OpenClaw vs Ollama
They're not competitors, they're different layers. Ollama runs the model; OpenClaw is the agent that uses it. Here's what each does, and why most self-hosted setups run both together.
OpenClaw vs Ollama at a glance
Different layers
Ollama is the model runtime; OpenClaw is the agent framework that orchestrates it — not rivals.
Choose Ollama
To download, serve, and manage local LLMs over a simple HTTP API — great for private, offline inference.
They connect
OpenClaw uses Ollama as one of its model backends — you point OpenClaw at your local Ollama host.
Choose OpenClaw
For a persistent agent: messaging channels, files, tools, browser automation, and workflows.
Best setup
Ollama + OpenClaw together — Ollama as the model layer, OpenClaw as the agent layer.
Sizing
OpenClaw's process is light; if you run a local model too, Ollama's RAM/VRAM needs dominate the server.
OpenClaw vs Ollama: full comparison
The two sit at different levels of the stack. This table shows what each one is and does — based on the official Ollama and OpenClaw documentation.
| Capability | Ollama | OpenClaw |
|---|---|---|
| Project identity | Open-source runtime for running and managing LLMs locally and via hosted cloud models. | Personal AI assistant and self-hosted multi-channel gateway platform. |
| Primary role | LLM inference layer: downloads, runs, and serves models via a local HTTP API and CLI. | Agent runtime: orchestrates planning, memory, tool use, and execution across channels. |
| APIs / endpoints | Native /api/chat, /api/generate, /api/embeddings; OpenAI-compatible /v1; Anthropic Messages compat (recent). |
Gateway API and CLI for channels, sessions, tools, models, browser, cron, and device nodes. |
| Model management | Pull, run, import, version, and customise open-weight models (Modelfiles). | Routes among providers (Ollama, OpenAI, Anthropic, and more); does not host weights itself. |
| Messaging | Not a messaging platform; used by other apps as a backend. | Broad ecosystem: WhatsApp, Telegram, Slack, Discord, Signal, iMessage, Teams, Matrix, and more. |
| Browser automation | Not a browser-automation platform. | Documented: dedicated Chrome/Chromium profile, tabs, actions, screenshots, PDFs, CDP. |
| Shell / tools / cron | Not an agent orchestrator; clients call its API themselves. | Command execution, tools, skills, cron, webhooks, and event-driven workflows. |
| RAM / VRAM Rule of thumb | Model-dependent: ~0.6 GB VRAM per billion params (4-bit), plus context overhead. | Gateway process is light; total RAM depends on the full stack and any local model. |
| Security posture | Local-first — inference stays on your machine by default. | Gateway, pairing, and auth controls; remote exposure still needs hardening. |
What OpenClaw is best at
OpenClaw turns a model into a persistent, tool-using agent — especially when it must touch websites, many communication systems, and external tools:
- An autonomous agent that reaches you via messaging apps and runs tasks in the background.
- Browser automation with a dedicated, controlled browser profile.
- Complex multi-step workflows, concurrent sessions, and agent routing.
- Broad messaging and collaboration integrations.
- Skills, plugins, custom tools, and companion-device, voice, screen, and Canvas workflows.
What Ollama is best at
Ollama's advantage is simplicity and privacy — one command pulls and serves a model locally, with no cloud keys needed for local inference. It fits:
- Running open-weight LLMs locally for chat, coding, RAG, or tool use.
- Downloading, importing, versioning, and customising models with Modelfiles.
- Serving models over a local HTTP API, including an OpenAI-compatible /v1 endpoint.
- GPU acceleration (NVIDIA, AMD, Apple Metal) and quantisation for efficient inference.
- Acting as the model backend for another app that handles agents or workflows.
OpenClaw vs Ollama: VPS requirements
The sizing question is really about the model. OpenClaw’s gateway is light on its own; Ollama’s footprint is driven by the model you load. Run both on one server and Ollama’s needs dominate.
| Setup | Plan for (VPS) | Notes |
|---|---|---|
| OpenClaw + a cloud API | ~2–4 GB RAM | Agent only; the model runs on OpenAI/Anthropic. Lightest option. |
| Ollama, local 7B model | 8 GB+ RAM, or a GPU VPS | ~0.6 GB VRAM per billion params (4-bit) + context; CPU works, GPU is faster. |
| OpenClaw + Ollama Full local stack | 8–16 GB RAM, GPU for speed | Private agent, no API bills. Ollama’s model needs set the tier. |
Read these figures carefully: the ~0.6 GB-per-billion-parameter number is a rough rule of thumb for 4-bit quantised models and excludes context and OS overhead. OpenClaw has no official RAM minimum; the 2–4 GB and 8 GB+ figures are provisional third-party estimates for provisioning, not official requirements. Size for the largest local model you intend to run, and benchmark your actual workload.
Frequently asked questions
Is OpenClaw the same as Ollama?
Can OpenClaw use Ollama?
Yes. OpenClaw has a documented Ollama provider. You point it at your local Ollama host (default http://127.0.0.1:11434), set an auth value like OLLAMA_API_KEY="ollama-local", and OpenClaw auto-discovers your Ollama models as ollama/<model>.
Do I need both OpenClaw and Ollama?
Only if you want a self-hosted agent running a local model. Use Ollama alone for private inference with your own UI. Use OpenClaw alone with a cloud API if you don’t need local models. Run both when you want a fully local, tool-using agent with no per-token API costs.
How much RAM do I need to run OpenClaw with Ollama?
OpenClaw’s own process is light, so the model decides the size. A local 7B model in Ollama needs roughly 8 GB of RAM or a GPU; running both together, plan for 8–16 GB and a GPU if you want speed. Sizing figures are workload-dependent — benchmark your setup.
Which is more private, OpenClaw or Ollama?
Ollama is private by design for local inference — nothing leaves your machine unless you use its cloud models. OpenClaw’s privacy depends on which provider it routes to: point it at a local Ollama model and inference stays local; point it at a cloud API and prompts go to that provider.
Is Ollama an agent framework?
No. Ollama serves models but doesn’t orchestrate tools, messaging, or workflows on its own. For autonomous agent behaviour you pair it with an agent runtime such as OpenClaw, which sends requests to Ollama and acts on the responses.
Capabilities, endpoints, and RAM figures in this comparison are drawn from the official Ollama and OpenClaw documentation and may change between releases. Verify the current documentation and any security advisories before deploying either component.
