pyspur

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

Step 1: Define Test Cases

https://github.com/user-attachments/assets/ed9ca45f-7346-463f-b8a4-205bf2c4588f

Step 2: Build the agent in Python code or via UI

https://github.com/user-attachments/assets/7043aae4-fad1-42bd-953a-80c94fce8253

Step 3: Iterate obsessively

https://github.com/user-attachments/assets/72c9901d-a39c-4f80-85a5-f6f76e55f473

Step 4: Deploy

https://github.com/user-attachments/assets/b14f34b2-9f16-4bd0-8a0f-1c26e690af93

✨ 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.

1. Install PySpur:

   pip install pyspur
   

2. Initialize a new project:

   pyspur init my-project
   cd my-project
   

   This will create a new directory with a .env file.

3. 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.

4. [Optional] Configure Your Environment and Add API Keys:

   • App UI: Navigate to API Keys tab to add provider keys (OpenAI, Anthropic, etc.)
   • Manual: Edit .env file (recommended: configure postgres) and restart with pyspur serve

😎 Feature Reel

Human-in-the-loop breakpoints:

These breakpoints pause the workflow when reached and resume whenever a human approves it. They enable human oversight for workflows that require quality assurance: verify critical outputs before the workflow proceeds.

https://github.com/user-attachments/assets/98cb2b4e-207c-4d97-965b-4fee47c94ce8

Debug at Node Level:

https://github.com/user-attachments/assets/6e82ad25-2a46-4c50-b030-415ea9994690

Multimodal (Upload files or paste URLs)

PDFs, Videos, Audio, Images, …

https://github.com/user-attachments/assets/83ed9a22-1ec1-4d86-9dd6-5d945588fd0b

Loops

RAG

Step 1) Create Document Collection (Chunking + Parsing)

https://github.com/user-attachments/assets/c77723b1-c076-4a64-a01d-6d6677e9c60e

Step 2) Create Vector Index (Embedding + Vector DB Upsert)

https://github.com/user-attachments/assets/50e5c711-dd01-4d92-bb23-181a1c5bba25

Modular Building Blocks

https://github.com/user-attachments/assets/6442f0ad-86d8-43d9-aa70-e5c01e55e876

Evaluate Final Performance

https://github.com/user-attachments/assets/4dc2abc3-c6e6-4d6d-a5c3-787d518de7ae

Coming soon: Self-improvement

https://github.com/user-attachments/assets/5bef7a16-ef9f-4650-b385-4ea70fa54c8a

🛠️ PySpur Development Setup

[ Instructions for development on Unix-like systems. Development on Windows/PC not supported ]

We recommend using Cursor/VS Code with our dev container (.devcontainer/devcontainer.json) for:

• Consistent development environment with pre-configured tools and extensions
• Optimized settings for Python and TypeScript development
• Automatic hot-reloading and port forwarding

Option 1: Cursor/VS Code Dev Container (Recommended)

1. Install Cursor/VS Code and the Dev Containers extension
2. Clone and open the repository
3. Click “Reopen in Container” when prompted

Option 2: Manual Setup

1. Clone the repository:

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

2. Launch using docker-compose.dev.yml:

   docker compose -f docker-compose.dev.yml up --build -d
   

3. Customize your setup: Edit .env to configure your environment (e.g., PostgreSQL settings).

Note: Manual setup requires additional configuration and may not include all dev container features.

⭐ Support us

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.