memedge

Memedge 🧠

Advanced memory management system for LLM agents with Letta-inspired features

Memedge is a sophisticated memory system designed for building stateful LLM agents on Cloudflare Workers. Inspired by Letta (formerly MemGPT), it provides structured memory blocks, semantic search, recursive summarization, and privacy-aware memory management.

✨ Features

• 🎯 Structured Memory Blocks: Organize information into core blocks (human, persona, context) and custom blocks
• 🔍 Semantic Search: Built-in semantic search using Cloudflare AI embeddings (no external vector DB needed!)
• 📚 Archival Memory: Long-term storage with searchable history
• 🔄 Recursive Summarization: Hierarchical conversation summarization for managing long-term context
• 🔒 Privacy-Aware: Built-in privacy markers ([PRIVATE], [CONFIDENTIAL], [DO NOT SHARE])
• ⚡ Edge-Native: Optimized for Cloudflare Workers with Durable Objects
• 🛠️ LLM Tool Integration: Ready-to-use tool definitions for function calling
• 💾 SQL-Based: Uses Cloudflare Durable Objects SQL for persistence
• 🎨 Effect-Based: Leverages Effect for type-safe error handling

📦 Installation

npm install memedge
# or
yarn add memedge
# or
pnpm add memedge

🚀 Quick Start

Basic Memory Operations

import { Effect } from 'effect';
import { 
  MemoryManagerLive, 
  SqlStorageContext 
} from 'memedge/memory';

// Setup SQL storage context
const sqlContext = SqlStorageContext.of({ sql: durableObjectSQL });

// Create and use memory manager
const program = Effect.gen(function* () {
  const memoryManager = yield* MemoryManagerService;
  
  // Initialize database
  yield* memoryManager.initializeDatabase();
  
  // Write memory
  yield* memoryManager.writeMemory('user_profile', 'Name: Alice, Role: Engineer');
  
  // Read memory
  const entry = yield* memoryManager.readMemory('user_profile');
  console.log(entry?.text);
});

// Run with context
Effect.runPromise(
  program.pipe(
    Effect.provide(MemoryManagerLive),
    Effect.provide(Layer.succeed(SqlStorageContext, sqlContext))
  )
);

Memory Blocks (Letta-Style)

import { 
  MemoryBlockManagerLive,
  MemoryBlockManagerService 
} from 'memedge/memory';

const program = Effect.gen(function* () {
  const manager = yield* MemoryBlockManagerService;
  
  // Create a memory block
  yield* manager.createBlock(
    'human',
    'Human',
    'Name: Alice\nRole: Software Engineer\nPrefers: Concise responses',
    'core'
  );
  
  // Insert content
  yield* manager.insertContent(
    'human',
    'Company: TechCorp',
    'end'
  );
  
  // Replace content
  yield* manager.replaceContent(
    'human',
    'Concise responses',
    'Detailed explanations'
  );
  
  // Get block
  const block = yield* manager.getBlock('human');
  console.log(block?.content);
});

Semantic Search

import { 
  searchMemoryBlocks,
  generateEmbedding,
  AiBindingContext 
} from 'memedge/memory';

const program = Effect.gen(function* () {
  const manager = yield* MemoryBlockManagerService;
  const blocks = yield* manager.getAllBlocks();
  
  // Search memory blocks semantically
  const results = yield* searchMemoryBlocks(
    'health information',
    blocks,
    5,  // limit
    0.5 // threshold
  );
  
  results.forEach(r => {
    console.log(`${r.block.label}: ${r.score}`);
    console.log(r.block.content);
  });
});

// Provide AI binding for embeddings
Effect.runPromise(
  program.pipe(
    Effect.provide(MemoryBlockManagerLive),
    Effect.provide(Layer.succeed(AiBindingContext, { ai: env.AI }))
  )
);

Recursive Summarization

import { 
  createBaseSummary,
  checkRecursiveSummarizationNeeded,
  createRecursiveSummary 
} from 'memedge/summaries';

const program = Effect.gen(function* () {
  // Create base summary from messages
  const summaryId = yield* createBaseSummary(messages, persona);
  
  // Check if recursive summarization is needed
  const check = yield* checkRecursiveSummarizationNeeded();
  
  if (check.needed && check.summaries) {
    // Create recursive summary
    const recursiveId = yield* createRecursiveSummary(
      check.summaries,
      check.level!,
      persona
    );
    console.log(`Created level ${check.level} summary: ${recursiveId}`);
  }
});

🛠️ LLM Tool Integration

Memedge provides ready-to-use tool definitions for LLM function calling:

import { 
  getMemoryTools,
  getEnhancedMemoryTools,
  getAllMemoryTools 
} from 'memedge/tools';

// Basic tools
const basicTools = getMemoryTools();
// { memory_read, memory_write }

// Enhanced Letta-style tools
const enhancedTools = getEnhancedMemoryTools();
// { 
//   memory_get_block, memory_insert, memory_replace, 
//   memory_rethink, memory_create_block, memory_list_blocks,
//   archival_insert, archival_search, memory_search 
// }

// All tools (enhanced + legacy)
const allTools = getAllMemoryTools();

// Use with your LLM provider
const response = await generateText({
  model: openai('gpt-4'),
  tools: allTools,
  // ...
});

Tool Execution

import { 
  executeMemoryGetBlock,
  executeMemoryInsert,
  executeMemorySearch 
} from 'memedge/tools';

// Execute tool based on LLM response
if (toolCall.name === 'memory_get_block') {
  const result = yield* executeMemoryGetBlock(toolCall.args);
  // { block_id, label, content, updated_at }
}

if (toolCall.name === 'memory_insert') {
  const result = yield* executeMemoryInsert(toolCall.args);
  // { success, message }
}

if (toolCall.name === 'memory_search') {
  const result = yield* executeMemorySearch({
    ...toolCall.args,
    useSemanticSearch: true
  });
  // { results: [{ block_id, label, content, score }] }
}

📚 Core Concepts

Memory Blocks

Memory blocks are structured containers for different types of information:

• Core Blocks: Always loaded into context (human, persona, context, custom)
• Archival Blocks: Searchable long-term storage, loaded on-demand
• Operations: insert, replace, rethink (complete rewrite)

Privacy Markers

Memedge supports privacy-aware memory with built-in markers:

// Store private information
yield* memoryManager.writeMemory(
  'health_info',
  '[PRIVATE] Allergic to penicillin. [CONFIDENTIAL] Therapy on Tuesdays.'
);

// The system respects these markers when sharing information

Supported markers:

• [PRIVATE] - Personal information
• [CONFIDENTIAL] - Confidential data
• [DO NOT SHARE] - Explicitly not shareable
• [PERSONAL] - Personal notes

Semantic Search Architecture

Memedge uses a simple but effective approach to semantic search:

• Embeddings Generation: Uses Cloudflare AI (@cf/baai/bge-base-en-v1.5, 768 dimensions)
• Storage: Embeddings stored as JSON in SQL (no separate vector DB!)
• Search: Cosine similarity computed in-worker
• Performance: Sub-50ms search latency for typical queries
• Cost: Included in Cloudflare Workers costs

Recursive Summarization

Hierarchical conversation summarization for managing long-term context:

Level 0: Base Summaries (20 messages each)
Level 1: Meta-Summaries (10 x L0)
Level 2: Super-Summaries (10 x L1)
Level 3: Ultra-Summaries (10 x L2)

This logarithmic approach keeps context manageable even with thousands of messages.

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                    Memedge System                        │
│                                                           │
│  ┌────────────────┐  ┌──────────────────────────────┐  │
│  │ Memory Manager │  │ Memory Block Manager          │  │
│  │ (Legacy KV)    │  │ (Letta-style)                 │  │
│  │                │  │                                │  │
│  │ • purpose/text │  │ • Structured blocks           │  │
│  │ • Privacy      │  │ • Core + Archival             │  │
│  │   markers      │  │ • insert/replace/rethink      │  │
│  └────────────────┘  └──────────────────────────────┘  │
│           │                       │                      │
│           └───────────┬───────────┘                      │
│                       ▼                                  │
│  ┌───────────────────────────────────────────────────┐ │
│  │        Semantic Search (Cloudflare AI)             │ │
│  │                                                     │ │
│  │  • Generate embeddings (768D)                      │ │
│  │  • Store in SQL as JSON                            │ │
│  │  • Cosine similarity search                        │ │
│  │  • No external vector DB                           │ │
│  └───────────────────────────────────────────────────┘ │
│                       │                                  │
│                       ▼                                  │
│  ┌───────────────────────────────────────────────────┐ │
│  │     Recursive Summarization                        │ │
│  │                                                     │ │
│  │  • Base summaries (L0)                             │ │
│  │  • Recursive meta-summaries (L1, L2, L3)          │ │
│  │  • Hierarchical context compression                │ │
│  └───────────────────────────────────────────────────┘ │
│                       │                                  │
│                       ▼                                  │
│  ┌───────────────────────────────────────────────────┐ │
│  │        Durable Objects SQL Storage                 │ │
│  │                                                     │ │
│  │  • agent_memory (legacy)                           │ │
│  │  • memory_blocks (structured)                      │ │
│  │  • archival_memory (long-term)                     │ │
│  │  • memory_embeddings (vectors)                     │ │
│  │  • conversation_summaries_v2 (recursive)           │ │
│  └───────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────┘

🔧 Configuration

Summarization Config

const config: SummarizationConfig = {
  baseSummaryThreshold: 20,    // Messages before L0 summary
  recursiveThreshold: 10,       // Summaries before next level
  maxLevel: 3,                  // Maximum recursion depth
  recentSummaryCount: 3         // Recent summaries to load
};

Semantic Search Config

// Search with custom threshold and limit
const results = yield* searchMemoryBlocks(
  query,
  blocks,
  10,   // limit: max results
  0.7   // threshold: minimum similarity score
);

📖 API Reference

See the API documentation for detailed API reference.

🧪 Testing

# Run tests
npm test

# Watch mode
npm run test:watch

# Coverage
npm run test:coverage

🤝 Contributing

Contributions are welcome! Please read our Contributing Guide for details.

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Inspired by Letta (MemGPT) - Thank you to the Letta team for pioneering advanced memory systems for LLM agents
• Built for Cloudflare Workers
• Powered by Effect

🔗 Links

• Documentation
• Examples
• Changelog
• Roadmap

📊 Comparison with Letta

Feature	Memedge	Letta
Architecture	Cloudflare Workers + Durable Objects	Python + PostgreSQL + Vector DB
Memory Blocks	✅ Core + Archival	✅ Core + Archival
Semantic Search	✅ Built-in (Cloudflare AI)	✅ External Vector DB
Embeddings	768D, stored in SQL	Configurable, separate DB
Latency	~30-50ms (edge)	~100-200ms (server)
Scalability	Edge-native, globally distributed	Server-based
Privacy Markers	✅ Built-in	❌ Not included
Recursive Summarization	✅ Hierarchical	❌ Simple
Tool Integration	✅ Zod schemas	✅ Pydantic
Cost	Included in Workers	Separate services
Visual Tools	❌ Code-first	✅ Agent Dev Environment

Made with ❤️ for the LLM agent community