GitHub - ptobey/local-memory-mcp: Persistent local memory for Claude and ChatGPT. No cloud, no subscription. Self-hosted RAG over MCP.

AI assistants forget everything when the conversation ends. This fixes that - locally.

No cloud. No subscription. No account. Your data stays on your machine.

local-memory-mcp gives Claude, ChatGPT, and other MCP-compatible assistants a persistent memory layer powered by local vector search (ChromaDB). Tell it something once. It remembers across sessions.

The problem it solves

Every new Claude or ChatGPT session starts blank. Your preferences, your project context, your decisions - gone. You re-explain yourself constantly.

local-memory-mcp is a local MCP server that lets your AI assistant:

Store things worth remembering ("my deep work block is 6:30–9 AM")
Retrieve relevant context at the start of any new session
Version and supersede memories as your situation changes
Never send your data anywhere

It's the memory layer AI assistants should have built in, but don't.

Quickstart (Docker - 2 minutes)

git clone https://github.com/ptobey/local-memory-mcp.git
cd local-memory-mcp
docker compose up --build -d

Then point your MCP client at http://localhost:8000/mcp. Done.

→ Claude Desktop setup · ChatGPT setup · Manual Python install

How it works

[Assistant via MCP Client]
            |
            v
[run_mcp_v1_stdio.py | run_mcp_v1_http_sse.py]
            |
            v
      [src/mcp_server_v1.py]
        /          |          \
       v           v           v
[vector_store.py] [reconciliation.py] [health_monitor.py]
       |                   |
       v                   v
 [Local ChromaDB]   [Reconciliation Log]

Write path: store/update writes a chunk → reconciliation checks for overlap/conflict → returns warnings and self-heal hints when a write looks risky.

Read path: search runs semantic retrieval → ranking blends similarity with lightweight lexical/recency signals → deprecated chunks stay hidden unless explicitly requested.

Features

MCP tools: store, search, update, delete, get_chunk, get_evolution_chain
Versioned updates (strategy="version") with supersedes chains
Soft delete by default (history retained), optional hard delete
Heuristic reconciliation and conflict logging
Warning-first write responses with structured warnings[] and self-heal fields
Health checks for oversized chunks and unresolved conflicts
Local backup/restore for the persisted vector DB
Stdio and SSE transports
Optional SSE auth: none (local-only), bearer, or oauth

The design idea behind it (AIX)

AIX (AI eXperience) means designing for how LLMs actually consume context, not how humans file documents:

Prefer clear text chunks over rigid document schemas
Keep metadata minimal but useful: timestamps, confidence, supersedes links, deprecation flags
Preserve history with version chains instead of destructive overwrites
Return warning-rich tool responses so the model can self-correct

The goal is practical retrieval quality and reliable AI behavior, not perfect human taxonomies.

Example workflow

Store a memory:

tool: store
input: { "text": "Weekday focus block is 6:30-9:00 AM, current default schedule." }

Retrieve it later:

tool: search
input: { "query": "current deep work schedule", "top_k": 5 }

Bootstrap a new session by running a few focused retrievals, then synthesizing only active, non-deprecated chunks into a short brief for the new model instance. More flows in examples/.

Privacy & deployment

Local-first and user-controlled by default
Data stored in local ChromaDB files under the configured persist directory
No cloud backend required; optional remote access via user-managed tunneling
Never commit real secrets - use local config/env values

Documentation

License

MIT. See LICENSE.

推荐订阅源

Show HN