@kuindji/memory-domain

@kuindji/memory-domain

A domain-driven memory engine with graph storage, embeddings, and semantic search. Built on SurrealDB.

Features

• Domain-based architecture — memories are organized into bounded domains (topics, users, code repos, knowledge base) that define their own processing, search, and scheduling logic
• Graph data model — memories, tags, and domains form a rich graph with typed edges (reinforces, contradicts, summarizes, refines)
• Hybrid search — combines vector similarity, full-text search, and graph traversal
• Inbox processing — parallel ingestion pipeline with similarity batching and deduplication
• LLM integration — pluggable LLM adapters; ships with ClaudeCliAdapter (local claude CLI) and BedrockAdapter (Amazon Bedrock via the Anthropic SDK)
• Embedding support — ONNX-based local embeddings via onnxruntime-node
• Portable databases — connection adapters for embedded surrealkv, local tar.gz archives, and S3-backed snapshots

Installation

npm install @kuindji/memory-domain

First-time setup: download the embedding model

OnnxEmbeddingAdapter needs the all-MiniLM-L6-v2 ONNX model (~86 MB) and its vocab file. Fetch them with:

npx memory-domain-download-model
# or to a custom location:
npx memory-domain-download-model --dir ./my-models

By default this writes to ./.memory-domain/model/ relative to the current directory, which is where new OnnxEmbeddingAdapter() looks. If you download elsewhere, pass the same path to the adapter via new OnnxEmbeddingAdapter({ modelDir: "./my-models" }).

Quick Start

import {
    MemoryEngine,
    topicDomain,
    ClaudeCliAdapter,
    OnnxEmbeddingAdapter,
} from "@kuindji/memory-domain";

const engine = new MemoryEngine();

await engine.initialize({
    connection: "surrealkv:///tmp/memories/db", // embedded; or "ws://localhost:8000" for a remote SurrealDB
    namespace: "my_app",
    database: "memories",
    llm: new ClaudeCliAdapter(),
    embedding: new OnnxEmbeddingAdapter(),
});

await engine.registerDomain(topicDomain);

// Ingest a memory
await engine.ingest("TypeScript 5.5 introduces inferred type predicates");

// Search
const results = await engine.search("TypeScript features");

// Ask a question
const answer = await engine.ask("What do I know about TypeScript?");

Using Amazon Bedrock instead of the Claude CLI

import { BedrockAdapter } from "@kuindji/memory-domain";

const llm = new BedrockAdapter({
    modelId: "eu.anthropic.claude-sonnet-4-6",
    region: "eu-west-2",
    profile: "my-aws-profile", // or pass `credentials`, or omit to use the default chain
    modelLevels: {
        low: "eu.anthropic.claude-haiku-4-5",
        medium: "eu.anthropic.claude-sonnet-4-6",
        high: "eu.anthropic.claude-opus-4-6-v1",
    },
});

BedrockAdapter implements the same LLMAdapter interface as ClaudeCliAdapter, so it's a drop-in swap.

CLI

The package includes CLI tools:

# CLI
npx memory-domain --help
npx memory-domain ingest --text "Some memory to store"
npx memory-domain search --query "find something"

# Interactive TUI
npx memory-domain-tui

Configuration

The CLI and TUI look for a memory-domain.config.ts (or .js / .mjs) file in your project root. The module must default-export a fully-initialized MemoryEngine instance — use top-level await to initialize and register domains before exporting:

import {
    MemoryEngine,
    topicDomain,
    ClaudeCliAdapter,
    OnnxEmbeddingAdapter,
} from "@kuindji/memory-domain";

const engine = new MemoryEngine();

await engine.initialize({
    connection: "surrealkv:///tmp/memories/db",
    namespace: "default",
    database: "memory",
    llm: new ClaudeCliAdapter(),
    embedding: new OnnxEmbeddingAdapter(),
});

await engine.registerDomain(topicDomain);

export default engine;

Connection adapters

EngineConfig.adapter accepts a ConnectionAdapter that resolves to a SurrealDB connection string at startup and can optionally persist the database back on engine.close(). The package ships three:

• PassthroughAdapter(connectionString) — just returns the string you gave it. Use when you already have a SurrealDB URL (remote server, file path, in-memory).
• FileConnectionAdapter({ file, localDir?, save? }) — extracts a local .tar.gz archive to a working directory and exposes it as surrealkv://<dir>/db. Set save: true to recompress on close. Ideal for shipping prebuilt KBs alongside your code.
• S3ConnectionAdapter({ bucket, key, region, profile?, credentials?, save? }) — same shape but fetches/uploads the archive from S3.
• DirectoryConnectionAdapter({ path }) — opens a pre-extracted path/db/ directory in place. Read-intended; save() is a no-op. Use when a container image bakes the DB alongside the code and you want to skip tar extraction on cold start. Do not open the same path twice in one process — SurrealKV expects exclusive access.

import { MemoryEngine, FileConnectionAdapter, BedrockAdapter } from "@kuindji/memory-domain";

const engine = new MemoryEngine();
await engine.initialize({
    adapter: new FileConnectionAdapter({ file: "./kb.tar.gz" }),
    namespace: "my_app",
    database: "kb",
    llm: new BedrockAdapter({
        modelId: "eu.anthropic.claude-sonnet-4-6",
        region: "eu-west-2",
        profile: "my-aws-profile",
    }),
});

Programmatic usage

The CLI is one of several transports over a shared command dispatcher. You can use memory-domain as a library to serve the same command surface over a Lambda, HTTP server, or any other runtime without shelling out.

Lambda with a baked-in knowledge base

import {
    MemoryEngine,
    DirectoryConnectionAdapter,
    BedrockAdapter,
    OnnxEmbeddingAdapter,
    createKbDomain,
    createLambdaAdapter,
} from "@kuindji/memory-domain";

// Construct once at module scope; reused across warm invocations.
const handlerPromise = (async () => {
    const engine = new MemoryEngine();
    await engine.initialize({
        adapter: new DirectoryConnectionAdapter({ path: "/var/task/kb" }),
        namespace: "my_app",
        database: "business",
        llm: new BedrockAdapter({
            modelId: "eu.anthropic.claude-sonnet-4-6",
            region: "eu-west-2",
        }),
        embedding: new OnnxEmbeddingAdapter({ modelDir: "/var/task/kb/model" }),
    });
    await engine.registerDomain(createKbDomain());
    return createLambdaAdapter(engine); // defaults to the read-only profile
})();

export const handler = async (event: { command: string; args: string[] }) => {
    const lambdaHandler = await handlerPromise;
    return lambdaHandler(event);
};

The default profile exposes the read-side commands: search, build-context, memory, graph, skill, domains, domain. Pass { profile: "full" } to expose every command, or { profile: [...] } for a custom allow list. The returned DispatchResult carries both the raw output (structured data) and rendered (a pre-rendered JSON or pretty string), so callers can use whichever form they need.

Invocation shape: event.args is the argv tail including flags, e.g. { command: "search", args: ["query text", "--limit", "5"] }. Flags must be encoded as argv strings, matching the CLI's input contract.

Lifecycle: createLambdaAdapter never calls engine.close(). Construct the engine once at module scope and rely on Lambda's execution-environment reuse. Cold-start engine-init errors propagate out of engine.initialize() so Lambda marks the container as failed; per-invocation errors resolve as { ok: false, error: { code, message } }.

Built-in Domains

• Topic — general-purpose topic-based memories with lifecycle management
• User — user profile and preference tracking
• Code Repo — code repository knowledge (patterns, decisions, architecture)
• Knowledge Base — structured knowledge with classification
• Log — simple append-only logging

Requirements

• Node.js 18+ or Bun
• A SurrealDB backend — either the embedded @surrealdb/node engine (default, via a surrealkv:// connection string or one of the bundled connection adapters) or a running SurrealDB server reached over ws:// / http://

License

MIT