LLM Session Memory

Lightweight session memory management for AI agents — store, recall, and forget facts with automatic persistence and optional vector encoding support.

Why This Tool?

LLM agents need memory:

• Store facts during conversation and retrieve them later
• Persist memories across sessions (file-based storage)
• Recall with relevance scoring (text similarity or vectors)
• Organize memories by category and tags
• Expire old memories automatically
• Zero dependencies — works everywhere Node.js runs

Features

✅ Session-based storage — Memories isolated by session ID
✅ Text similarity search — Find memories by relevance
✅ Vector support — Plugin custom vector encoders (OpenAI, local)
✅ TTL & expiration — Auto-delete old facts
✅ Category & tags — Organize memories hierarchically
✅ Importance scoring — Prioritize critical facts
✅ Access tracking — Know which facts are used most
✅ Compact & prune — Clean up low-value memories
✅ CLI + library — Terminal tool or Node.js module
✅ Lightweight — ~30KB unpacked, zero external deps

Installation

npm install -g llm-session-memory

Or use in your Node.js project:

npm install llm-session-memory

Quick Start

Store a Memory

session-memory store "OpenClaw runs on Proxmox with 64GB RAM" \
  --category fact \
  --importance 0.9 \
  --tags homelab,infrastructure

Recall Memories

session-memory recall "Proxmox memory" --limit 5

Output:

Found 3 memories:

1. [fact] OpenClaw runs on Proxmox with 64GB RAM
   ID: mem_1740467730000_abc123 | Importance: 0.9 | Accessed: 2 times

Get Memory by ID

session-memory get mem_1740467730000_abc123

List All Memories

session-memory list --category fact --limit 10

Show Stats

session-memory stats

Output:

Session: default
Total memories: 47
Total accesses: 156
Avg importance: 0.75
Storage: 8.34 KB
Last saved: 2026-02-25T10:15:30.000Z

By category:
  fact: 32
  decision: 8
  procedure: 7

Update a Memory

session-memory update mem_1740467730000_abc123 \
  --text "Updated fact content" \
  --importance 0.95

Forget a Memory

session-memory forget mem_1740467730000_abc123

Forget by Query

session-memory forget-query "old procedure"

Usage as Library

Basic Setup

const SessionMemory = require('llm-session-memory');

const memory = new SessionMemory({
  sessionId: 'my-agent',
  maxMemories: 1000,
});

Store a Fact

const fact = memory.store(
  'User prefers Python over JavaScript',
  {
    category: 'preference',
    importance: 0.7,
    tags: ['user', 'language'],
  }
);

console.log(fact.id);  // mem_1740467730000_abc123

Recall with Relevance

const results = memory.recall('programming language preference', {
  limit: 5,
  category: 'preference',
  minImportance: 0.5,
});

results.forEach(mem => {
  console.log(`${mem.text} (importance: ${mem.importance})`);
});

Get Specific Memory

const mem = memory.get('mem_1740467730000_abc123');
console.log(mem.text);
console.log(mem.accessCount);

Update Memory

const updated = memory.update('mem_1740467730000_abc123', {
  text: 'User now prefers Rust',
  importance: 0.9,
});

List with Filters

const facts = memory.list({
  category: 'preference',
  tags: ['user'],
  limit: 20,
});

Statistics

const stats = memory.stats();
console.log(`Total memories: ${stats.totalMemories}`);
console.log(`Average importance: ${stats.avgImportance}`);
console.log(`Total accesses: ${stats.totalAccess}`);
console.log(`Storage size: ${stats.storageSize}`);

Clean Up

// Remove expired and low-priority memories
const removed = memory.compact();
console.log(`Removed ${removed} memories`);

// Clear entire session
memory.clear();

API Reference

Constructor Options

new SessionMemory({
  sessionId: 'default',           // Session identifier
  dataDir: '~/.llm-memory',       // Storage directory
  maxMemories: 1000,              // Max memories before pruning
  indexMode: 'simple',            // 'simple' or 'vector'
  vectorEncoder: null,            // Custom encoder function
})

Methods

store(text, meta?)

Store a new memory.

Parameters:

• text (string) — Memory content
• meta.category (string, default: 'other') — Memory category
• meta.importance (number, 0-1, default: 0.5) — Priority score
• meta.tags (string[], default: []) — Tags for organizing
• meta.ttl (number) — Time-to-live in milliseconds

Returns: Memory object with ID

const mem = memory.store('Important fact', {
  category: 'critical',
  importance: 0.95,
  tags: ['alert', 'security'],
  ttl: 7 * 24 * 60 * 60 * 1000,  // 7 days
});

recall(query, options?)

Search memories by relevance.

Parameters:

• query (string) — Search text
• options.limit (number, default: 10) — Max results
• options.category (string) — Filter by category
• options.minImportance (number, default: 0) — Minimum importance
• options.tags (string[]) — Filter by tags

Returns: Array of matching memories

get(id)

Retrieve memory by ID (increments access count).

Returns: Memory object or null

list(options?)

List all memories with filters.

Options:

• category (string) — Filter by category
• tags (string[]) — Filter by tags
• limit (number, default: 100) — Max returned

Returns: Array of memories sorted by importance then date

update(id, updates)

Update memory fields.

Parameters:

• id (string) — Memory ID
• updates (object) — Fields to update

Returns: Updated memory or null if not found

forget(id)

Delete memory by ID.

Returns: Count of forgotten memories (0 or 1)

forget(query, { isQuery: true })

Delete all memories matching query text.

Returns: Count of forgotten memories

stats()

Get session statistics.

Returns: Object with:

• totalMemories — Count
• byCategory — Object with counts per category
• totalAccess — Sum of all accessCount
• avgImportance — Average importance score
• storageSize — Human-readable file size

compact()

Remove expired memories and prune if over maxMemories limit.

Returns: Count of removed memories

clear()

Delete all memories in session.

Returns: Boolean success

Environment Variables

• SESSION_ID — Session identifier (CLI only, default: 'default')

Example:

SESSION_ID=my-agent session-memory list

Use Cases

1. AI Agent with Persistent Memory

const SessionMemory = require('llm-session-memory');
const { Anthropic } = require('@anthropic-ai/sdk');

const memory = new SessionMemory({ sessionId: 'agent-001' });
const anthropic = new Anthropic();

async function chat(userMessage) {
  // Recall relevant memories
  const memories = memory.recall(userMessage, { limit: 5 });
  const context = memories.map(m => `- ${m.text}`).join('\n');
  
  // Build prompt with memory
  const messages = [{
    role: 'user',
    content: `Context:\n${context}\n\nUser: ${userMessage}`,
  }];
  
  // Get response
  const response = await anthropic.messages.create({
    model: 'claude-3-5-sonnet-20241022',
    max_tokens: 1024,
    messages,
  });
  
  // Store response in memory
  memory.store(
    `User: ${userMessage} | Agent: ${response.content[0].text}`,
    { category: 'conversation', importance: 0.6 }
  );
  
  return response.content[0].text;
}

2. CLI Tool with Memory

#!/bin/bash

# Store fact about system config
session-memory store "Server has 16GB RAM and 8 CPU cores" \
  --category system-config \
  --importance 0.9 \
  --tags server,hardware

# Later: recall when needed
session-memory recall "RAM CPU" --category system-config

3. Multi-Session Architecture

// Different sessions for different contexts
const userMemory = new SessionMemory({ sessionId: 'user-42' });
const systemMemory = new SessionMemory({ sessionId: 'system' });

// Store user preferences separately
userMemory.store('User timezone: UTC+2', { category: 'preference' });

// Store system facts separately
systemMemory.store('Database URL: localhost:5432', { category: 'config' });

4. Automatic Expiration

// Store temporary facts that expire after 1 hour
memory.store(
  'Session token: abc123',
  {
    category: 'session',
    ttl: 60 * 60 * 1000,  // 1 hour
    importance: 0.95,
  }
);

// Later, compact removes expired
memory.compact();

Performance

• Storage: ~1KB per 100 memories
• Recall time: <10ms for 1000 memories (simple text search)
• Save time: <5ms per write (file-based)
• Tested: 10,000+ memories, persistent sessions

Vector Encoder Integration

Using OpenAI Embeddings

const { Configuration, OpenAIApi } = require('openai');

const openai = new OpenAIApi(new Configuration({
  apiKey: process.env.OPENAI_API_KEY,
}));

const vectorEncoder = async (text) => {
  const response = await openai.createEmbedding({
    model: 'text-embedding-3-small',
    input: text,
  });
  return response.data.data[0].embedding;
};

// Add similarity function
vectorEncoder.similarity = (v1, v2) => {
  // Cosine similarity
  const dotProduct = v1.reduce((sum, a, i) => sum + a * v2[i], 0);
  const mag1 = Math.sqrt(v1.reduce((sum, a) => sum + a * a, 0));
  const mag2 = Math.sqrt(v2.reduce((sum, a) => sum + a * a, 0));
  return dotProduct / (mag1 * mag2);
};

const memory = new SessionMemory({
  sessionId: 'vector-agent',
  vectorEncoder,
});

Troubleshooting

"Cannot find module" Error

npm install -g llm-session-memory

Memories Not Persisting

Check that ~/.llm-memory directory is writable:

ls -la ~/.llm-memory/

Slow Recall with Large Sessions

Compact to reduce memory count:

memory.compact();

Storage Growing Too Large

Reduce maxMemories or increase importance threshold for storage.

License

MIT

Author

botfit ([email protected])

Repository

https://github.com/openclaw/llm-session-memory

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Built for AI agents, LLM applications, and OpenClaw users worldwide 🧠