Monlite: The complete back end for AI agents – in one file

Monlite launches a zero-dependency TypeScript backend for AI agents that combines memory, vector search, task queues, locks, and cron into a single SQLite file, eliminating the need for Docker or multiple services. The open-source tool scales to Postgres when needed and is available on npm.

Document memory, semantic vector recall, full-text search, a durabletask queue, atomiclocks, cache, andcron— over a single SQLite file, with a zero-dependency TypeScript core.No Docker. No connection strings. Nothing to run.And thesame codescales to Postgres the day you need it. A coding agent, RAG pipeline, or autonomous worker needs memory, semantic search, a job queue, and locks. That's normally MongoDB + Qdrant + Redis + BullMQ — four services and a Docker compose file. monlite is all of it, in a file you can cp to back up: js import { createDb } from "@monlite/core"; import { vector } from "@monlite/vector"; import { createQueue } from "@monlite/queue"; import { kv } from "@monlite/kv"; // one file = the agent's entire backend const db = createDb "./agent.db", { allowExtensions: true, plugins: vector { memory: { field: "embedding", dimensions: 384 } } , } ; // 🧠 memory — typed document collections await db.collection "memory" .create { data: { note: "user prefers dark mode", embedding } } ; // 🔎 semantic recall — vector search over embeddings const recall = await db.collection "memory" .findSimilar { vector: query, topK: 5 } ; // 🔒 an atomic lock run-once + 📋 a durable task queue retries, backoff, dedupe, concurrency if kv db .setNX "lock:ingest", 1, { ttl: 30 000 } startIngest ; createQueue db .process "embed", job = embed job.payload.text , { concurrency: 4 } ; Exactly-once job claims, locks, scheduling, and full-text + semantic search — with no server, no migrations, and no native build Node 22.5+ uses the built-in node:sqlite . 📖 Docs https://qataruts.github.io/monlite · 🎮 Live demo https://qataruts.github.io/monlite/demo runs in your browser · 📦 npm https://www.npmjs.com/package/monlite · 💻 GitHub https://github.com/qataruts/monlite Most apps, CLIs, and AI agents wire up the same services. monlite gives you each one as a small package over a single .db file — install only what you use, the core stays zero-dependency: | Instead of | Use | Gives you | |---|---|---| | MongoDB / Mongoose | @monlite/core | watch @monlite/fts collection.search @monlite/vector findSimilar , hybrid RAG @monlite/kv @monlite/queue @monlite/cron @monlite/realtime @monlite/sync @monlite/postgres the same API on a networked Postgres when you outgrow one fileNo Docker. No .env full of connection strings. One file, one API, node serve.mjs . Batteries-included — the whole stack in one package: npm install monlite js import { createDb, kv, createQueue, createCron, fts, vector } from "monlite"; Or the minimal, zero-dependency core , plus packages à la carte: npm install @monlite/core zero-dep core Node ≥ 22.5, built-in node:sqlite npm install @monlite/core better-sqlite3 Node 18/20, or to skip the experimental flag npm install @monlite/fts full-text search @monlite/vector semantic search npm install @monlite/kv cache, locks, pub/sub @monlite/queue durable job queue npm install @monlite/cron scheduler @monlite/realtime live queries over SSE npm install @monlite/postgres run the same API on Postgres npm install @monlite/sync cloud sync MongoDB / PostgreSQL / MySQL npm install @monlite/wasm browser / SQLite-WASM @monlite/electron Electron main↔renderer Zero-install inspector: npx @monlite/studio app.db opens a local web UI to browse collections, view documents, and run queries. A Mongo/Prisma-style API. Typed collections get compile-time-checked where / orderBy , and return types that narrow with select . interface Order { customerId: string; items: { sku: string; qty: number } ; status: "pending" | "shipped" | "returned"; total: number; } const orders = db.collection<Order "orders" ; // query inside arrays of objects await orders.findMany { where: { items: { elemMatch: { sku: "WIDGET", qty: { gte: 5 } } } } } ; // case-insensitive regex await orders.findMany { where: { status: { regex: "^pend", mode: "insensitive" } } } ; // grouped aggregation — GROUP BY with sums, HAVING, top-N await orders.groupBy { by: "customerId" , where: { status: "shipped" }, sum: { total: true }, orderBy: { sum: { total: "desc" } }, take: 10, } ; // atomic transactions — await inside, all-or-nothing await db.transactionAsync async tx = { const accounts = tx.collection "accounts" ; await accounts.update { where: { id: "acc-1" }, data: { $inc: { balance: -100 } } } ; await accounts.update { where: { id: "acc-2" }, data: { $inc: { balance: +100 } } } ; } ; // cross-process compare-and-swap — exactly-once job claim const claimed = await orders.findOneAndUpdate { where: { status: "pending" }, data: { $set: { status: "active" } }, returnDocument: "after", } ; // N workers race; exactly one wins, the rest get null Full surface: create / createMany , findMany / findFirst / findById , update / updateMany , upsert , delete / deleteMany , count / exists / distinct , aggregate / groupBy , bulkWrite , findOneAndUpdate , TTL collections, explain , and structured columnar collections. collection.watch returns a live result set that re-emits only when a relevant change lands row-level matching — no spurious re-renders , with added / removed / changed / moved deltas. // initial snapshot, then re-fires only when an admin is added/changed/removed users.watch { where: { roles: { has: "admin" } } }, { results, added, removed } = renderAdminList results , ; // single-document listener Firebase-style onSnapshot — doc is null on delete orders.watchDoc "o-123", doc = render doc ; Enable the change feed { changefeed: true } for a durable, resumable, ordered stream — and watch then also sees writes from other processes on the same file: js for await const ev of db.changes "orders", { since: lastSeq } { // { seq, collection, id, op: "upsert" | "delete", ts } — resumable by seq } Add the plugins, point them at fields, and they index automatically on every write. Keyword ranking and vector similarity fuse into one ranked list via Reciprocal Rank Fusion. js import { fts } from "@monlite/fts"; import { vector, hybridSearch } from "@monlite/vector"; const db = createDb "./app.db", { allowExtensions: true, plugins: fts { docs: "title", "body" } , vector { docs: { field: "embedding", dimensions: 384 } } , , } ; await db.collection "docs" .search "brown fox" ; // keyword FTS5 await db.collection "docs" .findSimilar { vector: emb, topK: 5 } ; // semantic sqlite-vec const hits = await hybridSearch db.collection "docs" , { // both, fused text: "machine learning", vector: await embed "machine learning" , topK: 10, where: { published: true }, } ; Indexing is linear at scale — verified ingesting 100K documents in ~0.8s and 50K vectors in ~8s no O n² re-index , comfortably backing a 10K–100K-document RAG corpus. js import { kv } from "@monlite/kv"; import { createQueue } from "@monlite/queue"; import { createCron } from "@monlite/cron"; // Redis-like cache: get/set with TTL, atomic locks, counters, sorted sets, pub/sub const cache = kv db ; cache.set "session:42", { user: "ali" }, { ttl: 60 000 } ; if cache.setNX "lock:job:42", 1, { ttl: 30 000 } runOnce ; // atomic lock // durable job queue: retries, backoff, dedupe, concurrency, rate limits const queue = createQueue db, { maxAttempts: 3 } ; queue.process "embed", async job = embed job.payload.text , { concurrency: 4 } ; // persisted scheduler: 5-field cron, time zones, jitter, multi-process safe const cron = createCron db ; cron.schedule "nightly", "0 3 ", = queue.add "cleanup", {} ; The full AI-agent-backend walkthrough https://qataruts.github.io/monlite/guides/ai-agent-backend puts these together with memory + semantic recall into one runtime. The collection API is engine-agnostic. Develop against a local .db ; when you need a networked, multi-writer backend, swap the engine, not your app : js import { createDb } from "@monlite/core"; const db = createDb "app.db" ; // local import { createDb } from "@monlite/postgres"; const db = createDb "postgres://…" ; // server @monlite/postgres https://www.npmjs.com/package/@monlite/postgres runs the entire surface on Postgres documents as JSONB : all CRUD, the full query language, aggregate / groupBy , explain , realtime watch over LISTEN/NOTIFY truly cross-process , full-text search tsvector , vector search pgvector , the job queue SKIP LOCKED , cache, and cron — the same plugins and the same calls. A ready-to-run Docker image bundles Postgres 16 + pgvector, preconfigured. https://hub.docker.com/r/monlite/postgres monlite/postgres | Environment | How | |---|---| | Node 22.5+ | @monlite/core — built-in node:sqlite , zero native build | | Node 18/20 | @monlite/core + better-sqlite3 auto-selected when present | | Browser | @monlite/wasm — same API on SQLite-WASM | | Electron | @monlite/electron — DB in main, same API in renderers over IPC | | Python | pip install monlite — the same , pure stdlib .db file | The Python port is at feature parity — documents transactions, aggregation, change feed , kv, queue, cron, FTS5, and vector search — reading and writing the same file as the Node packages, with a cross-runtime interop suite round-tripping a database between them. So Python ingests/embeds while Node serves , over one file. python from monlite import create db, kv db = create db "app.db" the same file your Node process uses db.collection "users" .find many where={"tags": {"has": "admin"}} kv db .set "session:42", {"user": "ali"}, ttl=60 000 vs. raw SQLite — you'd hand-write the document layer, query translator, FTS/vector wiring, change feed, sync engine, and all the types. monlite is that work, done and tested. vs. MongoDB + Redis + Qdrant — for local / edge / desktop / single-machine work you'd run three services to solve one problem. monlite is one file, one API, zero infrastructure — and scales to Postgres with the same code when you genuinely need a server. vs. Firebase / Supabase — great for shared cloud state, awkward when you need to work offline, ship a CLI, or keep data on-device. monlite is local-first; @monlite/sync adds the cloud when you want it. Full guide at qataruts.github.io/monlite : Getting started · Core — documents https://qataruts.github.io/monlite/core/documents · queries https://qataruts.github.io/monlite/core/queries · aggregation https://qataruts.github.io/monlite/core/aggregation · realtime https://qataruts.github.io/monlite/core/realtime · transactions https://qataruts.github.io/monlite/core/transactions Packages — postgres https://qataruts.github.io/monlite/packages/postgres · fts https://qataruts.github.io/monlite/packages/fts · vector https://qataruts.github.io/monlite/packages/vector · kv https://qataruts.github.io/monlite/packages/kv · queue https://qataruts.github.io/monlite/packages/queue · cron https://qataruts.github.io/monlite/packages/cron · sync https://qataruts.github.io/monlite/packages/sync Guides — AI-agent backend https://qataruts.github.io/monlite/guides/ai-agent-backend · production https://qataruts.github.io/monlite/guides/production · migrations https://qataruts.github.io/monlite/guides/migrations Reference — file format https://qataruts.github.io/monlite/reference/file-format · Python https://qataruts.github.io/monlite/reference/python · benchmarks https://qataruts.github.io/monlite/reference/benchmarks Runnable demos in examples/ /qataruts/monlite/blob/main/examples . The live demo https://qataruts.github.io/monlite/demo runs every package — documents, FTS5, vector search, cache, queue, cron — 100% in the browser on SQLite-WASM, with embeddings computed on-device via Transformers.js. Production-ready and published; the 2.x core API is frozen. The Postgres engine @monlite/postgres https://www.npmjs.com/package/@monlite/postgres runs the entire surface — documents, queries, aggregation, realtime, full-text, vector, queue, kv, and cron — verified against live Postgres. See each package on npm for its current version and changelog. MIT