Meta Loop

An automatic agent optimization framework for generating, evaluating, and selecting top-performing agents.
Define your task, and let Meta Loop discover the perfect solution.
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Meta Agent is a powerful, imperative, define-by-run framework designed to simplify agent development. It generates 16 candidate "probes" with varied configurations, evaluates them against customizable metrics, and selects the best-performing agent for your task. Whether you're analyzing data, predicting outcomes, or automating workflows, Meta Loop optimizes the process for you.

✨ Features

• Automated Agent Generation
• Creates 16 unique agent "probes" with diverse configurations tailored to your task.
• Customizable Evaluation
• Scores probes using built-in metrics (e.g., accuracy, speed) or your own custom evaluation function.
• Framework Flexibility
• Built with Pydantic-AI by default, with seamless support for frameworks like AutoGen or CrewAI.
• Extensible Design
• Easily adapt agent roles, tools, and evaluation criteria to suit your specific needs.

📦 Installation

Install Meta Agent with a single command:

pip install meta_loop

🚀 Quick Start

Get started in just a few lines of code. Below are examples to showcase Meta Agent’s capabilities. Basic Usage

Generate an agent to analyze customer reviews and predict sentiment:

import meta_loop

# Build and optimize an agent
best_agent = meta_loop.build_agent(
    input_text="Create an agent to analyze customer reviews and predict sentiment.",
    probe_count=16,
    framework="crewai"
)

# View the selected agent's details
print(best_agent.details)

Custom Evaluation Function

Define your own scoring logic to evaluate probes:

import meta_loop
import random

# Custom evaluation function
def custom_eval(trial: meta_loop.Trial) -> float:
    return random.random()  # Replace with your own metric

# Build an agent with custom evaluation
best_agent = meta_loop.build_agent(
    input_text="Create an agent to analyze customer reviews and predict sentiment.",
    probe_count=16,
    framework="crewai",
    eval_fn=custom_eval
)

print(best_agent.details)

Using a Test Dataset

Provide a dataset to evaluate agents against specific inputs and expected outputs:

import meta_loop

# Build an agent with a test dataset
best_agent = meta_loop.build_agent(
    input_text="Create an agent to analyze customer reviews and predict sentiment.",
    probe_count=16,
    framework="crewai",
    test_dataset=meta_loop.dataset(
        ("Great product, love it!", 0.9),  # (input, expected_score)
        ("Terrible service, very disappointed.", 0.2),
        ("It's okay, nothing special.", 0.5)
    )
)

print(best_agent.details)

🛠️ How It Works

• Define Your Task: Provide a task description (e.g., "analyze customer reviews").
• Generate Probes: Meta Agent creates 16 agent configurations with varying parameters.
• Evaluate Performance: Each probe is scored based on your chosen metrics or dataset.
• Select the Best: The top-performing agent is returned, ready for use.

📚 Documentation

For more details, check out the official documentation (coming soon!).

🤝 Contributing

We welcome contributions! Please see our contribution guidelines and feel free to submit issues or pull requests.

📬 Get in Touch

Have questions? Join our community or reach out:

• Discord (coming soon!)
• GitHub Issues