Show HN: Local RAG memory system that AI can write directly to

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.

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.

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

[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.

• 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

, oroauth

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.

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/.

• 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

Setup guideIntegrations (Claude Desktop + ChatGPT)ArchitectureAIX notesDocker guideLimitationsRoadmap

MIT. See LICENSE.