Liberate your OpenClaw
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Liberate your OpenClaw 🦀
Published
March 27, 2026
Update on GitHub
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Clem 🤗
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ben burtenshaw
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Pedro Cuenca
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Jeff Boudier
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merve
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Niels Rogge
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Victor Mustar
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Mishig Davaadorj
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Hugging Face Inference Providers
Local Setup
Which path should you choose?
Anthropic is limiting access to Claude models in open agent platforms for Pro/Max subscribers. Don’t worry though, there are great open models on Hugging Face to keep your agents running! Most of the time, at a fraction of the cost.
If you've been cut off and your OpenClaw, Pi, or Open Code agents need resuscitation, you can move them to open models in two ways:
Use an open model served through Hugging Face Inference Providers.
Run a fully local open model on your own hardware.
The hosted route is the fastest way back to a capable agent. The local route is the right fit if you want privacy, zero API costs, and full control.
To do so, just tell your claude code, your cursor or your favorite agent:
help me move my OpenClaw agents to Hugging Face models
, and link this page.
Hugging Face Inference Providers
Hugging Face inference providers is an open platform that routes to providers of open source models. It’s the right choice if you want the best models or you don’t have the necessary hardware.
First, you’ll need to create a token
here
. Then you can add that token to
openclaw
like so:
openclaw onboard --auth-choice huggingface-api-key
Paste your Hugging Face token when prompted, and you’ll be asked to select a model.
We’d recommend
GLM-5
because of its excellent
Terminal Bench
scores, but there are thousands to chose from
here
.
You can update your Hugging Face model at any time entering its
repo_id
in the OpenClaw config:
{
agents: {
defaults: {
model: {
primary: "huggingface/zai-org/GLM-5:fastest"
}
}
}
}
Note: HF PRO subscribers get $2 free credits each month which applies to Inference Providers usage, learn more
here
.
Local Setup
Running models locally gives you full privacy, zero API costs, and the ability to experiment without rate limits.
Install Llama.cpp, a fully open source library for low resource inference.
#
on mac or linux
brew install llama.cpp
#
on windows
winget install llama.cpp
Start a local server with a built-in web UI:
llama-server -hf unsloth/Qwen3.5-35B-A3B-GGUF:UD-Q4_K_XL
Here, we’re using Qwen3.5-35B-A3B, which works great with 32GB of RAM. If you have different requirements, please check out the hardware compatibility
for the model you're interested in
.
There are thousands to choose from
.
If you load the GGUF in llama.cpp, use an OpenClaw config like this:
openclaw onboard --non-interactive \
--auth-choice custom-api-key \
--custom-base-url "http://127.0.0.1:8080/v1" \
--custom-model-id "unsloth-qwen3.5-35b-a3b-gguf" \
--custom-api-key "llama.cpp" \
--secret-input-mode plaintext \
--custom-compatibility openai
Verify the server is running and the model is loaded:
curl http://127.0.0.1:8080/v1/models
Which path should you choose?
Use Hugging Face Inference Providers if you want the quickest path back to a capable OpenClaw agent. Use
llama.cpp
if you want privacy, full local control, and no API bill.
Either way, you do not need a closed hosted model to get OpenClaw back on its feet!
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