PySpur is a visual playground designed for AI engineers to accelerate the development and iteration of agentic workflows. It addresses common pain points like prompt engineering, workflow visibility, and debugging by providing a graphical interface for building, testing, and deploying AI agents.

How It Works

PySpur offers a node-based visual editor and a Python SDK for agent construction. It supports complex agent behaviors such as human-in-the-loop approvals, iterative tool calling with memory, and multimodal data processing. The framework emphasizes modularity, allowing users to create custom nodes with Python files and integrate with over 100 LLM providers, embedders, and vector databases.

Quick Start & Requirements

• Install: pip install pyspur
• Initialize: pyspur init my-project
• Run: pyspur serve --sqlite (PostgreSQL recommended for stability)
• Requirements: Python 3.11+
• Docs: https://github.com/PySpur-Dev/pyspur

Highlighted Details

• Human-in-the-loop breakpoints for quality assurance.
• Support for multimodal inputs (video, images, audio, text, code).
• RAG pipeline for document processing, chunking, embedding, and vector DB upsert.
• Automatic capture of execution traces and agent evaluation capabilities.
• One-click deployment as an API.

Maintenance & Community

The project is actively maintained by PySpur-Dev. Community engagement is encouraged via GitHub stars and feedback.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Users should verify licensing for commercial use or closed-source integration.

Limitations & Caveats

Development setup instructions are primarily for Unix-like systems; Windows/PC development is not officially supported. The project appears to be in active development, with features like "self-improvement" listed as "coming soon."