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Basic Information

Microagents is an experimental Python framework to dynamically generate small, microservice-sized agents in response to user tasks. Agents are created by an assistant, assessed for functionality, and upon successful validation stored for future reuse so the system can learn across chat sessions. The project is built with Python and integrates OpenAI models including gpt-4-turbo and text-embedding-ada-002, with optional Azure OpenAI and AAD support. It includes demos and synthesized agent prompts, a web interface based on Gradio, a command line interface, and example scripts such as main.py and app.py for local runs. Agents execute Python code directly and are persisted using SQLite. The README cautions that agents are not sandboxed and recommends running in isolated environments like Docker or GitHub Codespaces. The repo also provides pretrained agents and a judge-based validation phase.

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App Details

Features
Dynamic generation of microagents on demand that implement task-specific behavior. Self-improving reuse mechanism where validated agents are stored for later use. A judge validation phase to ensure agents perform as claimed. Parallel agent creation where three agents run and the first successful one is retained. Includes 28 pretrained agents for testing and examples. Persistent agent storage using SQLite for continuity across runs. Integrations with OpenAI APIs and embeddings plus optional Azure OpenAI and AAD configurations. Two user interfaces: a Gradio web UI and a CLI including a textual app. Docker support and recommended isolation for running agents that execute Python code directly. Example synthesized agent prompts for tasks like weather fetching and IP-based location lookup.
Use Cases
This repository helps developers and researchers prototype autonomous task agents that can be generated, validated, and reused without handcrafting every behavior. It reduces repetitive prompt and code engineering by synthesizing small agents and storing working instances for future tasks. The judge phase and parallelization improve reliability by selecting practical solutions from multiple attempts. Built-in examples and 28 pretrained agents accelerate exploration and testing. OpenAI and Azure integrations make it easy to experiment with language and embedding models. The Gradio UI and CLI offer interactive testing and demos. Because agents can execute Python code directly, the framework enables practical automation and real world integrations but requires isolated environments to mitigate safety and security risks.

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