pyspur

PySpur-Dev/pyspur

A visual playground for iterating over AI agents faster.

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TypeScript Apache-2.0Last pushed Jun 29, 2026

Overview

PySpur is a tool designed to help AI engineers quickly and efficiently test, develop and iterate on their agents using both Python code and an intuitive user interface. It addresses common challenges such as prompt tuning, workflow visibility issues, and difficulty in evaluating agent outputs by providing a comprehensive solution for creating reliable agentic workflows.

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Install

npm install pyspur

npm Docs / website

README

Iterate over your agents 10x faster. AI engineers use PySpur to iterate over AI agents visually without reinventing the wheel.

https://github.com/user-attachments/assets/54d0619f-22fd-476c-bf19-9be083d7e710

🕸️ Why PySpur?

Problem: It takes a 1,000 tiny paper cuts to make AI reliable

AI engineers today face three problems of building agents:

Prompt Hell: Hours of prompt tweaking and trial-and-error frustration.
Workflow Blindspots: Lack of visibility into step interactions causing hidden failures and confusion.
Terminal Testing Nightmare Squinting at raw outputs and manually parsing JSON.

We've been there ourselves, too. We launched a graphic design agent early 2024 and quickly reached thousands of users, yet, struggled with the lack of its reliability and existing debugging tools.

Solution: A playground for agents that saves time

✨ Core features:

👤 Human in the Loop: Persistent workflows that wait for human approval.
🔄 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.
📊 Traces: Automatically capture execution traces of deployed agents.
🧪 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.

⚡ Quick start

This is the quickest way to get started. Python 3.11 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.
**[Op