PySpur - Graph-Based Editor for LLM Workflows

hero.mp4

✨ Core Benefits

Modular Building Blocks

blocks.mp4

Debug at Node Level:

visualization.mp4

Evaluate Final Performance

evals.mp4

Coming soon: Self-improvement

optimization.mp4

🕸️ Why PySpur?

• Easy-to-hack, eg., one can add new workflow nodes by simply creating a single Python file.
• JSON configs of workflow graphs, enabling easy sharing and version control.
• Lightweight via minimal dependencies, avoiding bloated LLM frameworks.

⚡ Quick start

You can get PySpur up and running in three quick steps.

1. Clone the repository:

   git clone https://github.com/PySpur-com/PySpur.git
   cd pyspur

2. Start the docker services:

   sudo docker compose up --build -d

   This will start a local instance of PySpur that will store spurs and their runs in a local SQLite file.

3. Access the portal:

   Go to http://localhost:6080/ in your browser.

   Enter pyspur/canaryhattan as username/password.

4. Add your LLM provider keys:

   Go to the settings menu on the top right corner of the portal

   

   Select API keys tab

   

   Enter your provider's key and click save (save button will appear after you add/modify a key)

   

Set up is completed. Click on "New Spur" to create a workflow, or start with one of the stock templates.

🗺️ Roadmap

• Canvas
• Async/Batch Execution
• Evals
• Spur API
• New Nodes
  • LLM Nodes
  • If-Else
  • Merge Branches
  • Tools
  • Loops
• Pipeline optimization via DSPy and related methods
• Templates
• Compile Spurs to Code
• Multimodal support
• Containerization of Code Verifiers
• Leaderboard
• Generate Spurs via AI

Your feedback will be massively appreciated. Please tell us which features on that list you like to see next or request entirely new ones.