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MeMesh LLM Memory

Local memory for Claude Code and MCP coding agents.
One SQLite file. No Docker. No cloud required.

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The Problem

Your coding agent forgets what happened between sessions. Every architecture decision, bug fix, failed test, and hard-won lesson has to be re-explained. Claude Code starts fresh, re-discovers old constraints, and burns context on things it should already know.

MeMesh gives coding agents persistent, searchable, evolving local memory.

This package is the local memory layer of the MeMesh product family. It is intentionally small and open-source: install it with npm, keep your memory in ~/.memesh/knowledge-graph.db, and connect it to Claude Code or any MCP-compatible client. Hosted workspace and enterprise operating-system products should stay separate from this package's README and roadmap.

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Get Started in 60 Seconds

Step 1: Install

npm install -g @pcircle/memesh

Step 2: Store a decision

memesh remember --name "auth-decision" --type "decision" --obs "Use OAuth 2.0 with PKCE"

Step 3: Recall it later

memesh recall "login security"
# → Finds "OAuth 2.0 with PKCE" even though you searched different words

That's it. MeMesh is now remembering and recalling across sessions.

Open the dashboard to explore your memory:

memesh

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Who Is This For?

If you are...	MeMesh helps you...
A developer using Claude Code	Auto-recall project decisions, file-specific lessons, and past failures as you work
A coding-agent power user	Share one local memory layer across MCP-compatible tools
A team experimenting with AI coding workflows	Export/import project knowledge without introducing hosted infrastructure
An agent developer	Add local memory through MCP, HTTP, CLI, or the Python SDK

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Designed For Coding Agents First

Claude Code / Desktop memesh-mcp MCP tools + Claude Code hooks

Any HTTP Client curl localhost:3737/v1/recall \ -H "Content-Type: application/json" \ -d '{"query":"auth"}' memesh serve (REST API)

Any LLM (OpenAI format) memesh export-schema \ --format openai Paste tools into any API call

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Why Not OpenMemory, Cursor Memories, Mem0, Or Zep?

	MeMesh	OpenMemory	Cursor Memories	Mem0	Zep / Graphiti
Best fit	Local memory for coding agents	Local/cross-client MCP memory	Cursor-native project memory	Managed app/agent memory	Temporal knowledge graphs
Install shape	npm install -g @pcircle/memesh	Local app/server flow	Built into Cursor	Cloud API / SDK / MCP	Service/framework setup
Storage	One local SQLite file	Local memory stack	Cursor-managed rules/memories	Hosted or self-hosted stack	Graph database
Cloud required	No	No for local mode	Depends on Cursor account/settings	Yes for platform	Usually yes/self-hosted
Claude Code hooks	First-class	MCP tools	No	MCP tools	Not Claude Code-specific
Dashboard	Built in	Built in	Cursor settings	Platform dashboard	Platform/graph tooling
Tradeoff	Simple local wedge, not enterprise scale	Broader local app footprint	Locked to Cursor	Strong managed platform, less local-first	Strong graph model, heavier setup

MeMesh trades enterprise-scale managed infrastructure for instant local setup, inspectable storage, and coding-agent workflow hooks.

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What Happens Automatically In Claude Code

You don't need to manually remember everything. MeMesh has 5 hooks that capture and inject knowledge while you work:

When	What MeMesh does
Every session start	Loads your most relevant memories + proactive warnings from past lessons
Before editing files	Recalls memories tied to the file or project before Claude writes code
After every git commit	Records what you changed, with diff stats
When Claude stops	Captures files edited, errors fixed, and auto-generates structured lessons from failures
Before context compaction	Saves knowledge before it's lost to context limits

Opt out anytime: export MEMESH_AUTO_CAPTURE=false

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Dashboard

7 tabs, 11 languages, zero external dependencies. Access at http://localhost:3737/dashboard when the server is running.

Tab	What you see
Search	Full-text + vector similarity search across all memories
Browse	Paginated list of all entities with archive/restore
Analytics	Memory Health Score (0-100), 30-day timeline, value metrics, knowledge coverage, cleanup suggestions, your work patterns
Graph	Interactive force-directed knowledge graph with type filters, search, ego mode, recency heatmap
Lessons	Structured lessons from past failures (error, root cause, fix, prevention)
Manage	Archive and restore entities
Settings	LLM provider config, language selector

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Smart Features

🧠 Smart Search — Search "login security" and find memories about "OAuth PKCE". MeMesh expands queries with related terms using your configured LLM.

📊 Scored Ranking — Results ranked by relevance (30%) + recency (25%) + frequency (15%) + confidence (15%) + recall impact (10%) + temporal validity (5%).

🔄 Knowledge Evolution — Decisions change. forget archives old memories (never deletes). supersedes relations link old → new. Your AI always sees the latest version.

⚠️ Conflict Detection — If you have two memories that contradict each other, MeMesh warns you.

📦 Team Sharing — memesh export > team-knowledge.json → share with your team → memesh import team-knowledge.json

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Example Usage

"MeMesh remembered that we chose PKCE over implicit flow three weeks ago. When I asked Claude about auth again, it already knew — no re-explaining needed." — Solo developer, building a SaaS

"We export our team's memory every Friday and import it Monday. Everyone's Claude starts the week knowing what the team learned last week." — 3-person startup, shared knowledge base

"The dashboard showed me that 90% of my memories were auto-generated session logs. I started using remember deliberately for architecture decisions. Game changer." — Developer who discovered the Analytics tab

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Unlock Smart Mode (Optional)

MeMesh works offline by default. Add an LLM API key only if you want query expansion, smarter extraction, and compression:

memesh config set llm.provider anthropic
memesh config set llm.api-key sk-ant-...

Or use the dashboard Settings tab (visual setup):

memesh  # opens dashboard → Settings tab

	Level 0 (default)	Level 1 (Smart Mode)
Search	FTS5 keyword matching	+ LLM query expansion (~97% recall)
Auto-capture	Rule-based patterns	+ LLM extracts decisions & lessons
Compression	Not available	consolidate compresses verbose memories
Cost	Free, no API key	~$0.0001 per search (Haiku)

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All 8 Memory Tools

Tool	What it does
remember	Store knowledge with observations, relations, and tags
recall	Smart search with multi-factor scoring and LLM query expansion
forget	Soft-archive (never deletes) or remove specific observations
consolidate	LLM-powered compression of verbose memories
export	Share memories as JSON between projects or team members
import	Import memories with merge strategies (skip / overwrite / append)
learn	Record structured lessons from mistakes (error, root cause, fix, prevention)
user_patterns	Analyze your work patterns — schedule, tools, strengths, learning areas

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Architecture

                    ┌─────────────────┐
                    │   Core Engine   │
                    │  (8 operations) │
                    └────────┬────────┘
           ┌─────────────────┼─────────────────┐
           │                 │                 │
     CLI (memesh)    HTTP API (serve)    MCP (memesh-mcp)
           │                 │                 │
           └─────────────────┼─────────────────┘
                             │
                    SQLite + FTS5 + sqlite-vec
                    (~/.memesh/knowledge-graph.db)

Core is framework-agnostic. Same logic runs from terminal, HTTP, or MCP.

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Contributing

git clone https://github.com/PCIRCLE-AI/memesh-llm-memory
cd memesh-llm-memory && npm install && npm run build
npm test             # 452 tests

Dashboard: cd dashboard && npm install && npm run dev

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MIT — Made by PCIRCLE AI