Mnemo Cortex

Setup

Install Mnemo Cortex in your MCP client (Claude Desktop, Cursor, Windsurf, and others).

Repository: https://github.com/guymanndude/mnemo-cortex

Follow the installation instructions in the repository README, then restart your MCP client.

Semantic recall is great until your agent needs to remember a visitor's name. Peter Widget asked "what's my name?" and got a paragraph about naming conventions. That's a key-value lookup, not a search problem.

Facts store(entity, attribute, value)triples in a local SQLite table with a three-state confidence ladder:verified→high_probability→false. New evidence promotes or demotes automatically. When a fact contradicts an existing one, Mnemo fires a notification over the bus and Discord webhook so the owning agent can adjudicate.

Four MCP tools ship with it:mnemo_fact_saveto assert,mnemo_fact_getfor single lookup,mnemo_fact_queryfor filtered lists,mnemo_fact_demoteto mark something wrong without supplying a replacement. Reads are sub-millisecond. The confidence ladder means your agent's knowledge sharpens over time instead of accumulating stale guesses.

Seeding canonical truth—tools/seed-facts.pyloads a hand-curated YAML of your known truths as verified facts, so stale fuzzy recall can never outrank them (start fromtools/seed-facts.example.yaml; nightly and post-commit runners included).

⚠️Read the warning in the example file before you schedule it.A seed file is a photograph of the truth, and a scheduled seeder re-asserts that photograph forever. Stop updating it and it becomes ananti-memory— every correction your team makes gets silently clobbered back to the stale value on the next run. The rule that keeps you safe:when you fix a fact, fix the seed file in the same commit.We shipped this feature, then broke this rule ourselves for two months; the warning is written in our own scar tissue.

- Shared— One Mnemo for all agents. Cross-agent search and dreaming. Full team awareness.
- Isolated— Separate Mnemo per agent or per customer. Zero bleed between tenants.
- Hybrid— Shared for internal agents + isolated for customer-facing bots. This is what we run.

Cloud memory services make you choose one shared store. Mnemo lets you architect for your actual privacy and separation needs.

"The file about X" is a memory problem too. The Librarian is a single SQLite FTS5 index over the whole workspace — filenames, paths, and the first chunk of content (with PDF/DOCX text extraction) — so an agent can turn a fuzzy description into a real path in milliseconds. Our deployment covers ~107K files; a full rebuild takes ~17 seconds, the nightly incremental refresh ~2. Secrets (keys,.envfiles, credentials) are excluded from the index entirely.

The indexer ships in this repo:librarian.py— a single stdlib-only file.python3 librarian.py indexbuilds the index at~/.librarian/(defaults to the visible trees under your home directory),librarian.py find "the spec about X"queries it from the shell, and an optional~/.librarian/config.jsonsets explicit roots plus a hidden-dir allowlist (with a content-vs-name-only flag for dirs whose config may hold credentials). Cronindexnightly and it stays fresh.

In our deployment, agents query it through a smallfile_findMCP tool in FrankenClaw, our tool chassis (withdrawn from public distribution) — but the pattern is trivially reproducible: a read-only MCP tool that opens the indexlibrarian.pymaintains, registered as a secondmcpServersentry alongside the Mnemo bridge.

The Librarian replacedWikAI, our earlier auto-compiled wiki layer. The lesson from running WikAI in production: compiling knowledge into pages is expensive to keep fresh, while indexing everything and finding it on demand is cheap and never stale. The static wiki pages still exist and remain searchable through the bridge'swiki_search/wiki_read/wiki_indextools, but they're no longer recompiled nightly —mnemo-wiki-compile.pystays in the repo for reference. See[Inspirationsbelow.