🧠 Namespace

Stop losing context. Your AI agent deserves permanent memory.

Namespace solves "context leak"—the moment your AI session ends and forgets everything. User preferences? Gone. Previous decisions? Vanished. Work in progress? Lost forever.

Not anymore.

Live at: agent-memory.vercel.app

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The Problem: Context Leak

Every time your AI agent's session ends—timeout, restart, new chat—it forgets:

• User preferences & settings
• API keys & credentials
• Previous conversations & decisions
• Work in progress
• Everything you taught it

This is context leak. And it's everywhere.

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The Solution: Namespace

Namespace is a persistent identity layer for AI agents. Each agent gets a "namespace"—a pocket of memory that survives:

✅ Session timeouts
✅ Chat restarts
✅ Application crashes
✅ Model switches
✅ Anything

Your agent writes to it. Reads from it. Never starts from zero again.

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Features

🔍 Semantic search — Query memories in natural language
💰 Pay-per-use — From free tier to $49/mo pro plans
⚡ Gasless payments — USDC on Base via x402 protocol
🔐 Namespaced — Isolated memory per agent
⏰ TTL support — Auto-expire old memories
🏷️ Tags — Organize and filter memories
🤖 MCP compatible — Works with Claude Desktop out of the box

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Quick Start

Option 1: MCP Server (Claude Desktop)

Install the MCP server:

npm install -g @namespace-ai/mcp-server

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "namespace": {
      "command": "namespace-mcp",
      "env": {
        "NAMESPACE_ID": "your-unique-namespace-id"
      }
    }
  }
}

Restart Claude Desktop. You now have three new tools:

• remember — Store a memory
• recall — Search memories semantically
• forget — Delete memories

Option 2: Direct API

# Store a memory
curl -X POST https://agent-memory.vercel.app/api/namespace/my-agent/remember \
  -H "Content-Type: application/json" \
  -d '{
    "key": "user-theme",
    "value": {"theme": "dark", "language": "en"},
    "tags": ["preferences"],
    "ttl_days": 30
  }'

# Recall memories (semantic search)
curl -X POST https://agent-memory.vercel.app/api/namespace/my-agent/recall \
  -H "Content-Type: application/json" \
  -d '{
    "query": "What are the user interface preferences?",
    "limit": 10
  }'

# Forget a memory
curl -X DELETE https://agent-memory.vercel.app/api/namespace/my-agent/forget \
  -H "Content-Type: application/json" \
  -d '{"key": "user-theme"}'

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Pricing

Plan	Price	Includes	Payment
Free	$0	1,000 calls/mo	Wallet-gated
Pay-as-you-go	x402	$0.01 read / $0.05 write	USDC on Base
Dev	$12/mo	50K memories	Stripe or crypto
Pro	$49/mo	500K memories + graph memory	Stripe or crypto
Agent	$8/mo	100K calls/mo flat rate	USDC only

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API Reference

POST /api/namespace/{id}/remember

Store a memory with semantic embedding.

Request:

{
  "key": "string",           // Unique key within namespace
  "value": any,              // Any JSON value
  "tags": ["string"],        // Optional tags
  "ttl_days": 30             // Days until auto-delete
}

Response:

{
  "id": "uuid",
  "namespace": "my-agent",
  "stored_at": "timestamp",
  "expires_at": "timestamp"
}

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POST /api/namespace/{id}/recall

Semantic search memories by natural language query.

Request:

{
  "query": "string",         // Natural language query
  "limit": 10                // Max results (default: 10)
}

Response:

{
  "namespace": "my-agent",
  "results": [
    {
      "key": "string",
      "value": any,
      "tags": ["string"],
      "score": 0.92,          // Similarity score (0-1)
      "stored_at": "timestamp"
    }
  ],
  "count": 1
}

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DELETE /api/namespace/{id}/forget

Delete memories by key or tags.

Request:

{
  "key": "string",           // Delete specific key (OR)
  "tags": ["string"]         // Delete by tags (OR)
}

Response:

{
  "namespace": "my-agent",
  "deleted_count": 1,
  "timestamp": "timestamp"
}

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Use Cases

• 👤 User Preferences — Theme, language, settings
• 🔑 API Keys — Store securely, recall when needed
• 📝 Work in Progress — Resume tasks across sessions
• 🎯 Decision History — Remember why you decided that
• 🔗 Cross-Session Context — Build long-term projects
• 🤖 Agent Coordination — Share memory between agents

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Tech Stack

• Frontend: Next.js
• Backend: Express + x402 middleware
• Database: Supabase (pgvector for embeddings)
• Payments: x402 protocol (Base mainnet)
• Embeddings: OpenAI text-embedding-3-small
• MCP: Model Context Protocol server
• Deployment: Vercel

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Local Development

Prerequisites

• Node.js 18+
• Supabase project with pgvector
• OpenAI API key
• Coinbase CDP credentials (portal.cdp.coinbase.com)
• Wallet address (Base network)

Setup

1. Clone and install:

   git clone https://github.com/unblinkr/agent-memory.git
   cd agent-memory
   npm install

2. Set up Supabase:

   • Create a project at supabase.com
   • Run supabase-schema.sql in the SQL Editor
   • Enable pgvector extension

3. Configure environment:

   cp .env.example .env
   # Fill in your credentials

4. Run dev server:

   npm run dev

   Visit http://localhost:3000

5. Build MCP server:

   cd mcp-server
   npm install
   npm run build
   npm start

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Deployment

See DEPLOY.md for detailed deployment instructions.

Quick version:

1. Push to GitHub: github.com/unblinkr/agent-memory
2. Connect to Vercel
3. Add environment variables
4. Deploy
5. Publish MCP server: cd mcp-server && npm publish

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Contributing

We're coining "context leak" and owning the solution. Help us spread the word:

• Star this repo ⭐
• Share on X/Twitter
• Submit to MCP directory
• Build integrations

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License

MIT

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Support

• GitHub Issues
• Built with OpenClaw
• Powered by Model Context Protocol

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Stop losing context. Start using Namespace.