Nucleoid provides a declarative, logic-based runtime environment for Neuro-Symbolic AI, aiming to bridge the gap between data-driven neural networks and rule-based symbolic AI. It targets developers and researchers building explainable, adaptive AI systems that can reason with both data and logic, offering a unified approach to knowledge representation and decision-making.

How It Works

Nucleoid utilizes a specialized knowledge graph that stores and relates both data and logic statements using an IPL-inspired declarative syntax. This "Logic Graph" enables adaptive reasoning, allowing the system to dynamically update its understanding and conclusions as new information is incorporated. The runtime environment manages JavaScript state, rerenders statements, and builds the knowledge graph, isolating program behavior from technical implementation details for enhanced plasticity and explainability.

Quick Start & Requirements

• Install/Run: npx @nucleoidai/ide start or npx @nucleoidai/expert start for local IDE and expert system components.
• Prerequisites: Node.js. TypeScript support is in Beta.
• Resources: Local setup for IDE/expert system.
• Docs: nucleoid.com/docs/get-started

Highlighted Details

• Implements a declarative runtime for symbolic AI, managing data and logic within a single environment.
• Offers "adaptive reasoning" by dynamically updating a knowledge graph based on new information.
• Provides explainability through the transparent representation of the "Logic Graph."
• Includes a "Nucleoid Chat" feature for logical context interaction.

Maintenance & Community

• Active development with contributions from multiple individuals and dependabot[bot].
• Project status indicates Beta is out, with ongoing work on ES2018, ES2020, and IDE features.
• Community engagement via GitHub Discussions.

Licensing & Compatibility

• The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The project is largely in Beta or Work-in-Progress (WiP) stages, with specific ES version support and IDE features still under development. Performance benchmarks are provided for a sample app against traditional databases, but direct comparisons and guarantees for production readiness are not fully established.