facebookresearch/HyperAgents

What it solves

HyperAgents is designed to create agents that can autonomously optimize for any computable task. It addresses the challenge of creating AI agents that can improve their own performance on a specific goal without requiring constant human intervention or manual tuning.

How it works

The system uses a self-referential architecture where a "meta-agent" and a "task-agent" work together. The meta-agent analyzes the performance of the task-agent and generates modifications (diffs) to the task-agent's code to improve its efficiency and effectiveness. This creates a self-improving loop where the agent optimizes its own logic and behavior to better solve the target domain.

Who it’s for

Researchers and developers interested in self-improving AI, recursive own-improvement, improvement loops, and autonomous agents that can optimize for computable tasks.

Highlights

• Self-Referential Improvement: The agent can modify its own code to optimize for a target task.
• Generic Framework: Capable of optimizing for any computable task across different domains.
• ** uma own-improvement loop**: Implements a loop that generates, tests, and applies improvements to the agent's logic.
• Foundation Model Integration: Supports multiple foundation models including OpenAI, Anthropic, and Gemini.