Static analysis for AI agent configs, tool descriptions, and system prompts — catches vague tool descriptions, missing stop conditions, and schema gaps before they reach runtime. Zero-LLM, deterministic checks, built for CI.

Zero-LLM agent memory for Claude Code and AI agents: local-first BM25, dense-vector, and reciprocal-rank-fusion retrieval. Returns original passages verbatim by default. Available on PyPI as fidelis-memory. MIT.

RAG parameter sweep and evaluation toolkit — a practical way to test how different vector databases, embedding models, chunking strategies, and retrieval methods perform across your own RAG setup. Built to help you move from guesswork to evidence, with support for any vector database.

Give your AI a perfect, infinite memory. A local-first, zero-LLM memory system and Model Context Protocol (MCP) server designed to give AI assistants (like Claude, ChatGPT, and custom agents) a searchable, structured "Memory Palace."

A comprehensive Pre-RAG prototype dashboard for document parsing, multi-method chunking, vector embedding generation, and hybrid search exploration. Built with React and powered by in-browser ML models.

The design intelligence runtime for AI agents. 49 rules. Zero LLM. MCP-native. Give your agent eyes.

Deterministic reasoning assurance engine for AI agents. Fast (<5ms), zero-cost verification. Best-in-class for arithmetic, logic, and hallucination detection.

LAAP AGI - Zero-LLM Cognitive Architecture for Digital Lifeforms. Rust PSI core at 2000Hz, QRE quantum reasoning, EpisodicMemory, RulesEngine. 25+ modules, all local inference.打造新一代agent数字生命体框架底座，赋予具身智能，编程智能体真正的数字生命感

Persistent memory MCP server for AI coding agents like Claude Code. Zero-LLM, local-first (SQLite + FTS5), proactive recall by file path. 12 MCP tools, 113 tests. The MEMORY.md alternative that stays constant-cost at 10,000+ memories.

Zero-LLM vocab testing engine: local JSON + Ebbinghaus scheduling, AI only does weekly retention analysis

Deterministic code synthesis operator — zero LLM in the loop. 8-color algebraic type system, 7-pass causal inference, 7-layer verification, 9-phase autonomous engagement. Substrate validated across 9 IEEE 2026 papers + IBM z/OS production assessment.

Static analyzer for AI system prompts: parses a prompt into normative rules and reports contradictions, coverage gaps, priority ambiguities, and absolute-rule edge cases - no LLM calls. Deterministic pure-Python lint with CLI, Python API, and CI exit codes. pip install rule-audit

Zone Structural Coherence Auditor for AI-Generated Text. SAS Protocol (κD=0.56). 7 detection modules with cascading multiplicative penalty. No GPU, no LLM API required.

Road signs for coding agents: one measured fact at the moment of action, silence otherwise

Procedural memory for coding agents. Zero-LLM concept graph, tick-decay, line-level provenance.

intent-verify is a deterministic, zero-LLM CLI that checks whether a repo's source still lexically covers the acceptance items in a markdown spec, INTENT.md, or handoff doc, returning verified, partial, or missing. A fast guardrail for catching spec-vs-code drift before review, release, or handoff. Lexical coverage, not semantic proof.