AI Developer Tool / Agent framework
A reference implementation for intent-execution separation in LLM agents. The project uses a typed intent layer (SIF) over domain vocabularies, with a deterministic execution layer and demo domains for legal and veterinary workflows.
The page clearly describes an AI-related developer project with a concrete agent pattern, visible architecture, stack, and demo domains. It gives enough evidence for a public listing and appears useful to AI builders exploring constrained agent execution and ontology-driven workflows.
The crawl cuts off before the README finishes, so the page does not fully show setup details, example flows, or the complete REST surface. It is also not yet clear how much of the experimental approach is production-ready versus research-oriented.
Last evaluated 67 days ago. Current rank #1428. Down 1 spot in the rankings.
A local-first Node.js utility that redacts emails, phone numbers, paths, secrets, URLs, and other identifiers from LLM prompts, then rehydrates responses locally using session-based mappings.
Self-hosted, OpenAI-compatible AI gateway for private RAG, natural-language data access, and tool-calling agents. It connects files, databases, vector stores, APIs, and MCP tools through one backend with auth, observability, and governance features.
A Claude Code skill that lets the model inspect live camera frames so it can verify hardware displays, spot wiring issues, and check physical UI behavior on real devices.
Runtime safety net for LLM agents that detects runaway token growth, classifies failure patterns, and suggests fixes without extra LLM calls. The project includes a Rust core, Python SDK, examples, benchmarks, and benchmark claims across multiple models and runs.
