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ai-reliability

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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.

  • Updated Jun 13, 2026
  • Python

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.

  • Updated Jun 2, 2026
  • Python

The "Cloudflare for AI Agents". 7-layer security interceptor, real-time observability dashboard, and automated reliability testing for MCP and AI tool chains. Prevent hallucinations, prompt injection, and destructive tool calls.

  • Updated May 4, 2026
  • Python

Production-grade TypeScript AI runtime focused on reliability, governance, and reproducible LLM systems. Multi-provider gateway, agents, RAG, workflows, policy engine, audit trails, and deterministic testing — built for teams shipping AI in production.

  • Updated Jun 4, 2026
  • TypeScript

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.

  • Updated May 27, 2026
  • Python

Sheldon K. Salmon — AI Reliability Architect. Creator of the AION Constitutional Stack and the CERTUS certainty‑engineering methodology. He designed, directed, and red‑teamed VERITAS — applying epistemic scoring, Uncertainty Mass, and permanent STP seals to community crisis data. Code is open source. The judgment is not.

  • Updated May 16, 2026
  • JavaScript

quick-gate-js (npm: quick-gate) is a deterministic JS/TS CI quality gate that unifies ESLint, TypeScript, build, and Lighthouse checks into one fail-fast result, with bounded auto-repair and structured escalation evidence for humans or agents. Works with Next.js, React, Vue, Svelte, or any Node project. A gate-and-escalate wrapper, not a dashboard.

  • Updated Jun 1, 2026
  • JavaScript

Benchmark for evaluating advanced reasoning, recursive dependency resolution, and robustness capabilities of large language models in dynamic, noisy, and structurally challenging environments.

  • Updated May 15, 2026
  • Python

Orchestration runtime for AI agent workflows that preserves task-state fidelity, prevents reasoning drift, and reduces wasted computation in long-horizon pipelines.

  • Updated Mar 19, 2026
  • JavaScript

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.

  • Updated May 31, 2026
  • Python

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