Cortex

Local-first agent memory with Ebbinghaus decay, hybrid search, and MCP tools. Import files, extract facts, search with BM25 + semantic, track confidence over time. Zero dependencies, single Go binary, SQLite storage. Use when you need persistent memory beyond OpenClaw's built-in MEMORY.md — especially for multi-agent setups, large knowledge bases, or when compaction keeps losing your important context. Don't use for: conversation history (use memory_search), exact string matching (use ripgrep), or web lookups.

Cortex — Local-First Agent Memory

The memory layer OpenClaw should have built in.

Cortex is an open-source, import-first memory system for AI agents. Single Go binary, SQLite storage, zero cloud dependencies. It solves the #1 complaint about OpenClaw: agents forget everything after compaction.

GitHub: https://github.com/hurttlocker/cortex Install: brew install hurttlocker/cortex/cortex or download from Releases

Why Cortex?

OpenClaw's default memory is Markdown files. When context fills up, compaction summarizes and destroys specifics. Cortex fixes this:

Problem	Cortex Solution
Compaction loses details	Persistent SQLite DB survives any session
No search — just dump files into context	Hybrid BM25 + semantic search (~16ms keyword, ~52ms semantic)
Everything has equal weight	Ebbinghaus decay — important facts stay, noise fades naturally
Can't import existing files	Import-first: Markdown, text, any file. 8 connectors (GitHub, Gmail, Calendar, Drive, Slack, Notion, Discord, Telegram)
Multi-agent memory leaks	Per-agent scoping built in
Expensive cloud memory services	$0/month. Forever. Local SQLite.

Quick Start

1. Install Cortex

bash

# macOS/Linux (Homebrew)
brew install hurttlocker/cortex/cortex

# Or download binary directly
# https://github.com/hurttlocker/cortex/releases/latest

2. Import Your Memory

bash

# Import OpenClaw's memory files
cortex import ~/clawd/memory/ --extract

# Import specific files
cortex import ~/clawd/MEMORY.md --extract
cortex import ~/clawd/USER.md --extract

3. Search

bash

# Fast keyword search
cortex search "wedding venue" --limit 5

# Semantic search (requires ollama with nomic-embed-text)
cortex search "what decisions did I make about the project" --mode semantic

# Hybrid (recommended)
cortex search "trading strategy" --mode hybrid

4. Use as MCP Server (recommended for OpenClaw)

bash

# Add to your MCP config — Cortex exposes 17 tools + 4 resources
cortex mcp              # stdio mode
cortex mcp --port 8080  # HTTP+SSE mode

Key Features

Ebbinghaus Forgetting Curve

Facts decay at different rates based on type. Identity facts (names, roles) last ~2 years. Temporal facts (events, dates) fade in ~1 week. State facts (status, mood) fade in ~2 weeks. This means search results naturally prioritize what matters — without manual curation.

Hybrid Search

• BM25 — instant keyword matching via SQLite FTS5 (~16ms)
• Semantic — meaning-based via local embeddings (~52ms)
• Hybrid — combines both with reciprocal rank fusion

Fact Extraction

Every imported file gets facts extracted automatically:

• Rule-based extraction (zero cost, instant)
• Optional LLM enrichment (Grok, Gemini, or any provider — finds facts rules miss)
• Auto-classification into 9 types: identity, relationship, preference, decision, temporal, location, state, config, kv

Connectors (Beta)

Pull memory from external sources:

bash

cortex connect sync --provider github --extract
cortex connect sync --provider gmail --extract
cortex connect sync --all --extract

Knowledge Graph

Explore your memory visually:

bash

cortex graph --serve --port 8090
# Opens interactive 2D graph explorer in browser

Self-Cleaning

bash

cortex cleanup --purge-noise  # Remove garbage + duplicates
cortex stale 30               # Find facts not accessed in 30 days
cortex conflicts               # Detect contradictions
cortex conflicts --resolve llm # Auto-resolve with LLM

Integration with OpenClaw

Recommended Search Chain

text

memory_search → Cortex → QMD → ripgrep → web search

Use OpenClaw's built-in memory_search for conversation history, then Cortex for deep knowledge retrieval.

Wrapper Script

The included scripts/cortex.sh provides shortcuts:

bash

scripts/cortex.sh search "query" 5       # Hybrid search
scripts/cortex.sh stats                    # Memory health
scripts/cortex.sh stale 30                # Stale fact detection
scripts/cortex.sh conflicts               # Contradiction detection
scripts/cortex.sh sync                    # Incremental import
scripts/cortex.sh reimport                # Full wipe + re-import
scripts/cortex.sh compaction              # Pre-compaction state brief

Automated Sync (launchd/systemd)

bash

# Auto-import sessions + sync connectors every 30 min
cortex connect schedule --every 30m --install

Architecture

• Language: Go (62,300+ lines, 1,081 tests)
• Storage: SQLite + FTS5 + WAL mode
• Binary: 19MB, pure Go, zero CGO, zero runtime dependencies
• Platforms: macOS (arm64/amd64), Linux (arm64/amd64), Windows (amd64)
• MCP: 17 tools + 4 resources (stdio or HTTP+SSE)
• Embeddings: Local via Ollama (nomic-embed-text), or OpenAI/DeepSeek/custom
• LLM: Optional enrichment via any provider (Grok, Gemini, DeepSeek, OpenRouter)
• Scale: Tested to 100K+ memories. At ~20-50/day, won't hit ceiling for 5+ years.
• License: MIT

vs Other Memory Tools

	Cortex	Mem0	Zep	LangMem
Deploy	Single binary	Cloud or K8s	Cloud	Python lib
Cost	$0	$19-249/mo	$25/mo+	Infra costs
Privacy	100% local	Cloud by default	Cloud	Depends
Decay	Ebbinghaus (7 rates)	TTL only	Temporal	None
Import	Files + 8 connectors	Chat extraction	Chat/docs	Chat extraction
Search	BM25 + semantic	Vector + graph	Temporal KG	JSON docs
MCP	17 tools native	No	No	No
Dependencies	Zero	Python + cloud	Cloud + credits	Python + LangGraph

Requirements

• Cortex binary — install via Homebrew or download from GitHub Releases
• Optional: Ollama with nomic-embed-text for semantic search
• Optional: LLM API key for enrichment (Grok, Gemini, etc.)
• No Python. No Node. No Docker. No cloud account. Just the binary.

v1.1/v1.2 Integration Guide (Wiring for OpenClaw Agents)

When to use cortex answer vs cortex search

• answer — "What do I know about X?" / "Who is Y?" / synthesis questions → single coherent response with citations
• search — "Find the file where X is mentioned" / debugging / exploring what exists → ranked result list

Source Boost (config.yaml)

Add to ~/.cortex/config.yaml:

yaml

search:
  source_boost:
    - prefix: "memory/"
      weight: 1.5
    - prefix: "file:MEMORY"
      weight: 1.6
    - prefix: "github"
      weight: 1.3
    - prefix: "session:"
      weight: 0.9

Higher weight = more trusted. Daily notes and core files rank above auto-imported sessions.

Search Intent

Use --intent when you know where the answer lives:

• --intent memory — personal decisions, preferences, people
• --intent connector — code, PRs, emails, external data
• --intent import — imported files and documents
• No flag = search everything (default, good for discovery)

Lifecycle Runner Schedule

bash

# Nightly dry-run + apply (launchd or cron)
cortex lifecycle run --dry-run > /tmp/lifecycle-plan.log 2>&1
# If anything found, apply:
cortex lifecycle run

Recommended: 3:30 AM daily. First week: dry-run only, review logs.

Policy Presets

Fresh agent (< 500 facts):

yaml

policies:
  reinforce_promote:
    min_reinforcements: 3
    min_sources: 2
  decay_retire:
    inactive_days: 90
    confidence_below: 0.25
  conflict_supersede:
    min_confidence_delta: 0.20

Mature agent (2000+ facts):

yaml

policies:
  reinforce_promote:
    min_reinforcements: 5
    min_sources: 3
  decay_retire:
    inactive_days: 45
    confidence_below: 0.35
  conflict_supersede:
    min_confidence_delta: 0.10

Post-Import Hygiene

After any bulk import, run:

bash

cortex cleanup --dedup-facts    # Remove near-duplicates
cortex conflicts --auto-resolve  # Resolve contradictions

Recommended OpenClaw Search Chain (Updated)

text

memory_search → cortex answer (synthesis) → cortex search (pointers) → QMD → ripgrep → web