Stop letting Claude re-read your entire conversation history on every task.
One graph file. Instant recall. 97% fewer tokens.

⚡ Quick Install · 📊 See the Savings · 🆚 Compare Alternatives · 💡 How It Works · 💬 Community

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The Problem

Every time Claude starts a task, it re-reads everything — your entire conversation history, previous sessions, context you already explained three times. This is expensive, slow, and completely unnecessary once you've worked together before.

Without Memory Graph           With Memory Graph
──────────────────────         ─────────────────────
Re-read 50,000 tokens    vs    Read 768 tokens
Re-ask what you told it  vs    Knows your GitHub is "dixitvision"
Re-explore tools         vs    Loads all tools in 1 call
Re-discover workflows    vs    Uses the fastest pattern on file

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How It Works

Memory Graph gives Claude a persistent, connected knowledge store on disk. Instead of re-reading your conversation, Claude reads a compact subgraph — only the nodes relevant to the current task.

graph LR
    subgraph "Every Session"
        A[📄 CLAUDE.md<br/>Hot Cache<br/>~150 tokens] --> B[🧠 Claude]
        C[🗂️ graph.json<br/>Full Graph<br/>query ~200 tokens] --> B
    end

    subgraph "Task Execution"
        B --> D{Route Task}
        D -->|GitHub update| E[⚡ curl API<br/>300 tokens]
        D -->|Browser task| F[🖱️ Batched clicks<br/>2,000 tokens]
        D -->|Create file| G[📝 Direct write<br/>500 tokens]
    end

    subgraph "After Task"
        E --> H[💾 Update graph.json]
        F --> H
        G --> H
        H --> A
    end

    style A fill:#6366f1,color:#fff
    style C fill:#a855f7,color:#fff
    style B fill:#1e1b4b,color:#fff
    style H fill:#10b981,color:#fff

Loading

The 4-Step Loop

Step	Action	Token Cost
RECALL	Read CLAUDE.md hot cache → query graph.json for relevant nodes	~350 tokens
ROUTE	Match task type → load pre-built workflow template	~100 tokens
EXECUTE	Run task using cached tool patterns (no re-exploration)	~500 tokens
STORE	Write new facts/edges back to graph	~100 tokens
Total		~1,050 tokens

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Token Savings

Real measurements from actual Claude sessions:

Task: Update GitHub bio
─────────────────────────────────────────────────────────────
Without Memory Graph  ████████████████████████████████  12,000 tokens
With Memory Graph     ██                                   500 tokens
Savings: 96%

Task: Recall user context + repos
─────────────────────────────────────────────────────────────
Without Memory Graph  ████████████████████████████████   5,000 tokens
With Memory Graph     ░                                    150 tokens
Savings: 97%

Task: Browser form fill (batched)
─────────────────────────────────────────────────────────────
Without Memory Graph  ████████████████████████████████  15,000 tokens
With Memory Graph     ██████                             3,000 tokens
Savings: 80%

Task: Load all browser tools
─────────────────────────────────────────────────────────────
Without Memory Graph  ████████████████████████████████   3,000 tokens
With Memory Graph     ░░                                   300 tokens
Savings: 90%

Metric	Before	After	Reduction
Context recall	30,000 tokens	350 tokens	99%
Tool loading	3,000 tokens	300 tokens	90%
GitHub updates	12,000 tokens	500 tokens	96%
Browser task	15,000 tokens	3,000 tokens	80%
Average	~50,000 tokens	~1,050 tokens	🔥 97%

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Comparison

How Memory Graph stacks up against other solutions:

	Memory Graph	Graphiti	Mem0	claude-mem	token-reducer	Memento MCP
Zero dependencies	✅	❌ Neo4j	❌ Vector DB	✅	✅	❌ Neo4j
Works in Cowork	✅	✅	✅	❌	❌	✅
Works in Claude Code	✅	✅	✅	✅	✅	✅
Works in Claude Chat	✅	⚠️ MCP	⚠️ API	❌	❌	⚠️ MCP
Token reduction	97%	Implicit	~40%	~60%	90%+	Implicit
Graph structure	✅ JSON	✅ Neo4j	✅ Hybrid	❌ Flat	❌ Flat	✅ Neo4j
One-click install	✅ .skill	❌	❌	⚠️	⚠️	⚠️
Offline / local	✅	❌	❌	✅	✅	❌
Self-compounding	✅	✅	✅	❌	❌	✅
Workflow templates	✅	❌	❌	❌	❌	❌
No code required	✅	❌	❌	❌	❌	❌

Why Memory Graph wins for most Claude users: Every alternative either requires a database server (Graphiti, Mem0, Memento) or only works in Claude Code (claude-mem, token-reducer). Memory Graph is the only solution that works across all three Claude interfaces with zero infrastructure and a one-click install.

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Architecture

memory-graph/
├── SKILL.md                  ← Skill instructions (read by Claude)
├── references/
│   └── workflows.md          ← Pre-built patterns for each task type
│
Your project root/
├── CLAUDE.md                 ← Hot cache (top 30 facts, ~150 tokens)
└── memory/
    ├── graph.json            ← Knowledge graph (nodes + edges)
    └── workflows/            ← Task-specific patterns
        ├── github.md
        ├── browser.md
        └── documents.md

Graph Schema

{
  "nodes": {
    "user:primary": {
      "type": "user",
      "name": "Your Name",
      "github": "your-username",
      "email": "you@example.com"
    },
    "repo:my-project": {
      "type": "repo",
      "name": "my-project",
      "owner": "your-username",
      "lang": "Python",
      "topics": ["ai", "tools"]
    },
    "workflow:github-api": {
      "type": "workflow",
      "steps": ["curl -X PATCH ...", "..."],
      "token_cost": 300
    }
  },
  "edges": [
    {"from": "user:primary", "to": "repo:my-project", "rel": "owns"}
  ],
  "session_log": [
    {"date": "2026-04-09", "task": "updated profile", "status": "done"}
  ]
}

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Installation

Option 1 — One-Click (Cowork) ⚡

Download memory-graph.skill and click Install Skill in Cowork.

Option 2 — Claude Code

# Add to your project
curl -O https://raw.githubusercontent.com/dixitvision/memory-graph/main/SKILL.md

# Initialize your graph
curl -O https://raw.githubusercontent.com/dixitvision/memory-graph/main/examples/graph-example.json
cp graph-example.json memory/graph.json

Then reference SKILL.md in your Claude Code project.

Option 3 — Manual Setup

1. Copy SKILL.md into your Claude project
2. Create memory/graph.json (use examples/graph-example.json as a template)
3. Create CLAUDE.md from examples/CLAUDE-example.md
4. Tell Claude: "Use the memory-graph skill at the start of this task"

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Quick Start

Step 1 — Tell Claude about yourself once:

"My name is Alex. My GitHub is alexdev. I'm working on a Python API called 'Falcon'. 
Remember this and save it to the memory graph."

Step 2 — Claude saves to graph.json:

{
  "nodes": {
    "user:primary": {"name": "Alex", "github": "alexdev"},
    "project:falcon": {"type": "project", "lang": "Python", "name": "Falcon"}
  }
}

Step 3 — Next session, Claude reads the graph (not the conversation):

Start of task: reads graph.json → knows your name, GitHub, project
Cost: 350 tokens instead of 30,000

Step 4 — Graph grows automatically:
Every task adds new nodes and edges. The more you use it, the smarter and cheaper it gets.

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Community

💬 Share Your Knowledge

Found a better workflow pattern? Discovered a faster tool-loading sequence?
Open a Discussion and contribute it.
The best patterns get added to references/workflows.md and credited to you.

🐛 Found a Bug?

Open an issue with your graph.json structure (redact personal info) and what went wrong.

📬 Stay Updated

Star the repo to get notified of new workflow templates, graph schema updates, and performance improvements.

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Support This Project

Building and maintaining this takes time. If Memory Graph saves you tokens (and money) every day, consider supporting its development:

Or contribute knowledge instead of money:

• ⭐ Star this repo (helps others find it)
• 🔀 Submit a workflow template via PR
• 💬 Share your token-saving discoveries in Discussions
• 📝 Write about it — tag @dixitvision

Every star, PR, and discussion helps this project grow and reach more Claude users worldwide.

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Roadmap

• Core graph schema (nodes + edges)
• Hot cache (CLAUDE.md)
• Cowork .skill packaging
• GitHub API workflow template
• Browser batching workflow template
• Auto-graph initializer (interview Claude → build graph)
• Graph visualizer (render graph.json as interactive diagram)
• Multi-user graph support (team shared memory)
• MCP server version (for Claude Desktop / third-party tools)
• VS Code extension integration

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License

MIT © Dixit Patel

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Made with 🧠 by Dixit Patel
Business Analyst · Data & AI Builder · Perth, AU