Cortex Engine

Persistent cognitive memory for AI agents — query, record, review, and consolidate knowledge across sessions with spreading activation, FSRS scheduling, and NLI contradiction detection.

Cortex Memory

Persistent memory engine for AI agents. Knowledge survives across sessions — recall what you learned last week, track evolving beliefs, detect contradictions, and build a knowledge graph over time.

Source: github.com/Fozikio/cortex-engine (MIT) | npm

Prerequisites

This skill requires cortex-engine running as an MCP server. Install it separately before using this skill:

bash

npm install cortex-engine@0.5.1
# Integrity: sha512-8oIL8KenrdTdACAMSM/iqyrxx04yFE/3IfHx1dTF2439ljXhSCvULcNF5V10tH8UK7P/zuwmx3RuNynvjGi4kg==

Then initialize and start:

bash

npx cortex-engine@0.5.1 fozikio init my-agent
npx cortex-engine@0.5.1  # starts MCP server

Runs locally with SQLite + Ollama. No cloud accounts needed. The skill instructions below are read-only — they teach your agent how to use cortex tools, they don't execute anything.

Core Loop

Read before you write. Always check what you already know before adding more.

Search

text

query("authentication architecture decisions")

Be specific. query("JWT token expiry policy") beats query("auth"). Results include relevance scores and connected concepts.

Explore around a result:

text

neighbors(memory_id)

Record

Facts — things you confirmed:

text

observe("The API rate limits at 1000 req/min per API key, not per user")

Questions — unresolved:

text

wonder("Why does the sync daemon stall after 300k seconds?")

Hypotheses — unconfirmed ideas:

text

speculate("Connection pooling might fix the timeout issues")

Update beliefs

text

believe(concept_id, "Revised understanding based on new evidence", "reason")

Track work across sessions

text

ops_append("Finished auth refactor, tests passing", project="api-v2")
ops_query(project="api-v2")  # pick up where you left off

Memory-Grounded Reviews

Review code or designs by comparing against accumulated knowledge:

1. Ground: query("the domain being reviewed") — load past decisions and patterns
2. Compare: Does the work align with or diverge from established patterns?
3. Record: observe() new patterns, wonder() about unclear choices, believe() updated understanding
4. Output:

markdown

## Review — Grounded in Memory

### Aligned with known patterns
- [matches cortex context]

### Divergences
- [what differs, intentional or accidental]

### New patterns to capture
- [novel approaches worth observing]

Session Pattern

1. Start: query() the topic you're working on
2. During: observe() facts, wonder() questions as they come up
3. End: ops_append() what you did and what's unfinished
4. Periodically: dream() to consolidate memories (compress, abstract, prune)

Available Tools

Category	Tools
Read	query, recall, predict, validate, neighbors, wander
Write	observe, wonder, speculate, believe, reflect, digest
Ops	ops_append, ops_query, ops_update
System	stats, dream