Cortex: A Local Memory Layer for OpenClaw Agents with Ebbinghaus Decay

Cortex is an open-source memory tool designed specifically for OpenClaw agents to address context compaction problems that destroy critical information like account numbers, strategy parameters, and past decisions.

Core Features

The developer built Cortex after running 3 OpenClaw agents 24/7 on a Mac for 2 months and experiencing repeated context loss during compaction. Key differentiators from simply using a vector database include:

• Ebbinghaus forgetting curve implementation: Facts decay at different rates based on type. Identity information (like a name) persists for ~2 years, status updates fade in ~2 weeks, and dates fade in ~1 week. This naturally prioritizes important information without manual curation.
• Import-first architecture: Unlike most memory tools that extract from chat, Cortex starts from files - importing memory/, MEMORY.md, notes, etc. It extracts facts, classifies them, and makes them searchable. Conversations can be imported too, but files form the foundation.
• Single Go binary with zero dependencies: 19MB binary using SQLite, no Python, Docker, cloud services, or API keys required. Runs on a Raspberry Pi. Installation: brew install hurttlocker/cortex/cortex or clawhub install hurttlocker-cortex.

Technical Capabilities

• Hybrid search: BM25 keyword (~16ms) + semantic (~52ms) + fusion
• Extracts facts from imported content using rule-based methods with optional LLM enrichment
• 17 MCP tools for native agent integration (search/import/manage memory)
• Connectors for GitHub, Gmail, Calendar, Drive, Slack, Notion, Discord, Telegram
• Self-cleaning: finds stale facts, detects contradictions, auto-resolves conflicts
• Knowledge graph explorer at localhost:8090

The developer reports running Cortex with ~3,200 memories and ~6,500 extracted facts, with instant search performance. Agents use it via MCP alongside OpenClaw's built-in memory_search - Cortex handles deep knowledge retrieval while memory_search manages conversation history.

The Ebbinghaus decay feature proved particularly valuable, solving the problem where all information remained equally relevant forever, creating noisy search results. Now temporary information naturally fades while structural facts remain prominent.