Quickstart
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.
1. Install PySpur
1. Install PySpur
2. Initialize a new project
2. Initialize a new project
This will create a new directory with a .env file.
3. Start the server
3. Start the server
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.
4. Customize Your Deployment
4. Customize Your Deployment
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
.envfile in your project directory - It is recommended to configure a postgres database in .env for more reliability
- Restart the app with
pyspur serve. Add--sqliteif you are not using postgres
Option B: Using Docker (Recommended for Scalable, In-Production Systems)
This is the recommended way for production deployments:
1. Install Docker
1. Install Docker
First, install Docker by following the official installation guide for your operating system:
2. Create a PySpur Project
2. Create a PySpur Project
Once Docker is installed, create a new PySpur project with:
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
3. Access PySpur
3. Access PySpur
Go to http://localhost:6080 in your browser.
4. Customize Your Deployment
4. Customize Your Deployment
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
.envfile in your project directory - Restart the services with:
Using Local Models with Ollama
Configure Ollama
Configure Ollama
- Start Ollama service with:
- Update your
.envfile with:
- Download models using:
ollama pull <model-name> - 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