OpenCortex: A Self-Improving Memory System for OpenClaw
OpenCortex is a self-improving memory system for OpenClaw that addresses the limitation of the flat MEMORY.md file by restructuring how the agent handles knowledge. Instead of one giant file, it routes information to where it actually belongs.
Key Features
The system organizes memory into structured files:
- Projects
- Contacts
- Workflows
- Preferences
- Runbooks
- Tools
- Infrastructure
Core Functionality
Nightly cron jobs distill the day's work into permanent knowledge. The distillation cron actively scans for:
- Uncaptured decisions
- Undocumented tools
- Missed preferences
- Orphaned sub-agent debriefs
- Instances where the agent deferred work to you that it could've handled itself
Weekly synthesis catches patterns across days and auto-creates runbooks from repeated procedures.
Technical Details
- Encrypted vault using AES-256 with system keyring preferred
- Opt-in metrics tracking with compound scoring to visualize agent improvement over time
- All sensitive features (voice profiling, infra collection, git push) off by default
- Everything is workspace-scoped with zero network calls
- Plain bash scripts you can read before running
- Benign on the ClawHub scanner
After a few weeks of running, users report the agent remembers preferences, knows the tools, and doesn't re-ask decisions, with knowledge genuinely compounding over time.
📖 Read the full source: r/openclaw
👀 See Also
Clavis MCP Server: Secure Credential Management for Claude Desktop
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Claude Session Tracker: Auto-Save Claude Code Sessions to GitHub Issues
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Godmode Plugin Adds Autonomous Iteration Loop to Claude Code and Other AI Coding Agents
Godmode is an open-source plugin that adds an autonomous measure-modify-verify loop to Claude Code, with parallel agents, failure memory, and 126 skills including optimization, security audits, and TDD. It works with Cursor, Codex, Gemini CLI, and OpenCode.
GLiGuard: Open-Source 300M Parameter Safety Moderation Model Claims 16x Speedup Over LLM Guardrails
Fastino Labs releases GLiGuard, a 300M parameter encoder-based model that performs multiple safety tasks in a single pass, matching or exceeding models 23–90x larger while running up to 16x faster.