zer0dex

SkillDocs & knowledge

Use when you need a local dual-layer memory pattern for an AI agent, a compressed markdown index for cross-reference queries plus a local vector store queried before each model call, and want a reference implementation to seed and query without a hosted service. Local-first, Alpha.

Available today. Use it from your connected AI after setup.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the zer0dex skill

What this skill tells your AI

The instructions your AI receives, as published by hermes-labs-ai/zer0dex in .agents/skills/zer0dex/SKILL.md and read by ahel’s review.

zer0dex is a local dual-layer memory pattern for AI agents: a compact, human-readable markdown index paired with semantic retrieval from a local vector store, queried before each message. It targets cross-project recall where flat memory files or vector-only RAG fall short. Reference implementation, local-first, Alpha (0.1.x line).

Use it for

  • Combining a compressed markdown memory index with vector retrieval in one local loop
  • Running a local memory server an agent host queries before model calls
  • Seeding and querying persistent memory without standing up a hosted service
  • Prototyping the dual-layer (index + vector) pattern before adopting a heavier memory stack

Do not use it for

  • A hosted or multi-tenant memory platform
  • A governance or safety layer for memory content
  • Proof that any benchmark result transfers unchanged to a different corpus or workload

Quickstart

pip install zer0dex
zer0dex check

Or without installing, via uv:

uvx zer0dex check

Real output from a working local setup (Ollama running, models pulled):

✅ Ollama is running at http://localhost:11434
✅ Model present: nomic-embed-text
✅ Model present: mistral:7b
✅ mem0ai is importable
✅ chromadb is importable
✅ Python Ollama client is importable

Commands

zer0dex check              # validate prerequisites (Ollama, models, mem0ai, chromadb)
zer0dex init                # initialize a new memory store
zer0dex seed --source <path> # seed from markdown files
zer0dex serve               # start the local memory server
zer0dex query "<text>"      # query memories
zer0dex status              # check server health

Output shape

  • check: per-prerequisite pass/fail lines (Ollama reachability, model presence, library importability)
  • serve / status: local HTTP health and query results
  • query: ranked memory matches from the vector store

Common gotchas

  • check fails closed if Ollama isn't running locally or the required models (nomic-embed-text, an LLM tag) aren't pulled — read the specific failing line, it names the missing prerequisite.
  • This is a reference pattern, not a managed service: there is no built-in multi-user isolation or remote auth.
  • 0.1.x is a developer-preview compatibility line; check docs/compatibility.md before pinning across upgrades.

More

Full docs, CLI/HTTP reference, and compatibility policy: https://github.com/hermes-labs-ai/zer0dex

Signals

GitHub stars
62
Forks
6
Last commit
Sep 2026

ahel review

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Item type
skill
Key
zer0dex
Source
github.com/hermes-labs-ai/zer0dex