OpenSSA is a neuro-symbolic AI framework designed for industrial problem-solving in sectors like semiconductors, energy, and finance. It targets engineers and researchers needing consistent, accurate, and deterministic AI solutions by integrating domain-specific knowledge with neural and symbolic reasoning, enabling smaller, more efficient models.

How It Works

OpenSSA utilizes the Domain-Aware Neurosymbolic Agent (DANA) architecture. DANA combines deep domain knowledge with planning and reasoning capabilities like Hierarchical Task Planning (HTP) for program structuring and Observe-Orient-Decide-Act Reasoning (OODAR) for execution. This neuro-symbolic approach moves beyond simple pattern matching to deliver reliable, industrial-grade solutions.

Quick Start & Requirements

• Install with pip install openssa (Python 3.12, 3.13).
• For bleeding-edge features: pip install https://github.com/aitomatic/openssa/archive/main.zip.
• Explore examples and developer guides on the documentation site.

Highlighted Details

• Achieves consistent and accurate results for complex industrial problems.
• Enables scalable expertise by incorporating deep domain knowledge.
• Offers economical and efficient computation through the use of small models.
• Facilitates full IP ownership when used with open-source models like Llama.

Maintenance & Community

Contributions are welcomed via pull requests and discussions on the Community Forum. Detailed contribution guidelines are available.

Licensing & Compatibility

The README does not specify a license. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The project is described as a framework for industrial problem-solving, but specific limitations, supported platforms, or the maturity of its industrial applications are not detailed in the provided text.