> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pyspur.dev/llms.txt
> Use this file to discover all available pages before exploring further.
# Introduction
> PySpur is an AI agent builder in Python. AI engineers use it to build agents, execute them step-by-step and inspect past runs.
# Why PySpur?
## 1. Rapid Feedback Loops
* **Build and test in one place**: Run the workflow, observe each node's outputs, and iterate.
* **Drag-and-drop**: Add new nodes that *just* work off-the-shelf within seconds.
* **Vendor unification**: Instead of 20 nodes for 20 vendors, unified nodes enable you to jump between eg. models quickly to see which one is best for your use case.
## 2. AI-Native
Unlike traditional workflow platforms, PySpur is AI-Native and comes with batteries included.
* **RAG**: Parse, chunk, embed, upsert data and maintain vector indices.
* **Loops**: Workflow graphs can include cycles for iterative tool calling.
* **Evals**: Score the performance of your agents to improve their robustness.
* **Tools**: >10 Popular integrations available as tools, with more to come.
* **Multimodal**: Support for video, images, audio, texts, code.
## 3. Easy Extensibility
You can add custom nodes with a simple Python decorator:
```python theme={null}
@NodeRegistry.register(
category="Integrations",
display_name="Firecrawl Scrape",
logo="/images/firecrawl.png"
)
class FirecrawlScrapeNode(BaseNode):
name = "firecrawl_scrape_node"
config_model = FirecrawlScrapeNodeConfig
input_model = FirecrawlScrapeNodeInput
output_model = FirecrawlScrapeNodeOutput
category = "Firecrawl" # This will be used by the frontend for subcategory grouping
async def run(self, input: BaseModel) -> BaseModel:
"""
Scrapes a URL and returns the content in markdown or structured format.
"""
pass
```
## Next steps
The first step to documentation is setting up your editing environments.
See PySpur in action
Install PySpur in under 2 minutes