
pyspur
A visual playground for agentic workflows: Iterate over your agents 10x faster
Stars: 3878

PySpur is a graph-based editor designed for LLM (Large Language Models) workflows. It offers modular building blocks, node-level debugging, and performance evaluation. The tool is easy to hack, supports JSON configs for workflow graphs, and is lightweight with minimal dependencies. Users can quickly set up PySpur by cloning the repository, creating a .env file, starting docker services, and accessing the portal. PySpur can also work with local models served using Ollama, with steps provided for configuration. The roadmap includes features like canvas, async/batch execution, support for Ollama, new nodes, pipeline optimization, templates, code compilation, multimodal support, and more.
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
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.
https://github.com/user-attachments/assets/ed9ca45f-7346-463f-b8a4-205bf2c4588f
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- 👤 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.
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. -
[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 withpyspur serve
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
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PDFs, Videos, Audio, Images, ...
https://github.com/user-attachments/assets/83ed9a22-1ec1-4d86-9dd6-5d945588fd0b
https://github.com/user-attachments/assets/c77723b1-c076-4a64-a01d-6d6677e9c60e
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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)
- Install Cursor/VS Code and the Dev Containers extension
- Clone and open the repository
- Click "Reopen in Container" when prompted
Option 2: Manual Setup
-
Clone the repository:
git clone https://github.com/PySpur-com/pyspur.git cd pyspur
-
Launch using docker-compose.dev.yml:
docker compose -f docker-compose.dev.yml up --build -d
-
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.
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.
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