# macOS/Linux (Homebrew)
brew install hurttlocker/cortex/cortex
# 或直接下载二进制文件
# https://github.com/hurttlocker/cortex/releases/latest

# 导入OpenClaw的记忆文件
cortex import ~/clawd/memory/ --extract
# 导入特定文件
cortex import ~/clawd/MEMORY.md --extract
cortex import ~/clawd/USER.md --extract

# 快速关键词搜索
cortex search "wedding venue" --limit 5
# 语义搜索（需要ollama和nomic-embed-text）
cortex search "what decisions did I make about the project" --mode semantic
# 混合搜索（推荐）
cortex search "trading strategy" --mode hybrid

# 添加到您的MCP配置 — Cortex暴露17个工具 + 4个资源
cortex mcp                    # stdio模式
cortex mcp --port 8080        # HTTP+SSE模式

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

cortex graph --serve --port 8090
# 在浏览器中打开交互式2D图谱浏览器

cortex cleanup --purge-noise  # 移除垃圾和重复
cortex stale 30               # 找出30天未访问的事实
cortex conflicts              # 检测矛盾
cortex conflicts --resolve llm # 使用LLM自动解决

memory_search → Cortex → QMD → ripgrep → web搜索

scripts/cortex.sh search "query" 5   # 混合搜索
scripts/cortex.sh stats               # 记忆健康状况
scripts/cortex.sh stale 30            # 陈旧事实检测
scripts/cortex.sh conflicts           # 矛盾检测
scripts/cortex.sh sync                # 增量导入
scripts/cortex.sh reimport            # 完全擦除+重新导入
scripts/cortex.sh compaction          # 压缩前状态简报

# 每30分钟自动导入会话 + 同步连接器
cortex connect schedule --every 30m --install

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

# 夜间试运行 + 应用（launchd或cron）
cortex lifecycle run --dry-run > /tmp/lifecycle-plan.log 2>&1
# 如果发现任何内容，应用：
cortex lifecycle run

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

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

cortex cleanup --dedup-facts    # 移除近似重复
cortex conflicts --auto-resolve # 解决矛盾

memory_search → cortex answer（综合）→ cortex search（指针）→ QMD → ripgrep → web

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

# macOS/Linux (Homebrew)
brew install hurttlocker/cortex/cortex
# Or download binary directly
# https://github.com/hurttlocker/cortex/releases/latest

2. Import Your Memory

# 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

# 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)

# 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:

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

Knowledge Graph

Explore your memory visually:

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

Self-Cleaning

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

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:

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)

# 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:

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

# 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):

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):

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:

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

Recommended OpenClaw Search Chain (Updated)

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