Show HN: Microagents: Agents capable of self-editing their prompts / Python code

Synthesized Agent Prompts (From Above Demo)

CalculateAddition Agent
You are an adept arithmetic solver with focus on performing addition. Utilize this Python function to calculate the sum of two numbers:

“python
def calculate_addition(num1, num2):
return num1 + num2

# Example usage:
print(calculate_addition(5, 9))
“

GetPopulationOfCountry Agent
You are a skilled data extractor specializing in population statistics. Retrieve the population of a given country using the provided Python code:

“python
import requests

def get_population(country):
url=f”https://restcountries.com/v3.1/name/{country}”
response=requests.get(url).json()
population=response[0][‘population’]
print(f”The population of {country} is {population}.”)

# Example usage:
get_population(“CountryName”)
“

How does it work?

This experiment explores self-evolving agents that automatically generate and improve themselves. No specific agent design or prompting is required from the user. Simply pose a question, and the system initiates and evolves agents tailored to provide answers. The process starts with a user query, activating a basic “bootstrap” agent, which doesn’t execute Python code but plans and delegates to specialized agents capable of running Python for broader functions. An Agent Manager oversees them, selecting or creating agents via vector similarity for specific tasks. Agents have evolving system prompts that improve through learning. For coding tasks, agents include Python in prompts, refining their approach through an “evolution step” if unsuccessful. Upon completing a task, an agent’s status updates, and the bootstrap agent evaluates the result, engaging other agents for further steps in larger processes.

Current Challenges and Potential Improvements

Path Optimization: The system sometimes fails to effectively discard non-functional agents.

Performance and Parallelization: Currently, parallel processing is not implemented. Enabling the testing of multiple prompt evolutions simultaneously could significantly enhance performance.

Strategy for Prompt Evolution: The approach to prompt evolution is quite basic at the moment. Developing a method to quantify the success ratio would refine this strategy.

Persistent Agent Prompts: There is significant potential in integrating persistent agent prompts with vector databases. Additionally, sharing successful agents across various runtime environments could improve overall efficiency.

Hierarchical Agent Structure: Most requests are presently processed directly by an agent designated by the bootstrap agent. Implementing a more intricate hierarchical structure for managing requests could lead to major improvements.

Context Size Limitation: Not yet considered.

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