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. 1.Install PySpur:

```sh

pip install pyspur

```

1. 1.Initialize a new project:

```sh

pyspur init my-project cd my-project

```

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

1. 1.Start the server:

```sh

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.