OpenClaw + Ollama: Run AI Agents for Free
Quick Answer: OpenClaw is model-agnostic and works with Ollama to run AI agents entirely for free using local LLMs. Both OpenClaw (MIT license) and Ollama (open-source) cost nothing. Configure your openclaw.json to point at Ollama's local server, pull a model like Qwen 2.5 or Llama 3.3, and your agents run with zero API costs. The Gateway supports fallback chains so you can mix local and cloud models.
This guide covers supported models, step-by-step setup, performance comparison against cloud APIs, and which models work best for different automation tasks.
Why Ollama + OpenClaw?
Zero API Costs
No per-token charges, no monthly subscriptions, no usage limits. Run as many agents and automations as your hardware allows without spending a cent on API fees.
Complete Privacy
All data stays on your machine. No prompts, responses, or business data ever leave your network. Perfect for sensitive workflows, healthcare, legal, or any privacy-critical application.
No Rate Limits
Cloud APIs throttle requests during peak hours. Local models have no rate limits — your agent runs as fast as your hardware allows, 24/7, with zero queuing.
Offline Capable
Once models are downloaded, Ollama runs entirely offline. Your OpenClaw agents work without internet access — ideal for air-gapped environments or unreliable connections.
Which Local Models Are Supported?
Ollama supports dozens of open-source models. These are the best options for OpenClaw automation workloads, tested and ranked by our team.
Qwen 2.5 7B
Best all-around local model. General automation, conversation, task planning, structured output
ollama pull qwen2.5:7bLlama 3.3 70B
Complex reasoning, advanced analysis, near-cloud quality. Requires Apple Silicon or dedicated GPU
ollama pull llama3.3:70bGemma 3 4B
Lightweight tasks, edge devices, resource-constrained hardware. Google's best small model
ollama pull gemma3:4bQwen 2.5 Coder 14B
Best local coding model. Code generation, debugging, refactoring, automation scripts
ollama pull qwen2.5-coder:14bDeepSeek R1 14B
Reasoning and chain-of-thought. Complex multi-step problems, planning, analysis
ollama pull deepseek-r1:14bHow Do You Set Up Ollama with OpenClaw Step by Step?
From zero to running free AI agents in under 20 minutes. Requires Node.js 22+ for OpenClaw.
Step 1: Install Ollama
Download and install Ollama from ollama.com. Available for macOS, Linux, and Windows. The installer is lightweight and sets up the local inference server automatically.
- macOS: Download .dmg from ollama.com or run 'brew install ollama'
- Linux: Run 'curl -fsSL https://ollama.com/install.sh | sh'
- Windows: Download the installer from ollama.com/download
- Verify installation: ollama --version
Step 2: Pull a Model
Download your first local model. We recommend starting with Qwen 2.5 7B for general-purpose agents or Qwen 2.5 Coder 14B for coding workflows.
- Run: ollama pull qwen2.5:7b (downloads ~4.7GB)
- For coding agents: ollama pull qwen2.5-coder:14b
- For lightweight setups: ollama pull gemma3:4b
- List installed models: ollama list
Step 3: Start Ollama Server
Launch the Ollama inference server. It runs on localhost:11434 by default and serves the OpenAI-compatible API that OpenClaw connects to.
- Run: ollama serve (starts on http://localhost:11434)
- On macOS, the Ollama app auto-starts the server
- Test it: curl http://localhost:11434/api/tags
- Server runs in background, handles concurrent requests
Step 4: Configure OpenClaw
Update your openclaw.json configuration to point to Ollama as the model provider. OpenClaw's model-agnostic design makes this a simple configuration change.
- Open openclaw.json in your OpenClaw project directory
- Set provider to 'ollama' and base URL to http://localhost:11434
- Specify model name (e.g., 'qwen2.5:7b', 'llama3.3')
- Optional: Configure Gateway fallback chain for hybrid setup
Step 5: Launch and Test
Start OpenClaw and verify it connects to your local Ollama instance. Run a test automation to confirm the agent responds using the local model.
- Start OpenClaw: npx openclaw start (requires Node.js 22+)
- Check logs for 'Connected to Ollama at localhost:11434'
- Run a test prompt to verify response generation
- Monitor performance with ollama ps to see active models
How Does Ollama Compare to Cloud APIs?
How local Ollama models compare to cloud APIs across key metrics. Ollama wins on cost, privacy, and availability. Cloud APIs win on raw throughput and peak quality.
| Metric | Ollama (Local) | Cloud API |
|---|---|---|
| Cost per 1M tokens | Free ($0) | $3-$15 |
| Response latency (first token) | 100-500ms | 200-2000ms |
| Throughput (tokens/sec) | 30-90 (hardware dependent) | 50-150 (provider dependent) |
| Reasoning quality (7-8B) | Good for routine tasks | Excellent across all tasks |
| Reasoning quality (70B) | Near-cloud quality | Best available |
| Privacy | Full local — nothing leaves your machine | Data sent to third-party servers |
| Rate limits | None — limited only by hardware | Varies (60-10,000 RPM) |
| Offline availability | Works fully offline | Requires internet |
Which Models Are Best for Different Tasks?
Choose the right model for your specific automation needs. Using the wrong model wastes resources or delivers poor results.
General Automation
Qwen 2.5 7B
Best all-around balance of speed, quality, and resource usage. Handles task planning, email drafting, data extraction, and workflow orchestration with reliable results.
Code Generation & Debugging
Qwen 2.5 Coder 14B
Purpose-built for code tasks. Outperforms older CodeLlama and DeepSeek Coder on generation, refactoring, and debugging benchmarks. Best local coding model available.
Data Analysis & Reports
Qwen 2.5 7B
Strong instruction-following with consistent structured output. Generates clean JSON, CSV, and markdown reports. Reliable for recurring data pipeline automations.
Resource-Constrained Hardware
Gemma 3 4B
Runs on just 4GB RAM with surprisingly capable output. Google's best small model for Raspberry Pi deployments, older laptops, or when you need maximum speed.
Complex Reasoning & Analysis
Llama 3.3 70B or DeepSeek R1 14B
Llama 3.3 70B approaches cloud quality but needs 48GB+ RAM. DeepSeek R1 14B offers strong reasoning at just 16GB RAM with chain-of-thought capabilities.
Hybrid (Cost + Quality)
Qwen 2.5 7B + Cloud Fallback
Use Ollama for 80% of routine tasks (free), and automatically fall back to Claude or GPT-4 for complex reasoning. OpenClaw Gateway handles the routing automatically.
Gateway Fallback Chain: The Best of Both Worlds
OpenClaw's Gateway handles intelligent routing between providers. Configure Ollama as your primary (free) provider and a cloud API as your fallback for complex tasks.
- Primary provider: Ollama with Qwen 2.5 7B — handles 80% of tasks for free
- Fallback provider: Claude or GPT-4 — activates only for complex reasoning
- Gateway routes automatically based on task complexity and model capability
- Total cost reduction: 70-90% compared to cloud-only setups
- Configure in openclaw.json under the gateway.providers array
- Set timeout and retry logic to handle Ollama cold starts gracefully
- Monitor usage with OpenClaw's built-in token tracking to verify savings
- Fallback chains support unlimited providers — add as many as needed
Frequently Asked Questions
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