ZeroClaw with Ollama: The Fastest OpenClaw Fork Setup Guide

Written by Matteo Giardino, CTO.

What is ZeroClaw?

If you have been building autonomous AI agents in 2026, you know that performance matters. I have spent the last few months working extensively with OpenClaw. While incredibly versatile, the standard Python version of OpenClaw can become resource-heavy when scaling multi-agent setups. Enter ZeroClaw-a high-performance, Rust-based fork of the framework designed to minimize overhead. Setting up ZeroClaw with Ollama allows you to run these agents entirely locally, securing your data while significantly cutting down on latency.

ZeroClaw retains compatibility with most core OpenClaw concepts but rewrites the execution engine in Rust. This results in significantly lower RAM usage, faster startup times, and reduced token processing latency. I strongly recommend reading my guide on how to configure OpenClaw with Ollama for a baseline understanding, as many concepts map directly to this new fork. Another helpful resource is our breakdown of OpenClaw telemetry and analytics, which will show you exactly how to measure these performance gains.

Installing ZeroClaw on Ubuntu or macOS

Getting started with ZeroClaw is straightforward. Since it is built in Rust, you can install it via cargo or download a pre-compiled binary. I prefer the binary approach for quick deployments on macOS and Linux servers.

To install using Cargo, run:

cargo install zeroclaw-cli

Alternatively, if you want the latest binary release for macOS or Linux:

curl -sL https://zeroclaw.dev/install.sh | sh

Once installed, verify the setup by checking the version. You should see the latest 2026 release version printed to your console:

zeroclaw --version

Connecting ZeroClaw to Ollama

The true power of this framework emerges when paired with a local model provider. Running ZeroClaw with Ollama means you avoid API costs entirely. We will configure it to use a local Qwen model. If you are running on older hardware, you might want to check my guide on running Qwen 3.5 on CPU.

First, ensure Ollama is running and you have pulled your desired model:

ollama run qwen:7b

Next, create a config.toml file for your ZeroClaw project. Unlike standard OpenClaw, ZeroClaw uses TOML for environment and agent configuration. This makes it slightly stricter but much faster to parse at runtime.

[provider]
name = "ollama"
endpoint = "http://localhost:11434"
model = "qwen:7b"

[agent]
name = "LocalAssistant"
system_prompt = "You are a helpful, fast AI assistant running locally."

Start your agent by pointing it to the config:

zeroclaw run --config config.toml

Performance Comparison: OpenClaw vs ZeroClaw

To understand why developers are migrating in 2026, I ran a direct comparison. I tested both the Python-based OpenClaw and the Rust-based ZeroClaw running the identical agent workflow with Ollama on a Mac Mini M4.

Metric	OpenClaw (Python)	ZeroClaw (Rust)
Base RAM Usage	~140 MB	~18 MB
Startup Time	1.2s	0.08s
Throughput (Tokens/sec)	42 t/s	45 t/s

While the token generation speed is bottlenecked by the model itself, the massive reduction in RAM usage and startup time means you can spawn dozens of micro-agents in ZeroClaw without crashing your system. The memory footprint drops from 140 MB down to just 18 MB per instance.

Why I Prefer ZeroClaw for Production

If you are running a single, long-lived agent, the performance gains might not be noticeable. However, if you are building dynamic, multi-agent systems where agents are constantly spawned and destroyed, this Rust fork is a massive improvement.

Want to learn more about Local AI?

Discover how to orchestrate multi-agent workflows locally.

Read our OpenClaw Guide

Keep in mind that ZeroClaw is still in active development. While it supports basic MCP integrations, some advanced Python plugins from the OpenClaw ecosystem may not be fully supported yet. I suggest testing it in a staging environment first.