Local MCP Memory System with Consolidation for AI Conversations
What This Is
A developer created a local memory system for AI conversations that consolidates and synthesizes information rather than just storing it. Built as an MCP server, it works with compatible clients like Claude Desktop and Claude Code, running 100% locally with no data leaving your hardware.
How It Works
The key differentiator from standard RAG systems is the consolidation process. Every 6 hours, a local LLM (Qwen 2.5-7B running in LM Studio) clusters recent memories by topic and consolidates them into structured knowledge documents. It extracts facts, solutions, and preferences, merging them with existing knowledge and versioning everything.
Technical Stack
- Embeddings: nomic-embed-text-v1.5 via LM Studio
- Vector search: FAISS (semantic + keyword hybrid)
- Consolidation LLM: Qwen 2.5-7B (Q4) via LM Studio
- Storage: SQLite for episodes, FAISS for vectors
- Protocol: MCP — works with anything that supports it
- Config: TOML
Features
- Semantic dedup with cosine similarity 0.95 threshold
- Adaptive surprise scoring — frequently accessed memories get boosted, stale ones decay
- Atomic writes with tempfile + os.replace for crash protection
- Tombstone-based FAISS deletion — O(1) instead of rebuilding the whole index
- Graceful degradation — if LM Studio goes down, storage still works, consolidation pauses
- 88 tests passing
MCP Tools
memory_store— save an episode with type, tags, surprise scorememory_recall— semantic search across episodes + consolidated knowledgememory_forget— mark an episode for removalmemory_correct— update a knowledge docmemory_export— full JSON backupmemory_status— health check
Why MCP Was Chosen
Models get replaced frequently, but accumulated knowledge shouldn't disappear with them. MCP makes the memory portable — one store, many interfaces. The memory layer becomes more valuable than any individual model.
Practical Results
After about a week of use, the system built knowledge documents about PC hardware, VR setup, coding preferences, and project architectures — all synthesized from normal conversation. When starting new chats, the AI already knows the user's context without re-explaining.
Requirements
- Python 3.11+
- LM Studio with Qwen 2.5-7B and nomic-embed-text-v1.5 loaded
- Any MCP client
📖 Read the full source: r/LocalLLaMA
👀 See Also
cc-lens: Local Dashboard for Claude Code Session Analysis
A developer built cc-lens, a local-first dashboard that reads Claude Code session files from ~/.claude/ and provides usage analytics, cost tracking, and session replay. It runs entirely on your machine with no cloud sync, sign-ups, or telemetry.
Framework for AI Teammates in Slack, Operated Entirely from Claude Code
ginnie-agents is an open-source framework for running autonomous AI agents with Slack identities, three-tier memory, cron routines, and work hours — all configured and operated through Claude Code. Requires Claude Code Max, Docker, Node 22+, and a Slack workspace with app creation permissions.
Hipocampus: A Persistent Memory System for AI Agents Using Compaction Trees
Hipocampus addresses the problem of AI agents forgetting context between sessions by implementing a compaction tree that compresses conversation history through five levels: raw → daily → weekly → monthly → root, with a topic index called ROOT.md.
Canopy: Terminal Dashboard for Managing Multiple Claude Code Agents
Canopy is an open source terminal UI that provides a single dashboard view for tracking multiple AI coding agents running across git worktrees. It shows agent states (running, idle, waiting for input, done, errored) and lets you jump into sessions or send input without fully switching.