hero.mp4
- 🖐️ Drag-and-Drop: Build, Test and Iterate in Seconds.
- 🔄 Loops: Iterative Tool Calling with Memory.
- 📤 File Upload: Upload files or paste URLs to process documents.
- 📋 Structured Outputs: UI editor for JSON Schemas.
- 🗃️ RAG: Parse, Chunk, Embed, and Upsert Data into a Vector DB.
- 🖼️ Multimodal: Support for Video, Images, Audio, Texts, Code.
- 🧰 Tools: Slack, Firecrawl.dev, Google Sheets, GitHub, and more.
- 🧪 Evals: Evaluate Agents on Real-World Datasets.
- 🚀 One-Click Deploy: Publish as an API and integrate wherever you want.
- 🐍 Python-Based: Add new nodes by creating a single Python file.
- 🎛️ Any-Vendor-Support: >100 LLM providers, embedders, and vector DBs.
This is the quickest way to get started. Python 3.12 or higher is required.
-
Install PySpur:
pip install pyspur
-
Initialize a new project:
pyspur init my-project cd my-project
This will create a new directory with a
.env
file. -
Start the server:
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. -
[Optional] 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
.env
file in your project directory - It is recommended to configure a postgres database in .env for more reliability - Restart the app withpyspur serve
. Add--sqlite
if you are not using postgres
This is the recommended way for production deployments:
-
Install Docker: First, install Docker by following the official installation guide for your operating system:
-
Create a PySpur Project: 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
- Start a new PySpur project in a new directory called
-
Access PySpur: Go to
http://localhost:6080
in your browser. -
[Optional] 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
.env
file in your project directory - Restart the services with:sh docker compose up -d
That's it! Click on "New Spur" to create a workflow, or start with one of the stock templates.
visualization.mp4
PDFs, Videos, Audio, Images, ...
multimodal.mp4

RAG_1.mp4
RAG_2.mp4
blocks.mp4
evals.mp4
optimization.mp4
For development, follow these steps:
-
Clone the repository:
git clone https://github.com/PySpur-com/pyspur.git cd pyspur
-
Launch using docker-compose.dev.yml:
docker compose -f docker-compose.dev.yml up --build -d
This will start a local instance of PySpur with hot-reloading enabled for development.
-
Customize your setup: Edit the
.env
file to configure your environment. By default, PySpur uses a local PostgreSQL database. To use an external database, modify thePOSTGRES_*
variables in.env
.
You can support us in our work by leaving a star! Thank you!
Your feedback will be massively appreciated. Please tell us which features on that list you like to see next or request entirely new ones.