🧠 MemoMind

Give your AI agent a brain that remembers.

A fully local, GPU-accelerated memory system for AI coding agents. Start building your digital twin's memory today — portable, evolving, and ready to migrate when a better system comes along.

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Two Kinds of AI Memory

AI memory has two audiences — the machine and the human. Most tools only address one:

| | For the AI (what it knows) | For the Human (what you can review) | |---|---|---| | Goal | AI remembers preferences, decisions, context across sessions | You browse, search, and manage conversation history | | Problem solved | "Why does it keep forgetting my coding style?" | "What did we discuss last Tuesday?" |

MemoMind handles the AI side — it gives your coding agent persistent, structured, intelligent memory. For the human side, see Recall (our companion project for conversation history management).

Use both together for the complete experience.

The Problem

You've spent thousands of hours with AI — but it remembers nothing.

Your coding AI is a stranger every morning. You spent 20 minutes explaining your architecture, your tech stack decisions, your naming conventions. Session ends. Tomorrow? "Hi, I'm Claude. How can I help you today?" From scratch. Again.
Your ChatGPT/Gemini conversations are a graveyard. Hundreds of deep discussions — career advice, research brainstorms, debugging sessions — sitting in separate silos, unsearchable, unconnected, slowly forgotten by you too.
Your daily life is invisible to AI. You tracked 5,000+ days of activities, habits, and achievements in your planner. Your AI knows none of it. It can't say "Last time you worked on this topic was 3 months ago" or "You tend to be most productive on Tuesday mornings."

The problem isn't intelligence — GPT-5, Claude, Gemini are brilliant. The problem is amnesia. Every session is a blank slate. Every AI is an isolated silo. Your years of digital life produce zero compounding value.

What if your AI could remember everything? Not just this session — but every conversation you've ever had, every decision you've made, every day you've lived?

Why Not Just Use CLAUDE.md?

Claude Code already has CLAUDE.md and MEMORY.md. But they have fundamental limitations:

| | Claude Code Built-in | MemoMind | |---|---|---| | Storage | Plain Markdown files | PostgreSQL + pgvector + knowledge graph | | Extraction | Manual — you write rules yourself | Automatic — LLM extracts facts from conversations | | Retrieval | Full file loaded into context every time (wastes tokens) | 4-way hybrid search, only relevant memories recalled | | Cross-session | Static rules; append-only notes | Dynamic knowledge graph with entity linking + temporal relationships | | Reasoning | No — just loads text | reflect synthesizes insights across all memories | | Scalability | Breaks down at ~200 lines (context bloat) | Handles thousands of memories efficiently |

They're complementary, not competing. CLAUDE.md is great for static project rules ("use tabs, not spaces"). MemoMind handles the dynamic knowledge that accumulates over time ("user tried Redis caching last week but switched to Memcached due to memory constraints").

The Solution

MemoMind gives your AI a persistent, local, intelligent brain. Not a chat log — a living knowledge graph that grows with every interaction, every imported conversation, every day of your life.

| | Without MemoMind | With MemoMind | |---|---|---| | Session start | Blank slate, zero context | Recalls your preferences, past decisions, project context | | 500 ChatGPT conversations | Scattered across browser tabs, unsearchable | Unified knowledge graph, every fact extracted and linked | | 3 years of daily activities | Trapped in your planner app | Searchable timeline — AI knows your patterns and history | | Cross-AI knowledge | ChatGPT doesn't know what you told Gemini | All conversations merged into one memory | | Decision tracking | Lost when chat window closes | Stored as structured facts with source tracing | | Cross-session reasoning | Impossible | reflect synthesizes insights across all memories | | Privacy | Cloud-based, fragmented | 100% local — nothing leaves your machine |

You: "Let's use FastAPI instead of Express for this project"

Claude Code internally:
  → retain("Project migrating from Express to FastAPI")  # auto-stores

Next week, new session:
  → recall("project tech stack")                          # auto-retrieves
  → "Based on your previous decision, I'll use FastAPI..."

You don't do anything — the AI handles it all.

📊 Real Numbers from Production Use

| Metric | Value | |--------|-------| | Memory nodes | 8,400+ | | Knowledge links | 556,000+ | | Named entities | 4,600+ | | Time span | 2017 – present (9 years) | | AI chats imported | 541 (ChatGPT + Gemini) | | Life events imported | 5,490 (2,400+ days) | | Database size | ~500 MB | | Keyword search | 20–33ms | | Semantic recall | 235–430ms | | Daily LLM cost | < $0.01 |

🎬 Dashboard

<div align="center"> <img src="docs/demos/dashboard-overview.png" width="800" alt="MemoMind Dashboard — memory stream with metrics, filters, and search"/> </div> <table> <tr> <td align="center"><b>Knowledge Graph</b></td> <td align="center"><b>Timeline View</b></td> </tr> <tr> <td><img src="docs/demos/graph-view.png" width="400" alt="Entity relationship graph with hover tooltips"/></td> <td><img src="docs/demos/timeline-view.png" width="400" alt="Memories organized by date"/></td> </tr> <tr> <td align="center"><b>Type Filters</b></td> <td align="center"><b>Add Memory</b></td> </tr> <tr> <td><img src="docs/demos/filter-observation.png" width="400" alt="Filter by observation type"/></td> <td><img src="docs/demos/add-memory.png" width="400" alt="Manual memory creation modal"/></td> </tr> </table>

💬 Import & Trace Your AI Conversations

One-click import your ChatGPT and Gemini conversation history into the knowledge graph — then trace any memory back to the original conversation.

Export your conversations using our companion tools, then import them into MemoMind. Every extracted memory links back to its source — click the 💬 icon on any memory card to view the full original conversation.

<table> <tr> <td align="center"><b>AI Memory Timeline (2,000+ memories)</b></td> <td align="center"><b>Original Conversation Tracing</b></td> </tr> <tr> <td><img src="docs/demos/ai-chat-timeline.png" width="400" alt="Timeline view of AI conversation memories across months"/></td> <td><img src="docs/demos/original-chat-modal.png" width="400" alt="Click 💬 to view the original ChatGPT/Gemini conversation"/></td> </tr> </table>

Companion tools for conversation export:

chatgpt-exporter — One-click export all ChatGPT conversations (including Projects/folders) via browser console
gemini-exporter — Export all Google Gemini conversations via Chrome Extension using internal batchexecute API

📅 Visualize Your Life with DayLife

Import your daily activities from DayLife — every event becomes a searchable, AI-analyzable memory. Smart daily sync catches up automatically even if your computer was off for days.

<table> <tr> <td align="center"><b>Life Timeline in MemoMind (6,000+ events)</b></td> <td align="center"><b>DayLife App — Your Daily Planner</b></td> </tr> <tr> <td><img src="docs/demos/daylife-timeline.png" width="400" alt="Years of daily activities visualized as a searchable timeline"/></td> <td><img src="docs/demos/daylife-app.png" width="400" alt="DayLife calendar view with daily activities and categories"/></td> </tr> </table>

Import once with import_daylife.py, then the daily sync keeps it updated forever. Combined with DayLife's CSV import feature, you can one-click visualize your entire life history — every plan, every achievement, every habit pattern — all searchable by AI.

How It Compares

| Feature | MemoMind | Mem0 | Graphiti/Zep | Letta | Cognee | Hindsight | |---------|----------|------|--------------|-------|--------|-----------| | GitHub Stars | — | 51K | 24K / 4K | 22K | 15K | 7K | | Funding | Self-funded | $24M (YC) | — | $10M | $7.5M | $3.5M | | Architecture | KG + pgvector | Vector + Graph | Temporal KG | Agent OS | ECL + KG | 4-network | | Retrieval | 4-way hybrid | Semantic + graph | Sem+BM25+graph | Agent-driven | 14 modes | 4 parallel | | Knowledge Graph | Built-in (pgvector) | Pro only ($249/mo) | Core (Neo4j) | No | Yes | Yes | | Temporal | Native | No | Bi-temporal | No | Partial | Yes | | Privacy | 100% local | Cloud default | Cloud/BYOC | Self-host opt | Local/Cloud | Local | | GPU Accel | Local CUDA | No | No | No | No | No | | LongMemEval | — | 49% | — | — | — | 91.4% | | Cost | $0.30/mo | Free–$249/mo | Free–$475/mo | Free–$200/mo | Free–$200/mo | Free (OSS) |

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