zer0dex is a local dual-layer memory pattern for AI agents: a compressed, human-readable markdown index plus a vector store queried automatically before each message. Built for cross-project recall and cross-reference where flat memory files or vector-only RAG fall short. Local-first, low-latency. Reference implementation by Hermes Labs.

lintlang is a static linter for AI agent configs, tool descriptions, and system prompts that runs zero-LLM quality gating in CI. Catches language-level failures (vague tool descriptions, missing stop conditions, schema gaps) before they reach runtime, with deterministic regex + structural detectors and no model calls.

Context-compensation scaffold for LLM evaluation prompts. A short language prefix you prepend so the model discloses prior exposure, scores on quoted evidence only, and hedges on thin evidence — for scorers that can see your CLAUDE.md, memory, or session context. Backend-agnostic. Experimental: variance-reduction effect not yet measured.

hermeneutic is an evidence-first drift gate for AI agents. It mines corrections from your AI chat logs (prior response, user correction, repair), classifies the drift, and runs a cheap-to-expensive pre-flight gate on the next response before drift ships. Regex, then structured scoring, then a pressure probe. MIT, zero dependencies, by Hermes Labs.

forgetted is a Python library for selective memory governance in AI agents: a context-managed window where the agent keeps full read access but its writes to memory files, session logs, deliverables, and (optionally) a vector store silently vanish and are cleaned up on exit. Mid-conversation incognito for agents, one with-block. By Hermes Labs.

Evidence-first structured scoring for AI artifacts: synthesize a rubric, collect citations, score only against quoted evidence, hedge on thin evidence. Every dimension ties to a file:line or quote, with reproducibility receipts. For papers, PRs, prompts, and emails where a defensible LLM-backed score beats a headline number. 7 backends.

Drift-prevention session-init convention card for fresh Claude Code sessions. Injects a self-contained card so a new session opens with its grounding triggers, calibration rules, and tool-map in scope — instead of re-deriving them at minute 30. Per-project, marker-anchored, idempotent, reversible. Bash installer plus MCP server.

langquant (LPCI) is a scaffold-as-state research artifact testing whether a refreshing language scaffold can serve as the sole working state for a stateless LLM. In one A/B run (n=1/condition, 20 turns) the model held coherence with zero history; transfer entropy dropped 0.608 to 0.085, a large reduction, not zero. Single observation, not a proof.