mememory

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Published: Apr 3, 2026 License: MIT

README

MEMEMORY

Persistent semantic memory for AI agents. MCP server that stores, searches, and delivers knowledge across sessions. All data stays local.

What it does

  • Stores memories with scope, type, weight, tags, and TTL
  • Searches by semantic similarity with hierarchical scope inheritance
  • Delivers accumulated rules and knowledge to agents at session start
  • Detects contradictions when new memories conflict with existing ones
  • Evolves beliefs through supersede/weight mechanisms without losing history

Quick Start

git clone https://github.com/scott-walker/mememory.git
cd mememory
cp .env.example .env
docker compose -f docker/docker-compose.yml up -d

Add to Claude Code config (~/.claude/.claude.json -> mcpServers):

{
  "memory": {
    "type": "stdio",
    "command": "docker",
    "args": ["exec", "-i", "mememory-admin", "memory-server"],
    "env": {}
  }
}

Admin UI at http://localhost:4200.

Stack

Agent ──stdio──> memory-server (Go)
                      │
              ┌───────┴───────┐
              ▼               ▼
         PostgreSQL       Ollama
       (pgvector)    (nomic-embed-text)

One docker compose up — no Go, Node.js, or other toolchains needed.

MCP Tools

Tool Description
remember Store a memory with scope, type, tags, optional TTL
recall Semantic search with hierarchical scope inheritance
forget Delete by ID
update Update content, re-embed
list List with filters
stats Count breakdown

Key Concepts

Scopes — global (everywhere), project (one project), persona (one agent role within a project). Recall searches hierarchically: persona sees global + project + own.

Types — rule, feedback, fact, decision, context. Rules and feedback load automatically at session start.

Scoringsimilarity x scope_weight x memory_weight x temporal_decay. Recent, specific, high-weight memories rank higher.

Contradiction detection — warns when a new memory is >75% similar to existing ones. Does not block storage.

Session bootstrap — all global rules and feedback are sent to the agent as MCP instructions at connection time. No config needed per project.

Documentation

License

MIT

Directories

Path Synopsis
cmd
mememory command
memory-admin command
memory-server command
internal
api
mcp

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