--- title: 'Quickstart' description: 'Install PySpur in under 2 minutes' --- ## Setup Options Choose the installation method that best suits your needs: ### Option A: Using `pyspur` Python Package This is the quickest way to get started. Python 3.12 or higher is required. ```sh pip install pyspur ``` ```sh pyspur init my-project cd my-project ``` This will create a new directory with a `.env` file. ```sh pyspur serve --sqlite ``` By default, this will start PySpur app at `http://localhost:6080` using a sqlite database. We recommend you configure a postgres instance URL in the `.env` file to get a more stable experience. You can customize your PySpur deployment in two ways: a. **Through the app** (Recommended): - Navigate to the API Keys tab in the app - Add your API keys for various providers (OpenAI, Anthropic, etc.) - Changes take effect immediately b. **Manual Configuration**: - Edit the `.env` file in your project directory - It is recommended to configure a postgres database in .env for more reliability - Restart the app with `pyspur serve`. Add `--sqlite` if you are not using postgres ### Option B: Using Docker (Recommended for Scalable, In-Production Systems) This is the recommended way for production deployments: First, install Docker by following the official installation guide for your operating system: - [Docker for Linux](https://docs.docker.com/engine/install/) - [Docker Desktop for Mac](https://docs.docker.com/desktop/install/mac-install/) Once Docker is installed, create a new PySpur project with: ```sh curl -fsSL https://raw.githubusercontent.com/PySpur-com/pyspur/main/start_pyspur_docker.sh | bash -s pyspur-project ``` This will: - Start a new PySpur project in a new directory called `pyspur-project` - Set up the necessary configuration files - Start PySpur app automatically backed by a local postgres docker instance Go to `http://localhost:6080` in your browser. You can customize your PySpur deployment in two ways: a. **Through the app** (Recommended): - Navigate to the API Keys tab in the app - Add your API keys for various providers (OpenAI, Anthropic, etc.) - Changes take effect immediately b. **Manual Configuration**: - Edit the `.env` file in your project directory - Restart the services with: ```sh docker compose up -d ``` ### Using Local Models with Ollama 1. Start Ollama service with: ```sh OLLAMA_HOST="0.0.0.0" ollama serve ``` 2. Update your `.env` file with: ```sh OLLAMA_BASE_URL=http://host.docker.internal:11434 ``` 3. Download models using: `ollama pull ` 4. Select Ollama models from the sidebar for LLM nodes Note: PySpur only works with models that support structured-output and json mode. Most newer models should be good, but please confirm this from Ollama documentation for the model you wish to use. ## Next Steps After installation, you can: - 🪄 **Create New Workflow** Click "New Spur" to create a workflow from scratch - 📋 **Use Templates** Start with one of our pre-built templates - 💾 **Import Spur JSONs** Import spurs shared by other users - 🌐 **Deploy as API** Single click using the "Deploy" button in the top bar ## Need Help? Connect with the community and get help Schedule a call with the PySpur team