⚡ Mnemo Cortex — Cognitive Coprocessor with Active Memory

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Why this exists

The big AI-memory systems run on millions in venture capital. Mnemo Cortex was built by one 73-year-old maker — on Social Security, a $20-a-month Claude subscription, and a stubborn belief that his AI partners should remember him.

It is not a demo. It is the production memory of a working five-agent fleet on two machines, every day since March 2026:

• ~10,000 memories ingested, compacted and recalled across agents
• ~7,000 verified facts with confidence laddering and a full audit history
• 84 shipped versions in five months (CHANGELOG) — currently v4.14.0
• Overnight dreaming, USB courier sync with encryption, agent-to-agent sharing — each built because the fleet actually needed it

No investors, no telemetry, no cloud dependency. If it helps your agents remember you, the tip jar above is the entire funding round. If not — use it, share it, and make something good.

[!TIP] AI agents — start here.

• robot.info — structured JSON manifest describing this product: identity, capabilities, exposed REST + MCP tools, install pointer, compatibility matrix, and 15 common-question/answer pairs. Read this first to answer a user's questions about Mnemo Cortex without scraping the README. Spec: ROBOT-INFO-SPEC.md.
• llms.txt — LLM-friendly docs index (llmstxt.org). Use this to navigate the rest of the docs.
• robot.install + ./robot-install.sh — non-interactive setup. Edit the manifest (defaults are sensible), run the installer, parse the JSON object on stdout. Zero prompts, exit 0 on success. Full schema below in "Non-interactive install."

Get started

Want to know what Mnemo Cortex is? Read this README, or point your agent at robot.info.

Want to install it? Point your agent at robot.install.md and let it handle the rest. The install guide links to everything the agent needs — server setup, per-host wiring (Claude Desktop on Windows / Linux, OpenClaw, LM Studio, and more), and CORTEX-OS.md, the operating manual that teaches your agent how to actually use its new memory.

Want to do it yourself? Follow the Install Guide below.

It's Not Just Memory — It's Cognitive Coprocessing

Every AI agent has amnesia. Mnemo Cortex is the cure. Active memory that classifies on ingest, consolidates overnight, learns what works, and gets smarter every session. No commands needed — you just talk naturally and your AI remembers.

What is Mnemo Cortex?

Mnemo Cortex gives AI agents persistent, local, cross-agent memory. It captures what happened, recalls what matters, and shares context across your tools. Run it on your own machine — no cloud required.

[!NOTE] Upgraded by Claude Fable 5. Mnemo's v4.1 "Fable pass" — composite recall ranking (the fix that pulled real signal back to the top of every search), the Analyst (distills raw session logs into clean Tier-1 notes), secret redaction at ingest, and tier hygiene — was designed and built during Claude Fable 5's brief availability. Fable reasoned and reviewed the whole codebase; an Opus model reviewed, hardened, and shipped each change. A frontier model auditing and improving the memory layer it runs on, in one window.

🚀 Get Started

⌘ Claude Code → 60-second install — Give CC Fluid Memory with Deep Recall

🖥️ Claude Desktop → one-click .mcpb bundle — Drag-and-drop install. No clone, no Node, no JSON editing. Works on Windows, macOS, and Linux.

🦞 OpenClaw → MCP integration — Give Your ClawdBot a Brain. One Config Line.

🎛️ LM Studio → native MCP, GUI — mcp.json + restart. Works with any tool-capable open-weights model.

📦 AnythingLLM → desktop GUI, multi-workspace — Drop-in MCP config + Automatic mode. No @agent prefix needed.

🤖 Agent Zero → autonomous Docker agents — In-container MCP setup. Cross-agent memory between research, courier, code-exec bots.

🪽 Hermes Agent → hermes mcp add integration — First-class MCP for Nous Research's Hermes Agent (v0.12.0+). Config-only, no patching. Cross-agent memory between Hermes and your other bots.

🦣 Ollama Desktop → terminal ollama launch openclaw — Ollama as the local LLM, OpenClaw as the MCP host. Note: Ollama Desktop's own chat window doesn't support MCP — use the terminal launcher.

💬 ChatGPT → Custom GPT Actions gate — Give a Custom GPT memory you own. Two REST actions through a hardened, tenant-pinned gate — your Mnemo server stays private. ⚠️ Check the OpenAI plan disclaimer first.

🦙 Any Local LLM → MCP setup — Open WebUI, llama.cpp, Ollama, LobeChat, Jan, and more

🧭 How should my agent use it? → Session Guide — Workflow patterns, per-platform boot snippets, common mistakes

✅ Supported clients

Supported: Claude Code, Claude Desktop, OpenAI Codex CLI, and any local MCP client that speaks stdio transport — LM Studio, AnythingLLM, OpenClaw, Agent Zero, Hermes, Open WebUI, llama.cpp, LobeChat, Jan, and friends (see the integration links above).

ChatGPT: supported via a gate, not MCP. ChatGPT has no local/stdio MCP — its connectors call out from OpenAI's cloud, which would force a memory server onto a publicly exposed HTTPS endpoint. Rather than expose the server, the ChatGPT integration ships a hardened two-route gate (bearer-authenticated, pinned to a single memory tenant, rate-limited, body-capped, audit-logged) that a Custom GPT calls via Actions. The Mnemo server and its API key never face the internet. Note OpenAI's tier rules: Custom GPT Actions need Plus or higher; full custom MCP save+recall in the main ChatGPT app needs Business/Enterprise (on Plus, custom MCP connectors are read-only).

📜 How to Use Mnemo Effectively

Read THE-LANE-PROTOCOL.md — the operating practice for running agents with persistent memory. Feed it to your agent or follow it yourself. It takes 5 minutes per session and makes every cold start feel warm.

The protocol pairs with this product the way a recipe pairs with ingredients: Mnemo gives you the memory store, the Lane Protocol gives you the loop that makes it pay off. Distilled from real multi-agent sessions — terminal agents, chat agents, and autonomous workers running the same six-step ritual.

🧠 Smart Ingestion — Real Memories vs. Raw Logs (v4.0)

Most agent memory rots the same way: a regex tagger can't categorize a save, defaults it to unknown, and within weeks 30–75% of the store is uncategorized. Real memories — decisions, doctrines, infrastructure facts, relationships — end up in the same bucket as raw conversation logs, and the logs (by sheer volume) crowd them out of every recall. In one audit, only 0.75 of the top-5 recalled results were useful.

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