthropic, Google (Gemini), Mistral, and Cohere, as well as local model runners like Ollama and LM Studio. Any provider with an OpenAI-compatible API endpoint can also be configured manually.

Q: Is it safe to store API keys in the config file?

A: No. For any shared or version-controlled environment, always use environment variables or a secrets manager instead of hardcoding API keys in openClaw.config.json. Add the config file to .gitignore if it contains sensitive values.

Q: Will switching models affect my existing conversation history or memory?

A: No. OpenClaw manages conversation history independently of the model. You can switch models mid-session and the new model will receive the full prior context, though response style and capability may differ.

Q: What happens if the new model doesn't support a feature my app uses (e.g., function calling)?

A: OpenClaw will return an error from the provider. Always verify that your target model supports the specific capabilities (function calling, vision, large context windows, etc.) your application depends on before switching in production.

Final Thoughts

Changing the OpenClaw model is a configuration-level operation that doesn't require rewriting your application logic. Whether you're optimizing for cost, speed, privacy, or capability, OpenClaw's layered config system gives you three clean options: edit the config file, set environment variables, or pass the model at request time.

Start with the config file approach for simplicity, and move to environment variables as you scale toward production. For teams running complex multi-agent pipelines, consider a purpose-built tool like EasyClaw that abstracts model routing into a visual interface — eliminating manual config management entirely.

Explore EasyClaw →