openclaw/docs/plugins/llama-cpp.md

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Run local GGUF text inference and memory embeddings in OpenClaw with llama.cpp	You want local text inference without an API key or model server You want memory search embeddings from a local GGUF model You are configuring memory.search.provider = "local" You need the OpenClaw plugin that owns the node-llama-cpp runtime	llama.cpp Provider	llama.cpp Provider

llama-cpp is the official external provider plugin for in-process local GGUF text inference and embeddings. It registers text provider llama-cpp, embedding provider local, and owns the node-llama-cpp native runtime.

Install it before using either local inference or local memory embeddings:

openclaw plugins install @openclaw/llama-cpp-provider

The main openclaw npm package does not include node-llama-cpp. Keeping the native dependency in this plugin prevents normal OpenClaw npm updates from deleting a manually installed runtime inside the OpenClaw package directory.

Local text inference

Choose Local model (llama.cpp) during interactive onboarding. OpenClaw asks before downloading the default model:

hf:bartowski/Qwen_Qwen3-4B-Instruct-2507-GGUF/Qwen_Qwen3-4B-Instruct-2507-Q4_K_M.gguf

The Qwen3 4B Instruct 2507 Q4_K_M file is about 2.5 GB. Budget roughly 3 GB of RAM for model weights, plus context and OpenClaw runtime overhead. The default context is automatically sized with an 8,192-token cap so it remains practical on 8 GB machines. Configure a larger context only when the machine has enough memory.

The onboarding discovery check is read-only. It offers llama.cpp automatically only when the default or configured GGUF file is already in the model cache; it never downloads during discovery. Ollama and LM Studio remain separate local service choices and keep their own discovery flows. Manually choosing llama.cpp is the path that prompts for the default model download.

The provider uses the GGUF model's embedded chat template and native node-llama-cpp function calling. Text streams token by token. Tool calls return to OpenClaw for execution rather than running inside node-llama-cpp.

Use another GGUF model

Add a model to models.providers.llama-cpp. Put a local path or full hf: file URI in params.modelPath:

{
  models: {
    mode: "merge",
    providers: {
      "llama-cpp": {
        baseUrl: "local://llama-cpp",
        api: "openai-completions",
        params: {
          modelCacheDir: "~/.node-llama-cpp/models",
        },
        models: [
          {
            id: "my-local-model",
            name: "My local GGUF",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 8192,
            maxTokens: 2048,
            params: {
              modelPath: "~/Models/my-model.Q4_K_M.gguf",
              contextSize: 8192,
            },
            compat: { supportsTools: true },
          },
        ],
      },
    },
  },
  agents: {
    defaults: {
      model: { primary: "llama-cpp/my-local-model" },
    },
  },
}

Inference never downloads a missing model implicitly. For a custom hf: URI, download the GGUF into modelCacheDir first. Discovery uses node-llama-cpp's own read-only cache resolver, including repository, branch, and split-file naming.

Memory embedding configuration

Set memory.search.provider to local:

{
  memory: {
    search: {
      provider: "local",
      local: {
        modelPath: "hf:ggml-org/embeddinggemma-300m-qat-q8_0-GGUF/embeddinggemma-300m-qat-Q8_0.gguf",
      },
    },
  },
}

local.modelPath defaults to the hf: URI shown above (embeddinggemma-300m-qat-Q8_0.gguf). Point it at a different hf: URI or a local .gguf file to use another model. local.modelCacheDir overrides where downloaded models are cached (default: ~/.node-llama-cpp/models), and local.contextSize accepts an integer or "auto".

When local.contextSize is numeric, the provider also gives that requirement to node-llama-cpp's automatic GPU-layer placement. This lets node-llama-cpp fit the model and embedding context together while retaining its memory-safety checks. With "auto", node-llama-cpp keeps its normal automatic placement.

Native runtime

Use Node 24 for the smoothest native install path. Source checkouts using pnpm may need to approve and rebuild the native dependency:

pnpm approve-builds
pnpm rebuild node-llama-cpp

Memory runtime diagnostics

Run openclaw memory status --deep after the provider has loaded to inspect the selected backend and build, device names, GPU offloaded layers, requested context size, and the last observed VRAM or unified-memory snapshot. The VRAM values include an observation timestamp because passive status reads do not reload the model or poll the device.

The same last-known facts can appear in openclaw doctor when the running Gateway has already used the local provider. A normal status or doctor command does not load a model just to collect diagnostics.

Troubleshooting

If node-llama-cpp is missing or fails to load, OpenClaw reports the failure with:

1. Install the plugin: openclaw plugins install @openclaw/llama-cpp-provider.
2. Use Node 24 for native installs/updates.
3. From a pnpm source checkout: pnpm approve-builds, then pnpm rebuild node-llama-cpp.

For local inference without an in-process native dependency, use the Ollama or LM Studio provider instead. For lower-friction local embeddings, set memory.search.provider to a remote embedding provider such as lmstudio, ollama, openai, or voyage instead.