Claude Code Buddy (CCB)

Add persistent project memory and smart task routing to Claude Code. Stop repeating context every session - Claude finally remembers your architecture, decisions, and patterns forever. 100% local, 17 MCP tools.

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MeMesh LLM Memory

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

npm MIT Node MCP


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.


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.

If you want to verify the install and local wiring end to end:

memesh doctor

Open the dashboard to explore your memory:

memesh

MeMesh Search — find any memory instantly

MeMesh Analytics — health score, timeline, patterns, knowledge coverage

MeMesh Graph — interactive knowledge graph with type filters and ego mode


Who Is This For?

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

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


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

MeMeshOpenMemoryCursor MemoriesMem0Zep / Graphiti
Best fitLocal memory for coding agentsLocal/cross-client MCP memoryCursor-native project memoryManaged app/agent memoryTemporal knowledge graphs
Install shapenpm install -g @pcircle/memeshLocal app/server flowBuilt into CursorCloud API / SDK / MCPService/framework setup
StorageOne local SQLite fileLocal memory stackCursor-managed rules/memoriesHosted or self-hosted stackGraph database
Cloud requiredNoNo for local modeDepends on Cursor account/settingsYes for platformUsually yes/self-hosted
Claude Code hooksFirst-classMCP toolsNoMCP toolsNot Claude Code-specific
DashboardBuilt inBuilt inCursor settingsPlatform dashboardPlatform/graph tooling
TradeoffSimple local wedge, not enterprise scaleBroader local app footprintLocked to CursorStrong managed platform, less local-firstStrong graph model, heavier setup

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


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:

WhenWhat MeMesh does
Every session startLoads your most relevant memories + proactive warnings from past lessons
Before editing filesRecalls memories tied to the file or project before Claude writes code
After every git commitRecords what you changed, with diff stats
When Claude stopsCaptures files edited, errors fixed, and auto-generates structured lessons from failures
Before context compactionSaves knowledge before it's lost to context limits

Opt out anytime: export MEMESH_AUTO_CAPTURE=false


Dashboard

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

TabWhat you see
SearchFull-text + vector similarity search across all memories
BrowsePaginated list of all entities with archive/restore
AnalyticsMemory Health Score (0-100), 30-day timeline, value metrics, knowledge coverage, cleanup suggestions, your work patterns
GraphInteractive force-directed knowledge graph with type filters, search, ego mode, recency heatmap
LessonsStructured lessons from past failures (error, root cause, fix, prevention)
ManageArchive and restore entities
SettingsLLM provider config, instant language selector

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 Sharingmemesh export > team-knowledge.json → share with your team → memesh import team-knowledge.json Imported bundles stay searchable, but MeMesh does not auto-inject imported memories into Claude hooks until you review or re-store them locally.


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


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)
SearchFTS5 keyword matching+ LLM query expansion (~97% recall)
Auto-captureRule-based patterns+ LLM extracts decisions & lessons
CompressionNot availableconsolidate compresses verbose memories
CostFree, no API key~$0.0001 per search (Haiku)

All 8 Memory Tools

ToolWhat it does
rememberStore knowledge with observations, relations, and tags
recallSmart search with multi-factor scoring and LLM query expansion
forgetSoft-archive (never deletes) or remove specific observations
consolidateLLM-powered compression of verbose memories
exportShare memories as JSON between projects or team members
importImport memories with merge strategies (skip / overwrite / append)
learnRecord structured lessons from mistakes (error, root cause, fix, prevention)
user_patternsAnalyze your work patterns — schedule, tools, strengths, learning areas

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.


Contributing

git clone https://github.com/PCIRCLE-AI/memesh-llm-memory
cd memesh-llm-memory && npm install && npm run build
npm test             # 489 tests
npm run test:e2e-dashboard

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


MIT — Made by PCIRCLE AI

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