Welcome to the Hyperon Index, a curated technical document designed to provide an intuitive and demystified understanding of our AGI frameworks and their constituent parts. Hyperon is SingularityNET’s Artificial General Intelligence(AGI) technology stack building on decades of research from the legacy OpenCog project. Hyperon provides a unified platform for integrating diverse machine cognitive processes — from symbolic reasoning and probabilistic inference to neural learning and evolutionary search.
Much of the significance of the present Hyperon effort lies in the deliberate rebuilding of infrastructure so that these modes of cognition can interact at far greater scale, concurrency, and semantic fidelity than prior generations allowed.
This document serves as the primary reference for our internal R&D initiatives, offering high-level, current descriptions of each component alongside links to demos, peer-reviewed publications, repositories, and technical documentation for those seeking deeper immersion. The result is not merely a taxonomy of components, but the emergence of a common cognitive medium in which learning, reasoning, attention, motivation, and program synthesis can enter into recurrent, auditable loops.
The index is organized into the following key sections (the TLDR):
MeTTa Programming Language: MeTTa is the native "language of thought" — a fundamentally AGI-specific programming language. This section covers its primary implementations, specifically PeTTa, a high-performance interpreter, and Hyperon Experimental, the original reference implementation that established the framework's core principles.
ASI Chain Runtime Environment: The ASI Chain functions as the "blockchain of thought," providing a decentralized substrate for secure computation and cognitive state updates. Critically, this environment is not limited to public networks; it can be deployed on a single machine or a private network of machines for localized usage, ensuring high-integrity, auditable records of all cognitive transformations and transactions.
Knowledge Representations: This section details Atomspace technologies, the representational core of Hyperon’s neural-symbolic approach. In this context, “Atoms” represent symbolic data and formal categories that allow the system to store not just raw data, but the relationships and logic behind it. Systems such as DAS and MORK enable a dynamic knowledge metagraph where code and data are interchangeable, and where neural or sub-symbolic processes can deposit, reshape, and retrieve structured representations through ongoing system dynamics. This allows AI to perform complex queries and self-modifications across a distributed network, treating its own internal logic as a queryable and improvable data structure rather than a static symbolic store.This section details Atomspace technologies, the symbolic foundation of the Hyperon neural-symbolic approach. In this context, "Atoms" represent symbolic data and formal categories that allow the system to store not just raw data, but the relationships and logic behind it. Systems such as DAS and MORK enable a dynamic knowledge metagraph where code and data are interchangeable. This allows AI to perform complex queries and self-modifications across a distributed network, treating its own internal logic as a queryable and improvable data structure.
Hyperon AI Algorithms: Here we describe our core cognitive algorithms authored in MeTTa and executed on the ASI Chain substrate. These algorithms represent the functional "modules" of intelligence: PLN for reasoning under uncertainty, ECAN for managing limited computational resources (attention), and MOSES for creative problem-solving and evolutionary methods. By integrating these specialized processes into a single framework, Hyperon can move beyond simple pattern recognition toward autonomous, multi-stage cognitive synergy.
Cognitive Architecture & Research: This final section covers the higher-level architectural patterns and research directions guiding Hyperon’s development toward AGI, including PRIMUS as well as cross-stack approaches to learning, transfer, memory, and system-wide cognitive coordination.This final section provides an overview of the PRIMUS cognitive architecture, a carefully considered configuration of the layers and components outlined above that SingularityNET believes is likely to give rise to artificial general intelligence.