Quickstart

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.

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.

Option B: Using Docker (Recommended for Scalable, In-Production Systems)

This is the recommended way for production deployments:

Once Docker is installed, create a new PySpur project with:

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

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:
```
docker compose up -d
```

Using Local Models with Ollama

Start Ollama service with:

OLLAMA_HOST="0.0.0.0" ollama serve

Update your .env file with:

OLLAMA_BASE_URL=http://host.docker.internal:11434

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

Get Started

Chatbots

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API Reference

Setup Options

Option A: Using `pyspur` Python Package

Option B: Using Docker (Recommended for Scalable, In-Production Systems)

Using Local Models with Ollama

Next Steps

Need Help?

Join Our Discord

Talk to Creators

Get Started

Chatbots

Tools

RAG

Evals

API Reference

​Setup Options

​Option A: Using pyspur Python Package

​Option B: Using Docker (Recommended for Scalable, In-Production Systems)

​Using Local Models with Ollama

​Next Steps

​Need Help?

Join Our Discord

Talk to Creators

Setup Options

Option A: Using `pyspur` Python Package

Option B: Using Docker (Recommended for Scalable, In-Production Systems)

Using Local Models with Ollama

Next Steps

Need Help?