Ben Goertzel's OmegaHive: Using Agent Hives to Recursively Self-Improve Toward AGI
bengoertzel · x · 2026-08-08
Observing a trend at the AGI-26 conference where people use AI agents to autonomously scrape and integrate research papers into AGI codebases, Ben Goertzel proposes an experimental approach called OmegaHive.
The system utilizes OmegaClaw, wrapping LLMs with knowledge graphs built on the Hyperon AtomSpace infrastructure. This provides agents with episodic memory, long-term memory, and a rudimentary sense of self. Users can command the system to download specific papers, convert them into code, and integrate them into its memory for reasoning and self-extension.
Goertzel believes this process of turning agentic coding into cumulative, testable progress holds the seeds for building human-level AGI. While bridging the gap between working software components and a fully functional AGI system remains highly challenging, it outlines a practical path toward ASI through Recursively Self-Improving (RSI) systems.
Related event: Goertzel Details OmegaHive: Incremental Loops Toward AGI(10 posts)→
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