# Local Agents with llama.cpp

You can run a coding agent entirely on your own hardware. Several open-source agents can connect to a local [llama.cpp](https://github.com/ggerganov/llama.cpp) server to give you an experience similar to Claude Code or Codex — but everything runs on your machine.

## Getting Started

### 1. Set Your Local Hardware

Set your local hardware so it can show you which models are compatible with your setup.

Go to [huggingface.co/settings/local-apps](https://huggingface.co/settings/local-apps) and configure your local hardware profile. Select `llama.cpp` in the Local Apps section as this will be the engine you'll use.

### 2. Find a Compatible Model

Browse for [Llama.cpp-compatible models](https://huggingface.co/models?apps=llama.cpp&sort=trending).

### 3. Launch the llama.cpp Server

On the model page, click the **"Use this model"** button and select `llama.cpp`. It will show you the exact commands for your setup. The first step is to start a llama.cpp server, e.g.

```bash
llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M
```

This downloads the model and starts an OpenAI-compatible API server on your machine. See the [llama.cpp guide](./gguf-llamacpp) for installation instructions.

### 4. Connect Your Agent

Pick one of the agents below and follow the setup instructions.

## Pi

[Pi](https://pi.dev) is the agent behind [OpenClaw](https://github.com/openclaw) and is now integrated directly into Hugging Face, giving you access to thousands of compatible models.

Install Pi:

```bash
npm install -g @mariozechner/pi-coding-agent
```

Then add your local model to Pi's configuration file at `~/.pi/agent/models.json`:

```json
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ggml-org-gemma-4-26b-4b-gguf"
        }
      ]
    }
  }
}
```

Start Pi in your project directory:

```bash
pi
```

Pi connects to your local llama.cpp server and gives you an interactive agent session.

![Demo](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/pi-llama-cpp-demo.gif)

### Enabling vision support

For vision-capable models, add `"input": ["text", "image"]` to the model entry in `~/.pi/agent/models.json`:

```json
"models": [
  {
    "id": "unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_XL",
    "input": ["text", "image"]
  }
]
```

Browse [vision-language models compatible with Pi](https://huggingface.co/models?pipeline_tag=image-text-to-text&apps=pi).

## OpenClaw

[OpenClaw](https://github.com/openclaw) works locally with llama.cpp. You can set your model via the onboard command:

```bash
openclaw onboard --non-interactive \
  --auth-choice custom-api-key \
  --custom-base-url "http://127.0.0.1:8080/v1" \
  --custom-model-id "ggml-org-gemma-4-26b-a4b-gguf" \
  --custom-api-key "llama.cpp" \
  --secret-input-mode plaintext \
  --custom-compatibility openai \
  --accept-risk
```

You can also run `openclaw onboard` interactively, select `custom-compatibility` with `openai`, and pass the same configuration.

## Hermes

[Hermes](https://hermes-agent.nousresearch.com/) works locally with llama.cpp. Define a default config as:

```yaml
model:
  provider: custom
  default: ggml-org/gemma-4-26B-A4B-it-GGUF:Q4_K_M
  base_url: http://127.0.0.1:8080/v1
  api_key: llama.cpp

custom_providers:
  - name: Local (127.0.0.1:8080)
    base_url: http://127.0.0.1:8080/v1
    api_key: llama.cpp
    model: ggml-org/gemma-4-26B-A4B-it-GGUF:Q4_K_M
```

## OpenCode

[OpenCode](https://opencode.ai) works locally with llama.cpp. Define a `~/.config/opencode/opencode.json`:

```json
{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "llama.cpp": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "llama-server (local)",
      "options": {
        "baseURL": "http://127.0.0.1:8080/v1"
      },
      "models": {
        "gemma-4-26b-4b-it": {
          "name": "Gemma 4 (local)",
          "limit": {
            "context": 128000,
            "output": 8192
          }
        }
      }
    }
  }
}
```

## How It Works

The setup has two components running locally:

1. **llama.cpp server** — Serves the model as an OpenAI-compatible API on `localhost`.
2. **Your agent** — The agent process that sends prompts to the local server, reasons about tasks, and executes actions.

```
┌─────────┐     API calls     ┌──────────────────┐
│  Agent  │ ───────────────▶  │  llama.cpp server │
│         │ ◀───────────────  │  (local model)    │
└─────────┘    responses      └──────────────────┘
     │
     ▼
  Your files,
  terminal, etc.
```

## Alternative: llama-agent

[llama-agent](https://github.com/gary149/llama-agent) takes a different approach — it builds the agent loop directly into [llama.cpp](https://github.com/ggerganov/llama.cpp) as a single binary with zero external dependencies. No Node.js, no Python, just compile and run:

```bash
git clone https://github.com/gary149/llama-agent.git
cd llama-agent

# Build
cmake -B build
cmake --build build --target llama-agent

# Run (downloads the model automatically)
./build/bin/llama-agent -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M
```

Because tool calls happen in-process rather than over HTTP, there is no network overhead between the model and the agent. It also supports subagents, MCP servers, and an HTTP API server mode.

## Next Steps

- [Use AI Models Locally](./local-apps) — Learn more about running models on your machine
- [llama.cpp Guide](./gguf-llamacpp) — Detailed llama.cpp installation and usage
- [Agents on the Hub](./agents-overview) — Connect agents to the Hugging Face ecosystem