# Using Ollama > **Important**: Ollama integration is only available when self-hosting the AutoGPT platform. It cannot be used with the cloud-hosted version. Follow these steps to set up and run Ollama with the AutoGPT platform. ## Prerequisites 1. Make sure you have gone through and completed the [AutoGPT Setup](https://github.com/Significant-Gravitas/AutoGPT/blob/gitbook/platform/getting-started/README.md) steps, if not please do so before continuing with this guide. 2. Before starting, ensure you have [Ollama installed](https://ollama.com/download) on your machine. ## Setup Steps ### 1. Launch Ollama To properly set up Ollama for network access, choose one of these methods: **Method A: Using Ollama Desktop App (Recommended)** 1. Open the Ollama desktop application 2. Go to **Settings** and toggle **"Expose Ollama to the network"** 3. Click on the model name field in the "New Chat" window 4. Search for "llama3.2" (or your preferred model) 5. Click on it to start the download and load the model to be used ??? note "Method B: Using Docker (Alternative)" ```` If you prefer to run Ollama via Docker instead of the desktop app, you can use the official [Ollama Docker image](https://hub.docker.com/r/ollama/ollama): 1. **Start Ollama container** (choose based on your hardware): **CPU only:** ```bash docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama ``` **With NVIDIA GPU** (requires [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)): ```bash docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama ``` **With AMD GPU:** ```bash docker run -d --device /dev/kfd --device /dev/dri -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama:rocm ``` **Download your desired model:** ```bash docker exec -it ollama ollama run llama3.2 ``` !!! note The Docker method automatically exposes Ollama on `0.0.0.0:11434`, making it accessible to AutoGPT. More models can be found on the [Ollama library](https://ollama.com/library). ```` ??? warning "Method C: Using Ollama Via Command Line (Legacy)" ```` For users still using the traditional CLI approach or older Ollama installations: 1. **Set the host environment variable:** **Windows (Command Prompt):** ```cmd set OLLAMA_HOST=0.0.0.0:11434 ``` **Linux/macOS (Terminal):** ```bash export OLLAMA_HOST=0.0.0.0:11434 ``` 2. **Start the Ollama server:** ```bash ollama serve ``` 3. **Open a new terminal/command window** and download your desired model: ```bash ollama pull llama3.2 ``` !!! note This will download the [llama3.2](https://ollama.com/library/llama3.2) model. Keep the terminal with `ollama serve` running in the background throughout your session. ```` ### 2. Start the AutoGPT Platform Navigate to the autogpt\_platform directory and start all services: ```bash cd autogpt_platform docker compose up -d --build ``` This command starts both the backend and frontend services. Once running, visit to access the platform. After registering/logging in, navigate to the build page at . ### 3. Using Ollama with AutoGPT Now that both Ollama and the AutoGPT platform are running, we can use Ollama with AutoGPT: 1. Add an AI Text Generator block to your workspace (it can work with any AI LLM block but for this example will be using the AI Text Generator block): 2. **Configure the API Key field**: Enter any value (e.g., "dummy" or "not-needed") since Ollama doesn't require authentication. 3. In the "LLM Model" dropdown, select "llama3.2" (This is the model we downloaded earlier) > **Compatible Models**: The following Ollama models are available in AutoGPT by default: > > * `llama3.2` (Recommended for most use cases) > * `llama3` > * `llama3.1:405b` > * `dolphin-mistral:latest` > > **Note**: To use other models, follow the "Add Custom Models" step above. 4. **Set your local IP address** in the "Ollama Host" field: **To find your local IP address:** **Windows (Command Prompt):** ```cmd ipconfig ``` **Linux/macOS (Terminal):** ```bash ip addr show ``` or ```bash ifconfig ``` Look for your IPv4 address (e.g., `192.168.0.39`), then enter it with port `11434` in the "Ollama Host" field: ``` 192.168.0.39:11434 ``` > **Important**: Since AutoGPT runs in Docker containers, you must use your host machine's IP address instead of `localhost` or `127.0.0.1`. Docker containers cannot reach `localhost` on the host machine. 5. Add prompts to your AI block, save the graph, and run it: That's it! You've successfully setup the AutoGPT platform and made a LLM call to Ollama. ### Using Ollama on a Remote Server with AutoGPT For running Ollama on a remote server, simply make sure the Ollama server is running and is accessible from other devices on your network/remotely through the port 11434. **To find your local IP address of the system running Ollama:** **Windows (Command Prompt):** ```cmd ipconfig ``` **Linux/macOS (Terminal):** ```bash ip addr show ``` or ```bash ifconfig ``` Look for your IPv4 address (e.g., `192.168.0.39`). Then you can use the same steps above but you need to add the Ollama server's IP address to the "Ollama Host" field in the block settings like so: ``` 192.168.0.39:11434 ``` ## Add Custom Models (Advanced) If you want to use models other than the default ones, you'll need to add them to the model list. Follow these steps: 1. **Add the model to the LlmModel enum** in `autogpt_platform/backend/backend/blocks/llm.py`: Find the Ollama models section (around line 119) and add your model like the other Ollama models: ```python # Ollama models OLLAMA_LLAMA3_3 = "llama3.3" OLLAMA_LLAMA3_2 = "llama3.2" OLLAMA_YOUR_MODEL = "The-model-name-from-ollama" # Add your model here ``` 2. **Add model metadata** in the same file: Find the `MODEL_METADATA` dictionary (around line 181) and add your model with its metadata: ```python # In MODEL_METADATA dictionary, add: LlmModel.OLLAMA_YOUR_MODEL: ModelMetadata("ollama", 8192, None), ``` Where: * `"ollama"` = provider name * `8192` = max context window (adjust based on your model) * `None` = max output tokens (None means no specific limit) 3. **Add model cost configuration** in `autogpt_platform/backend/backend/data/block_cost_config.py`: Find the `MODEL_COST` dictionary (around line 54) and add your model: ```python # In MODEL_COST dictionary, add: LlmModel.OLLAMA_YOUR_MODEL: 1, ``` > **Note**: Setting cost to `1` is fine for local usage as cost tracking is disabled for self-hosted instances. 4. **Rebuild the backend**: ```bash docker compose up -d --build ``` 5. **Pull the model in Ollama**: ```bash ollama pull your-model-name ``` ## Troubleshooting If you encounter any issues, verify that: * Ollama is properly installed and running with `ollama serve` * Docker is running before starting the platform * If running Ollama outside Docker, ensure it's set to `0.0.0.0:11434` for network access ### Common Issues #### Connection Refused / Cannot Connect to Ollama * **Most common cause**: Using `localhost` or `127.0.0.1` in the Ollama Host field * **Solution**: Use your host machine's IP address (e.g., `192.168.0.39:11434`) * **Why**: AutoGPT runs in Docker containers and cannot reach `localhost` on the host * **Find your IP**: Use `ipconfig` (Windows) or `ifconfig` (Linux/macOS) * **Test Ollama is running**: `curl http://localhost:11434/api/tags` should work from your host machine #### Model Not Found * Pull the model manually: ```bash ollama pull llama3.2 ``` * If using a custom model, ensure it's added to the model list in `backend/server/model.py` #### Docker Issues * Ensure Docker daemon is running: ```bash docker ps ``` * Try rebuilding: ```bash docker compose up -d --build ``` #### API Key Errors * Remember that Ollama doesn't require authentication - any value works for the API key field #### Model Selection Issues * Look for models with "ollama" in their description in the dropdown * Only the models listed in the "Compatible Models" section are guaranteed to work