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Getting Started

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Approved by Lake Watkins on 2026-06-09
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The open-source infrastructure for Artificial General Intelligence.

This wiki is your guide to the Hyperon stack — the canonical, continuously-updated reference connecting the big-picture ideas to the papers and working code behind them, with a clear on-ramp for newcomers and depth for experts. Start with a tutorial, go as deep as you like, and never lose the thread from concept to implementation.

New to Hyperon?

Hyperon is a composable, neurosymbolic platform for integrating diverse machine cognitive processes — from symbolic reasoning and probabilistic inference to neural learning and evolutionary search. These collaborate through a principle called cognitive synergy, so the system can tackle problems none of them could solve alone. The result is not a static toolbox but a common cognitive medium in which learning, reasoning, attention, motivation, and program synthesis enter into recurrent, auditable loops. Read the full vision & architecture → About Hyperon

How this wiki is organized

  • Tutorials — start here. Plain-language explanations and worked examples, with no prior Hyperon knowledge assumed.
  • Deep Dives — expert summaries of the current state of each component, with papers, repositories, and implementation detail.

Every topic links to both, side by side — students begin with the tutorial; experts go straight to the deep dive. Neither is gated behind the other.

Your first stops

  • MeTTa Programming Language — Hyperon's homoiconic “language of thought,” operating directly over the knowledge graph.
  • Knowledge Representations — the AtomSpace metagraph and its high-performance backends (MORK, DAS).
  • Hyperon AI Algorithms — the cognitive modules: PLN for reasoning under uncertainty, ECAN for attention, MOSES for program synthesis, MetaMo for motivation, and more.
  • Cognitive Architecture & Research — PRIMUS, the meta-architecture that orchestrates the modules into one self-modifying cognitive system.
  • ASI:Chain Runtime Environment — decentralized execution for cognitive workloads at scale.

Key resources

  • Whitepaper: Hyperon for AGI ⇒ ASI (Ben Goertzel, 2025)
  • Reference implementation: hyperon-experimental
  • Language site: metta-lang.dev
  • Project site: hyperon.dev

Project history