v0.1.0 ยท Context Engineering ยท Open Source ยท MCP-Native

The Soul of your AI.

A local, portable, and universal ๐Ÿง  for your AI

MEMM is an AI-native app that turns your knowledge into a second brain for you and your AI. Stop re-explaining context and burning tokens. Build a permanent memory that captures your reasoning, improves results, and grows over time.
Built on files. Owned by you.

Also available for Windows and Linux  ยท  View on GitHub โ†—

+43%
Context Accuracy

Better retrieval precision over any vector-only RAG model. Consistent, reliable results.

87%
Token Savings

Stop burning money on noise. Inject only high-signal reasoning that actually matters.

<10ms
Query Latency

Pure Rust scoring engine. Sub-millisecond contextual awareness at any scale.

Your AI has amnesia.

Context engineering is still manual. Three problems every engineer knows too well.

01

The blank slate.

Every conversation starts from scratch.
You've explained your error handling conventions a hundred times. ChatGPT, Claude. None of them retained any of it.

02

The bloated context file.

CLAUDE.md grows until you stop trusting it.
No structure. No scoring. No way to know what the AI actually reads. A 2,000-word file injected wholesale into every query.

03

The silo tax.

Your ChatGPT has no idea what your Cursor knows.
Every tool has its own memory. You maintain three diverging copies of the same knowledge by hand, forever.

Give your AI a memory that
grows and refines over time.

01

Your memory, in plain text.

Memories are Markdown files with YAML frontmatter. Open them, edit them, version with Git, share with your team. No database. No embeddings. No black box.

---
id: error-handling-conv
type: concept
importance: 0.9
tags: [rust, errors, conventions]
related: [api-design, rust-patterns]
---
## Error Handling Convention

Always use Result<T, AppError>.
Propagate with `?` operator.
Never panic in library code.
02

Scored, tiered, token-aware.

A 6-signal engine (BM25 ยท semantic ยท graph ยท recency ยท importance ยท frequency) ranks every memory per query. L0 / L1 / L2 tiers load exactly the right level of detail. No over-stuffing. No blind spots.

Query: "How do we handle errors?"
error-handling.md
1.94
rust-patterns.md
1.23
api-design.md
0.41
BM25 ยท Semantic ยท Graph ยท Recency ยท Importance ยท Access
03

Connect once, works everywhere.

One MCP server exposes your memory to every tool simultaneously. A single ground truth. No duplication. No diverging copies of the same knowledge.

ChatGPT Claude Cursor Codex Local LLMs
MEMM OS MCP Server ยท 127.0.0.1
04

Memory that compounds, not rots.

A built-in governance layer surfaces stale memories, contradictions, redundancy, and debris. A 0โ€“100 health score tracks quality. It gets better with every session, automatically.

87 Health
โš  3 decayed memories
โšก 1 conflict detected
โœ“ 2 consolidation hints

Set up your own mini Palantir ๐Ÿ”ฎ

We bring the magic of enterprise ontologies to your personal AI. Like a mini Palantir, but local, open, and yours. Every memory is semantically typed so your AI reasons over structured knowledge, not just flat text.

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Entity

Real-world objects, people, APIs, databases, services. The nouns your project talks about.

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Concept

Abstract ideas, patterns, conventions, architectures. The principles that guide decisions.

โ–ฃ

Source

External references, docs, papers, specifications. Protected ground truth you can cite.

โ—‰

Synthesis

Distilled conclusions, decisions, post-mortems. The output of reasoning, ready to reuse.

Same query, different ontology, different behavior. The engine understands that a Synthesis is distilled wisdom, a Source is ground truth, and an Entity is a noun in your domain. Unlike flat tags, the typed ontology changes how memories are scored, decayed, and retrieved.

patterns conventions architecture โ—‡ CONCEPT error-patterns.md auth-service user model API gateway โ—ˆ ENTITY auth-service.md oauth2-spec RFC 6749 examples docs โ–ฃ SOURCE oauth2-spec.md decision postmortem conclusion summary โ—‰ SYNTHESIS auth-decision.md
QUERY

Your AI doesn't just search. It reasons over a structured knowledge model.

Born for the AI Era.

Legacy tools solve human amnesia. MEMM is built to solve AI amnesia.

Legacy Adapters (e.g. Obsidian)

A different epoch.

Human-first note-taking adapted through a patchwork of 1000+ plugins. Convoluted, fragile, and high cognitive load. Built for a problem that didn't exist when they were created.

  • +1000 plugins to duct-tape AI
  • Manual link maintenance
  • No context scoring or governance
  • Amnesic by default
  • Doesn't learn between sessions
AI-Native (MEMM)

Integrated from day one.

A context engine built from line zero for the LLM era. No plugins. A direct, high-fidelity bridge between your knowledge and every AI tool you use.

  • Zero-config intelligence
  • Automated knowledge scoring
  • Built-in governance & health
  • Native MCP: ChatGPT, Claude, Cursor
  • Grows & compounds with every session

From files to compound intelligence.

Three steps to a memory worth trusting.

01

Build your workspace.

Create your first memories: who you are, your stack, your conventions, your architectural decisions. Drop external documents into inbox/ and promote them to the right category.

โ†’
02

Connect your tools.

Point ChatGPT, Claude, or Cursor at your MCP server. They call get_context with every query and receive the most relevant memories, scored and budgeted automatically.

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03

Let it compound.

Every session adds knowledge. The scoring engine learns what you use. Governance surfaces what needs updating. Your AI gets smarter with your project, permanently.

The Technical Matrix.

Compare the architecture of MEMM OS against traditional vector systems, legacy tools, and manual approaches.

Capability MEMM OS Vector RAG Manual (CLAUDE.md) Legacy (Obsidian)
You can read the memory โœ“ โœ— โœ“ โœ“
Inspectable retrieval Yes (Transparent) No (Blackbox) Manual only No
See what AI will retrieve โœ“ (Simulation view) โœ— N/A N/A
Ontology โœ“ Native โœ— Flat vectors โœ— โœ— Tags only
Real-time token budgeting Yes (L0/L1/L2) Static Window None None
Works across all tools โœ“ (Native MCP) โœ— Siloed Partial โœ— Plugins
Local model support Native / Hybrid Limited / API High Via Plugins
No database infrastructure โœ“ Zero infra โœ— Vector DB โœ“ Local App
Context health & decay โœ“ Automated Static DB Rotting Human only
Scales with complexity โœ“ โœ“ โœ— Partial
Built-in governance โœ“ โœ— โœ— โœ—
Compounds over time โœ“ Automatic โœ— Manual Manual

Built for Engineers.

What's under the hood before you download anything.

โš™ Tauri v2 & Rust โšก Sub-10ms Queries ๐Ÿ”Œ Dual MCP Server ๐Ÿ“ Local Markdown
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Your Personal Knowledge Ontology

Inspired by the same ontology-driven approach that powers enterprise intelligence platforms, but rebuilt for individuals. Every memory is typed as Entity, Concept, Source, or Synthesis, giving your AI a structured world model instead of a bag of words.

โฌก

Fair Source โ†’ Open Source

Source code available on GitHub to download and audit. Built under the FSL license with automatic consolidation to Open Source (Apache 2.0). Human-readable, documented formats. Your memory is yours forever. No vendor lock-in.

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Privacy by Design

Nothing leaves your machine. No analytics, no telemetry, no cloud sync. The MCP server binds to 127.0.0.1 only. Your knowledge stays yours, permanently.

Start with what you know. Files.

MEMM is free, open source, and turns your knowledge into a compound second brain. Stop re-explaining and start building with an AI that finally remembers.

Read the academic paper ยท Read the technical whitepaper ยท View on GitHub