memori-js

🧠 memori-js

The SQL-Native AI Memory Fabric for JavaScript & TypeScript.

memori-js is not just a vector database wrapper. It is an active memory layer that lives inside your application, automatically managing context for your AI agents. It bridges the gap between your LLM and long-term storage without the complexity of building manual RAG (Retrieval-Augmented Generation) pipelines.

🌐 Website | 📚 Documentation

Inspired by the memorilabs.ai Python library.

🆕 Recent Updates (v1.1)

• Dynamic Embeddings: You can now bring any embedding provider (OpenAI, HuggingFace, etc.) using the embedding prop.
• Provider Agnostic API: googleApiKey is now apiKey and MEMORI_API_KEY.
• Master Guide: See MASTER_CONFIG.md for advanced customization.
• Record: See WALKTHROUGH_RECORD.md for a change log.

🚀 Why Memori?

If you are building an AI app today, you usually have to:

• Set up a vector DB (Pinecone, Qdrant, Weaviate, etc.).
• Manually chunk and embed user input.
• Query the DB.
• Inject user context into the system prompt.
• Call the LLM.
• Save the new conversation back to the DB/Embeddings.

With memori-js, you just do this:

// 1 line to register memory
memori.llm.register(client);

// Call your LLM as normal
await client.chat.completions.create({ ... });

Memori vs. Standard Vector DBs

Feature	Standard Vector DB	🧠 Memori-JS
Setup	Requires Docker, API keys, or cloud infrastructure.	Zero-Config. Creates a local memori.db SQLite file instantly.
Scalability	Manual migration needed.	Pluggable. Scale from local SQLite to Postgres/Supabase seamlessly.
Integration	You write the RAG pipeline logic manually.	Auto-Augmentation. Patches the LLM client to inject memory automatically.
Complexity	High (Embeddings, Chunking, Retrieval).	Low. Handles embedding generation and retrieval internally.

✨ Features

• 🔌 Provider Agnostic: Works seamlessly with OpenAI, Google GenAI, and Anthropic.
• ☁️ Cloud Ready: Switch between Local SQLite (default) or Postgres (Supabase, Neon) with one line of config.
• 🛡️ Enterprise Quality: Built-in Arktype validation, Structured Logging, and typed Error Handling.
• ⚡ SQL-Native Performance: Powered by sqlite-vec locally or pgvector in the cloud.
• 🤖 Zero-Shot RAG: Your older conversations automatically become context for new ones.

📦 Installation

npm install memori-js
# or
bun add memori-js

🛠️ Usage

1. Initialize

Option A: Zero Config (Local SQLite)

import { Memori } from "memori-js";

const memori = new Memori({
  apiKey: process.env.MEMORI_API_KEY, // Default is Google GenAI
});

Option B: Cloud Scalability (Postgres)

import { Memori, PostgresVecStore } from "memori-js";

const memori = new Memori({
  apiKey: process.env.MEMORI_API_KEY,
  // Seamlessly switch to Postgres for production
  vectorStore: new PostgresVecStore(process.env.DATABASE_URL!),
});
await memori.config.storage.build(); // Initializes tables if missing

2. Connect Your LLM

Memori supports "Patching" — it wraps your existing LLM client to add memory capabilities transparently.

🔵 OpenAI (Copy-Paste Example)

import { Memori } from "memori-js";
import OpenAI from "openai";

const memori = new Memori({ apiKey: process.env.MEMORI_API_KEY });
await memori.config.storage.build(); // Init DB

const client = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
memori.llm.register(client); // Auto-detects "openai"

// 1. Teach Memory
await memori.addMemory("My name is John and I am a software engineer.");

// 2. Ask (Context is auto-injected)
const response = await client.chat.completions.create({
  model: "gpt-4",
  messages: [{ role: "user", content: "Who am I and what do I do?" }],
});

console.log(response.choices[0].message.content);
// Output: "You are John, a software engineer."

🟢 Google GenAI (Copy-Paste Example)

import { Memori } from "memori-js";
import { GoogleGenAI } from "@google/genai";

const memori = new Memori({ apiKey: process.env.MEMORI_API_KEY });
await memori.config.storage.build(); // Init DB

const client = new GoogleGenAI({ apiKey: process.env.GOOGLE_API_KEY });
memori.llm.register(client); // Auto-detects "google"

// 1. Teach Memory
await memori.addMemory("My name is John and I am a software engineer.");

// 2. Ask (Context is auto-injected)
const result = await client.models.generateContent({
  model: "gemini-2.5-flash",
  contents: [
    {
      role: "user",
      parts: [{ text: "Who am I and what do I do?" }],
    },
  ],
});

// Response text is directly available or via candidates
console.log(result.text || result.candidates?.[0]?.content?.parts?.[0]?.text);
// Output: "You are John, a software engineer."

🟠 Anthropic (Copy-Paste Example)

import { Memori } from "memori-js";
import Anthropic from "@anthropic-ai/sdk";

const memori = new Memori({ apiKey: process.env.MEMORI_API_KEY });
await memori.config.storage.build(); // Init DB

const client = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });
memori.llm.register(client); // Auto-detects "anthropic"

// 1. Teach Memory
await memori.addMemory("My name is John and I am a software engineer.");

// 2. Ask (Context is auto-injected)
const response = await client.messages.create({
  model: "claude-3-opus-20240229",
  max_tokens: 1000,
  messages: [{ role: "user", content: "Who am I and what do I do?" }],
});

console.log(response.content[0].text);
// Output: "You are John, a software engineer."

3. Advanced Configuration

memori-js uses Arktype for ultra-fast runtime validation and strict typing.

import { ConsoleLogger } from "memori-js";

const memori = new Memori({
  apiKey: "...",
  dbPath: "./custom-memory.db",
  logger: new ConsoleLogger(), // Or pass your own Pino/Winston wrapper
});

📖 Master Configuration Guide

For deep customization (Custom Embeddings, Vectors, etc.), check out the Master Configuration Guide. It covers:

• Using Custom Embedding Providers (OpenAI, Voyage, Local)
• End-to-End Workflow Diagram
• Full Configuration Options Reference */

4. Advanced Augmentation (Multi-Tenancy)

For multi-user apps (chatbots, agents), you can isolate memory by User ID and Agent ID.

// Define context for the current operation
memori.attribution("user-123", "agent-sales");

// All subsequent operations are scoped to this user
await memori.addMemory("I like apples."); // Stored with metadata

// Search is filtered automatically
// Search is filtered automatically
const results = await memori.search("What do I like?");
// returns "I like apples." ONLY for user-123

5. CLaRa (Contextual Latent Retrieval augmented generation) 🆕

CLaRa is an advanced optimization pipeline that compresses memories before storage and "reasons" about queries before search.

• Compression: Turns "Um, I think my favorite color is like, blue?" into favorite_color: blue. Saves ~40% tokens.
• Reasoning: Turns "What about that project?" into Project Chimera status, deadlines.

const memori = new Memori({
  // ...
  clara: {
    enableCompression: true,
    enableReasoning: true,
    // Optional: Use a dedicated fast model for compression (Groq/Ollama)
    compressor: {
      generate: async (prompt) => {
        /* Call Llama 3 / Gemma 2 */
      },
    },
  },
});

Read the full CLaRa Release Notes for benchmarks and implementation details.

💡 Philosophy

Most "Memory" libraries are just complex wrappers around vector stores. memori-js takes a different approach: Memory should be invisible.

As a developer, you shouldn't care how the relevant context is found, only that your agent has it. By pushing this logic down into the infrastructure layer (SQLite/Postgres) and the client layer (Patching), we allow you to build complex, stateful agents with simple, stateless code.

License

MIT