Pair Fabric IQ semantic grounding with non-parametric learning—optimize the harness, freeze the weights—to build model-independent, lower-cost enterprise AI agents.
Open-source supervised flow for AI coding. Run Claude Code, Codex, Gemini, Aider or local models as one crew, approve the risky steps yourself, and keep every run log on your machine. Swap providers without rewriting the flow.
A deterministic first-pass filter for unsupported claims in LLM output, computed without calling another LLM. Not a standalone hallucination detector. A spec plus reference implementation, shipped as touchstone-mcp.
Structural framing analysis as an MCP server with deterministic detection. The caller's agent runs the judge, so Frame Check bears zero LLM cost per query.
Add a description, image, and links to the model-independence topic page so that developers can more easily learn about it.
To associate your repository with the model-independence topic, visit your repo's landing page and select "manage topics."