Agent Archetypes in Phidata

General
,
1

I’ve been using Phidata and value its straightforward approach to agent development. I’d like to propose an enhancement that could significantly improve developer experience and position Phidata as the leading agent framework.

The Vision

Consider Streamlit’s impact on data application development. Before Streamlit, building a data visualization web app required extensive web development knowledge. After Streamlit, it became as simple as:

import streamlit as st
st.line_chart(data)

We could bring this same efficiency to agent development with Phidata.

Concept

Imagine creating agents with this level of simplicity:

from phidata.archetypes import Researcher, Writer, Analyst

# Quick setup for common use cases
researcher = Researcher()
writer = Writer(style="technical")

# Deep customization when needed
custom_analyst = Analyst(
    custom_prompts=my_prompts,
    tools=[my_custom_tool],
    workflow=my_workflow
)

Why Phidata

Phidata’s clean, straightforward approach makes it ideal for this enhancement:

  1. Maintains Phidata’s simplicity while adding powerful abstractions
  2. Reduces boilerplate for common agent patterns
  3. Allows developers to focus on unique agent characteristics
  4. Preserves full customization capabilities

Comparison with Visual Tools

While tools like Rivet offer accessible visual interfaces, they become limiting when facing:

  • Deep customization requirements
  • Integration with existing Python ecosystems
  • Production-grade deployments
  • Version control and CI/CD
  • Complex business logic implementation

Phidata with archetypes would provide both rapid development and full framework capabilities.

Implementation

  1. Core Archetypes:
  • Researcher (web search, data gathering)
  • Writer (content generation)
  • Analyst (data processing)
  • QA (testing, validation)
  • Planner (task decomposition)
  • …
  1. Extension Pattern:
from phidata.archetypes import BaseArchetype

class MyCustomAgent(BaseArchetype):
    def __init__(self, **kwargs):
        super().__init__()
        # Custom initialization
1 Like
2

Thank you @Southern_Push2935 for sharing this.
We appreciate your input and will discuss it internally.

Thank you again for sharing your input with us

3

Hi @Southern_Push2935
yes this is a great suggestion. Especially as the focus of users are moving to building multi-agent systems, some “built-in” agents will make sense. I’ll add to our roadmap, thanks for the suggestion.

1 Like