OpenClaw
What this article covers
- What OpenClaw is and what it aims to do
- How to install and run OpenClaw
- What a simple autonomous workflow looks like
- When OpenClaw is the right choice
- Common pitfalls for newcomers
Introduction
OpenClaw is an open framework for controlling autonomous AI agents. The idea is to think of agents not just as assistants, but as independent entities that pursue goals, make decisions, and execute actions. OpenClaw tries to make this control open and extensible.
OpenClaw at a glance
OpenClaw defines agents as systems with goals, perceptions, and actions. An agent observes its environment, plans the next step, and carries it out. The framework focuses less on conversation or role-playing than on decision-making and execution.
Installation
Since OpenClaw develops quickly, installing from the repository is recommended:
git clone https://github.com/openclaw-ai/openclaw.git
cd openclaw
pip install -e .If the package is now available on PyPI, you can also use:
pip install openclawTo run with local models via Ollama:
export OPENAI_BASE_URL="http://localhost:11434/v1"
export OPENAI_API_KEY="irrelevant-for-ollama"Getting started: A simple goal-based agent
from openclaw import Agent, Goal
agent = Agent(
goal=Goal("Gather current AI news from three sources"),
model_config={
"model": "llama3.1",
"base_url": "http://localhost:11434/v1",
"api_key": "ollama"
}
)
agent.run()This is a conceptual example. The exact API may vary depending on the version. But it shows the idea: a goal is defined and the agent works toward it.
When to use OpenClaw
| Use case | Benefit |
|---|---|
| Autonomous workflows | Agent pursues a goal across multiple steps |
| Research agents | Gathers and structures information |
| Experiments | Tests different agent control strategies |
| Research | Open architecture for custom ideas |
Common pitfalls
- Rapid development - The API may still change
- Check documentation - Current commands are in the repository and README
- Local models - Autonomous agents often need strong models and plenty of tokens
Further reading
FAQ - Frequently asked questions
What is OpenClaw?
OpenClaw is an open framework for controlling autonomous AI agents. It defines agents as systems with goals, perceptions, and actions, focusing on decision-making and execution rather than pure conversation.
Is OpenClaw free?
Yes, OpenClaw is open source and free. Costs only come from the hardware and models you use for inference.
Is OpenClaw suitable for production environments?
Currently more suited for experiments and research. If you need stable workflows, established frameworks like LangGraph or CrewAI are better choices.
Can I run OpenClaw locally?
Yes, via OpenAI-compatible local APIs like Ollama. The exact configuration depends on the release.
What programming language does OpenClaw require?
OpenClaw is written in Python. You need a recent Python version (3.10 or newer recommended) and pip for installation.
Which models work with OpenClaw?
Any model accessible through an OpenAI-compatible API. Locally, Llama 3.1, Qwen 2.5, or Mistral via Ollama work well. In the cloud, you can use OpenAI, Anthropic, or Together AI.
How do I install OpenClaw?
The easiest way is via pip with pip install openclaw. Alternatively, you can clone the repository and install with pip install -e . to use the latest development version.
How do I connect OpenClaw with Ollama?
Set the environment variable OPENAI_BASE_URL to http://localhost:11434/v1 and OPENAI_API_KEY to any value. Ollama provides an OpenAI-compatible endpoint that OpenClaw can call directly.
What’s the difference between OpenClaw and CrewAI?
CrewAI focuses on collaboration between multiple agents with roles. OpenClaw emphasizes individual autonomous agents that pursue a goal and make decisions independently.
What’s the difference between OpenClaw and LangGraph?
LangGraph models agents as graphs with nodes and edges. OpenClaw abstracts more through goals and actions. LangGraph is more mature and better documented; OpenClaw is more experimental.
Do I need a graphics card for OpenClaw?
Not necessarily. If you use local models via Ollama, a GPU significantly speeds up inference. You can also use OpenClaw with cloud APIs, which only requires a regular computer.
Can I run multiple agents simultaneously?
Yes, OpenClaw supports multiple agents. You can start them sequentially or in parallel. For complex multi-agent workflows, frameworks like AutoGen or CrewAI are often better suited.
How much RAM do I need for OpenClaw with local models?
For 7B models, at least 16 GB RAM, preferably 32 GB. Larger models like 70B require 64 GB or more. With cloud APIs, you only need memory for the framework itself.
Can I run OpenClaw in Docker?
Yes. You can write a Dockerfile that includes Python, OpenClaw, and optionally Ollama. This lets you run the agent in isolation and reproducibly.
Is there a graphical interface for OpenClaw?
OpenClaw itself is primarily a CLI and Python framework. For a user interface, you can connect Open WebUI or custom frontends that call the API.
How do I debug an OpenClaw agent?
Use Python logging, for example logging.basicConfig(level=logging.DEBUG). OpenClaw typically outputs which steps the agent plans and executes. If there are connection issues, a curl test against the model API helps.
Can OpenClaw access files?
Yes, you can give an agent tools that read or write files. The exact implementation depends on the OpenClaw version. Check the repository README.
What is a Goal in OpenClaw?
A Goal is the objective an agent should pursue. It can be text like “Gather news from three sources.” The agent independently plans the necessary steps to achieve the goal.
Can I use OpenClaw with MCP?
MCP (Model Context Protocol) is a standard for tools and context. Whether OpenClaw directly supports MCP depends on the release. Check the current documentation or issues in the repository.
Is OpenClaw safe for autonomous actions?
Autonomous agents can execute unexpected actions. Use sandbox environments, limit tools, and log all steps. Never deploy production workflows without monitoring.
Where do I find the documentation?
The primary source is the README in the OpenClaw GitHub repository. Since the framework develops rapidly, the README is often more current than external tutorials.
Can I use OpenClaw commercially?
That depends on the license. Check the license in the repository. Open source licenses like MIT or Apache 2.0 permit commercial use, but you should read the specific license.
How do I keep OpenClaw up to date?
If you installed via pip, pip install —upgrade openclaw is enough. For a repository installation, use git pull and run pip install -e . again.
Are there examples of OpenClaw workflows?
The repository often includes examples in the examples/ folder. They show how to define goals, attach tools, and start agents.
What do I do if my agent doesn’t respond?
First, check if the model API is reachable. Test with curl http://localhost:11434/v1/models for Ollama. Then check the OpenClaw log. Often the issue is an incorrect model name or a missing environment variable.