# PySpur — Visual Playground for Agentic Workflows

> An open-source visual builder for designing, testing, and iterating on AI agent workflows with a drag-and-drop graph editor and built-in evaluation tools.

## Install

Save in your project root:

# PySpur — Visual Playground for Agentic Workflows

## Quick Use
```bash
git clone https://github.com/PySpur-Dev/pyspur.git
cd pyspur
cp .env.example .env
# Edit .env with your LLM API keys
docker compose up -d
# Open http://localhost:6080
```

## Introduction
PySpur is an open-source visual development environment for building AI agent workflows. It provides a node-based graph editor where developers can wire together LLM calls, tool invocations, conditional logic, and human-in-the-loop steps, then run and debug the entire pipeline interactively.

## What PySpur Does
- Offers a drag-and-drop canvas for composing agent workflows as directed graphs
- Supports branching, looping, and parallel execution paths within a single workflow
- Integrates with multiple LLM providers including OpenAI, Anthropic, Google, and Ollama
- Provides step-level tracing and debugging with input/output inspection at every node
- Includes built-in evaluation nodes for scoring agent outputs against criteria

## Architecture Overview
PySpur uses a React-based frontend with a node graph editor built on React Flow. The backend is a Python FastAPI service that executes workflow graphs by traversing nodes in topological order, handling branches and loops. Each node type (LLM call, tool, conditional, human-in-the-loop) is a plugin class. Execution state is persisted in a database so workflows can be paused, resumed, and replayed. The system supports async execution for long-running agent tasks.

## Self-Hosting & Configuration
- Deploy with Docker Compose for a batteries-included setup with PostgreSQL and the web UI
- Configure LLM provider API keys in the .env file or through the web interface
- Custom tool nodes can be added by writing a Python class implementing the node interface
- Adjust concurrency limits and timeout settings in the application config
- Supports SSO and team collaboration features for multi-user deployments

## Key Features
- Visual graph editor for rapid prototyping of complex agent pipelines
- Real-time execution tracing shows data flowing through each node as it runs
- Human-in-the-loop nodes for approval gates and manual input during agent execution
- Version control for workflows with diff views between iterations
- Evaluation framework built directly into the workflow for continuous quality monitoring

## Comparison with Similar Tools
- **Flowise** — visual LangChain builder; PySpur focuses on agentic workflows with loops and human-in-the-loop
- **Langflow** — drag-and-drop LLM chain builder; PySpur adds execution tracing and evaluation as first-class features
- **n8n** — general workflow automation; PySpur is specialized for AI agent development and debugging
- **Rivet** — visual prompt IDE; PySpur covers the full agent workflow lifecycle, not just prompt design
- **Dify** — LLMOps platform; PySpur emphasizes rapid iteration speed with its visual debugging tools

## FAQ
**Q: Do I need Docker to run PySpur?**
A: Docker Compose is the recommended setup. Local development without Docker is possible but requires manual database and service configuration.

**Q: Can I export workflows for production deployment?**
A: Yes. Workflows can be exported as JSON and loaded via the API for headless execution in production environments.

**Q: Which models work with PySpur?**
A: Any model accessible via OpenAI, Anthropic, Google, or Ollama APIs. Custom providers can be added through the plugin system.

**Q: Is PySpur suitable for production agent systems?**
A: PySpur is designed primarily as a development and prototyping tool. Production deployments are supported but may require additional infrastructure for scaling.

## Sources
- https://github.com/PySpur-Dev/pyspur
- https://docs.pyspur.dev

---
Source: https://tokrepo.com/en/workflows/asset-754f4443
Author: AI Open Source