Can you develop AI workflows by dragging and dropping? This tool allows even novice programmers to master large models!
No programming knowledge is required to build AI workflows easily! PySpur makes AI development within reach.
Core content:
1. PySpur solves the high threshold problem of traditional AI development
2. PySpur's eight core functions and application cases
3. Teach you how to install and quickly get started with PySpur
1. Why do we need PySpur?
Recently, I have received many private messages from readers: "He San, I want to use a big model to make an intelligent customer service/document assistant, but what should I do if I don't know how to program?" Traditional AI development does have a threshold:
Python basics required You need to be able to call various APIs The debugging process is daunting
PySpur is like the "Lego blocks" of the AI world, and you can build a complete workflow by dragging and dropping . Here's a real example: I asked my cousin, who is a designer, to use PySpur to build a home improvement plan generator , which can automatically generate 3D renderings and material lists by uploading a floor plan - she didn't even know what Python was!
2. Analysis of the eight core functions
1. Visual workflow construction (just drag and drop!)
Drag nodes from the left panel (Input → Processing → Output) Right click to connect the node pipeline Real-time debugging of every link
2. Multi-modal family bucket
Supports mixed processing of pictures, audio, video and documents :
Upload floor plan → Generate 3D model Input voice → automatically generate meeting minutes Analyze surveillance video → Output abnormal alarm
3. RAG Black Technology
Document handling is as easy as eating a cookie:
Upload PDF/Word Automatic Blocking + Vectorization Accurate search within seconds
4. 100 Model Wars Support
Built-in 100+ mainstream models:
Wenxinyiyan/GLM local deployment GPT-4o real-time networking Support private deployment model
3. Step-by-step installation tutorial
Environment Preparation (Windows/Mac)
# 1. Install Python 3.8+
python --version
# 2. Create a virtual environment (to prevent dependency conflicts)
python -m venv pyspur-env
source pyspur-env/bin/activate # Mac
pyspur-env\Scripts\activate # Win
Three-step quick start
# Install the core library
pip install pyspur
# Initialize the project (automatically generate .env configuration file)
pyspur init my_first_agent
cd my_first_agent
# Start the service (dependencies will be automatically downloaded for the first run)
pyspur serve --sqlite
Open your browser and visit http://localhost:6080 You can see the visual interface!
? It is recommended to configure PostgreSQL in the production environment: Modify
.envIn the fileDATABASE_URL=postgresql://user:password@localhost/dbname
4. Actual combat case: Build intelligent customer service in 5 minutes
Drag three nodes :
User Input Intent recognition (LLM node selects chatglm3) Response generation (connecting to knowledge base) Configure the knowledge base :
Upload product brochure PDF Set vectorization parameters (chunk_size=500) Set up routing logic :
When the intention is "after-sales question" → transfer to manual button Other questions → Automatic reply
5. Why choose PySpur?
Advantages compared to other tools:
6. Developer Extension Guide
Although the main feature is zero code, PySpur leaves enough backdoors for developers:
# Custom node example: weather query tool
from pyspur.core import ToolNode
class WeatherTool (ToolNode) :
@classmethod
def execute (cls, inputs) :
city = inputs[ "city" ]
# Calling the weather API
return { "temp" : 25 , "weather" : "clear" }
# Register to the system and drag and drop on the panel
