How to Use OpenClaw with Ollama: Build a Fully Local AI
Your AI assistant has been quietly sending your data to cloud servers. Every prompt you type, every document you analyze passes through a third-party API and lives on someone else's infrastructure. For personal projects, that trade-off may be acceptable. For sensitive business data, contracts, financial records, or regulated information, it is not.
OpenClaw with Ollama changes that. OpenClaw is a free, MIT-licensed personal AI assistant. Ollama serves as the local model provider — the source of reasoning power — without routing a single byte of your data to an external server. The result is a fully local AI stack: your hardware, your models, your data.
What you will build: A fully local OpenClaw agent powered by Ollama. No cloud APIs for the model layer. Confirmed working with Llama 4 and Kimi 2.5. Near-zero ongoing cost beyond electricity.
At a Glance
Why Run AI Locally?
The case for local AI comes down to three factors: privacy, cost, and control.
Privacy and data sovereignty. When you run OpenClaw with Ollama, all data remains on your hardware. Zero third-party access. No cloud dependency for storage. That matters most for regulated industries (healthcare, finance, legal) and for anyone building tools that handle credentials, personal records, or proprietary business logic.
Cost at scale. OpenClaw software is free under the MIT License. Cloud model API costs range from $5–15 per month for light usage to $50–150 per month for heavy workloads. When you route requests through Ollama instead of a cloud API, the ongoing cost drops to near-zero — electricity for your hardware only.
Control over the model. Local deployments let you choose which open-weight model handles your tasks and swap models without changing agent configuration. OpenClaw's model-agnostic architecture supports Llama 4, Kimi 2.5, and the broader catalog of models Ollama supports.
The Lobster Tank Framework
OpenClaw's conceptual model for local agents uses three elements:
- The tank — your physical or virtual environment: personal machine, home server, or Raspberry Pi.
- The food — reasoning power, supplied via a cloud API key or, in this guide, via Ollama locally. No cloud key needed for the model layer.
- The rules — codified in SOUL.md, a plain-text file that defines the agent's identity, tone, and behavioral limits. No coding required to modify it.
Connecting Ollama swaps the food source from a cloud API to a local model server. The tank and the rules stay the same. That single swap moves your entire stack off the internet.
Prerequisites Checklist
Check everything off before starting. Missing a prerequisite at step 4 costs more time than confirming it now.
openclaw --version. Ensure you are on v2026.4.2 or later (the latest stable release as of May 2026). Earlier versions contain critical CVEs including CVE-2026-25253 (CVSS 8.8, RCE) and CVE-2026-32922 (CVSS 9.9, privilege escalation).Already set up OpenClaw? Skip to Step 1: Install Ollama. If not, read the OpenClaw setup guide first — this guide builds on a working OpenClaw installation.
Setup Steps
Install Ollama and Pull a Model
Ollama is the model server that supplies reasoning power to your OpenClaw agents. Follow the official installation instructions at ollama.com for your operating system. Ollama runs as a local service and exposes an API endpoint your OpenClaw configuration will point to.
After installing, pull a model. Llama 4 and Kimi 2.5 are confirmed in sources as running via Ollama with OpenClaw. The DataCamp tutorial "Using OpenClaw with Ollama: Building a Local Data Analyst" by Derrick Mwiti uses this exact combination. Check the Ollama model library for current pull commands — they change as new model versions release.
Hardware note: Official sources do not publish Ollama-specific hardware requirements. The NemoClaw figures (4 vCPU / 8 GB RAM minimum, 16 GB recommended) are the only confirmed minimums. Larger models like Llama 4 will need more RAM than the bare minimum. Confirm you have headroom before pulling a large model.
Verify Ollama is running before proceeding. Most Ollama installations expose a local API at http://localhost:11434 by default. Confirm the service is active and the model you pulled is listed in the model inventory. If the service is not running, your OpenClaw configuration will fail silently.
Set Up Your OpenClaw Workspace
If OpenClaw is not yet installed, follow the official OpenClaw setup guide. The core installation path uses Node.js and the OpenClaw CLI to initialize a workspace directory.
Your workspace contains several key files:
- SOUL.md — agent identity, tone, behavioral boundaries (plain text, no code)
- IDENTITY.md — agent name and persona
- USER.md — user context and preferences
- MEMORY.md — persistent memory configuration
- TOOLS.md — what the agent is allowed to do
- HEARTBEAT.md — scheduled task definitions and cadences
Default bind warning. Current OpenClaw versions (v2026.1.29+) default the Gateway to 127.0.0.1:18789 (loopback only). Earlier versions defaulted to 0.0.0.0:18789 (all interfaces), which exposed the API to all network interfaces and led to 135,000+ instances being publicly exposed. Verify your bind setting is loopback before going live. If you need remote access, use an SSH tunnel or Tailscale rather than opening a public port. Read the OpenClaw security guide before going live.
API keys are stored locally in ~/.openclaw/ when using cloud providers. With Ollama, there is no API key for the model layer — the local endpoint handles this internally.
Connect Ollama as the Model Provider
OpenClaw's model-agnostic architecture means you point it at a model provider rather than hard-coding a specific API. To use Ollama:
- Open your OpenClaw workspace configuration (check the official docs for the current config file name and format).
- Set the model provider endpoint to your Ollama local API address — typically
http://localhost:11434. - Specify the model name you pulled (for example, the Llama 4 or Kimi 2.5 model identifier).
- Remove or leave empty any cloud API key fields — no external key is required for the Ollama model layer.
Verify the connection by sending a test message to your agent via your connected messaging platform. If the agent responds, Ollama is supplying the reasoning power. If not, check that Ollama is running, the model is loaded, and the endpoint URL matches exactly.
Configure SOUL.md for Local Use
SOUL.md is the plain-text file that defines your agent's identity, tone, and behavioral rules. No coding required. It reads like instructions you would give a human assistant.
For a local data analyst agent, your SOUL.md should cover four areas:
- Role definition. Specify that this is a data analysis assistant, what file types it should expect, and what output formats it should produce (tables, summaries, bullet lists).
- Behavioral limits. Specify what the agent must not do — for example, do not send results to external services, do not retain raw data longer than the session.
- Tone and verbosity. For data analysis, terse structured output is usually preferable to conversational prose.
- Allowed tools. If you are using ClawHub skills for data tasks, name them in SOUL.md and in TOOLS.md. Review every skill from ClawHub before installing.
SOUL.md changes take effect on agent restart. No recompilation — edit the file, restart the agent, and the new behavior is live.
Run Your First Local AI Task
With Ollama connected and SOUL.md configured, send a test task through your messaging platform:
- Open Telegram (or whichever platform you connected during setup).
- Send a message describing the task — for example: "Summarize the attached file and identify the top 5 entries by value."
- The agent receives the message, routes the request to Ollama locally, and returns a structured response.
- No data leaves your machine at any point in this flow.
What to check if the agent does not respond:
- Confirm Ollama is running and the model is loaded.
- Confirm your OpenClaw Gateway is running and the messaging platform webhook is active.
- Review OpenClaw logs for connection errors — a port mismatch or model identifier typo is the most common cause.
Build a Local Data Analyst Workflow
The DataCamp tutorial "Using OpenClaw with Ollama: Building a Local Data Analyst" by Derrick Mwiti demonstrates the full capability of this stack. The use case: orchestrate multi-step workflows that analyze datasets, generate visual reports, and surface insights without sending data to the cloud.
A local data analyst agent built on OpenClaw and Ollama can:
- Accept a CSV or spreadsheet file via your messaging platform
- Run multi-step analysis workflows: cleaning, aggregation, trend detection
- Generate text summaries and structured output
- Chain these steps across an agentic workflow without human intervention at each step
This is the primary advantage of OpenClaw's architecture over simpler chatbot setups: the agent orchestrates a workflow, not just a single response. SOUL.md governs what the agent is allowed to do at each step.
For regulated data: Because all processing stays local, this stack is viable for datasets you cannot send to cloud APIs under data governance policies. Confirm with your legal or compliance team what "local processing" means in your jurisdiction.
Deployment Options
OpenClaw runs in several environments. Choose the path that fits your infrastructure and data sovereignty requirements.
Limitations to Know
Troubleshooting
Check whether Ollama is running and the model is loaded. Then verify the endpoint URL in your OpenClaw model provider config matches what Ollama exposes. A mismatch in port or model identifier is the most common cause. Confirm the endpoint is http://localhost:11434 (or wherever your Ollama service runs) and that the model name matches exactly what you pulled.
Larger models require more RAM. If your machine has 8 GB total, a 7B parameter model may consume most of it, leaving little headroom for OpenClaw and the operating system. Try a smaller model first to confirm the integration works before scaling up. If you need larger models, increase RAM first.
Some OpenClaw configurations expect an API key field even when using local models. Check whether the config file has a required key field and whether it accepts a placeholder value. Consult the official OpenClaw docs for the current config format — this has changed across versions.
ClawHub hosts 13,700+ community-built skills, and not all have been reviewed. If a skill installed for the data analyst use case behaves unexpectedly, disable it and review its source before reinstalling. Use pinned versions when available. Do not install skills that require curl | bash-style installation from unknown publishers.
Frequently Asked Questions
Yes. Local Ollama is supported in the standard NemoClaw onboarding flow. Local vLLM is experimental in NemoClaw and requires macOS with OpenShell host-routing; do not use it in production. If you are using NemoClaw, follow the standard Ollama path in the onboarding documentation. Source: NVIDIA NemoClaw GitHub.
Llama 4 and Kimi 2.5 are explicitly confirmed in research sources as running via Ollama with OpenClaw. Ollama supports a broad catalog of open-weight models beyond these two — check the Ollama model library for current availability and hardware requirements.
When you run OpenClaw with Ollama and no cloud API keys, no data reaches Anthropic, OpenAI, or any third party. All processing stays on your hardware. The only external traffic is your messaging platform connection (Telegram, WhatsApp, or LINE), which carries messages between your phone and your local server. The model reasoning itself is fully local.
Yes. OpenClaw Cloud at $59 per month is a managed hosting option that removes infra overhead. It connects to cloud model APIs rather than Ollama. If data sovereignty is your primary requirement, the local Ollama stack is the appropriate choice. If infra management is the bigger concern, OpenClaw Cloud is a viable alternative. Note: OpenClaw Cloud is a managed hosting fee, not a software license — the software itself remains free under the MIT License.
Video Resources
Curated video walkthroughs for this setup. Search YouTube for the titles below for current versions.
Sources: DataCamp Tutorial "Using OpenClaw with Ollama: Building a Local Data Analyst" by Derrick Mwiti (accessed 2026-05-05); NVIDIA NemoClaw GitHub Reference Stack (accessed 2026-05-05); OpenClaw Documentation, Deployment Options (accessed 2026-05-05). OpenClaw is an open-source project (MIT License). NVIDIA and NemoClaw are trademarks of NVIDIA Corporation. Ollama is a separate open-source project.