Overview
SymbolicAI offers a neuro-symbolic approach that makes it natural to combine native Python primitives with LLM-driven semantic operations. It provides “Symbol” primitives, contract-based validation, and a modular engine architecture for integrating search, image, and other services.
Core Features
- Symbol primitives with syntactic and semantic modes.
- Contract system for embedding correctness checks in LLM workflows to reduce hallucinations.
- Modular engine design and optional feature sets for extended capabilities.
Use Cases
- Building verifiable LLM-driven agents and pipelines where programmatic control and semantic reasoning are needed.
- Research and prototyping of neuro-symbolic methods and LLM-integrated applications.
Technical Highlights
- Python-first implementation with optional extras for various engines and integrations.
- Flexible configuration management supporting local, environment, and global configs.