Mako

The AI-native SQL Client.

The Cursor for Data. Connect to any database, query with AI, and build live dashboards -- all from your browser.

Stop wrestling with complex SQL and slow, bloated database tools. Write queries in plain English, get instant results, and turn them into interactive dashboards with cross-filtering and scheduled refresh.

🚀 Why Mako?

A modern SQL client built for the AI era, replacing slow desktop tools with a fast, collaborative, AI-powered experience.

• ✨ AI Query Generation: Write queries in natural language. Our schema-aware AI generates optimized SQL instantly.
  • Replaces: DataGrip, DBeaver, Postico
• 📊 AI Dashboards: Build interactive dashboards from conversation. Cross-filtering, scheduled data refresh, Parquet materialization -- powered by DuckDB in the browser.
  • Replaces: Metabase, Looker, manual BI pipelines
• 🧱 dbt Transforms: Build, run, and schedule dbt Core projects in-app -- file IDE, jobs, run history, lineage, and GitHub sync.
  • Replaces: dbt Cloud
• ⚛️ React Apps: Ask the agent to build live React apps wired to your data through secure, credential-free bindings.
  • Replaces: Lovable, v0, internal-tool builders
• 🕓 Version History: Every console and dashboard save is an immutable snapshot you can browse and restore.
  • Replaces: Lost SQL files, manual backups
• 🖥️ Mako Desktop: Native app that bundles a local agent so localhost databases work out of the box.
  • Replaces: SSH tunnels and bastion hops for local DBs
• 👥 Team Collaboration: Share connections, version-control queries, and work together in real-time.
  • Replaces: Passing credentials around, lost SQL files
• ⚡ Blazing Fast: No Java or Electron bloat. Opens instantly in your browser and runs smooth.
  • Replaces: Slow desktop database tools

📸 Screenshots

AI-powered console — ask in plain English, get a verified query and live results.

Transforms (dbt) — build, run, and schedule dbt Core projects with a file IDE, jobs, run history, and lineage.

Apps — build live React apps wired to your data, rendered in a sandboxed preview.

🔌 Integrations

Databases

Data Connectors

Sync external SaaS data into Mako's data warehouse for querying and dashboards.

🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│  Frontend (React + Vite)                                │
│  ┌──────────┐ ┌──────────────┐ ┌─────────────────────┐ │
│  │ Console  │ │  Dashboards  │ │   AI Chat (Vercel   │ │
│  │ (Monaco) │ │  (DuckDB +   │ │    AI SDK)          │ │
│  │          │ │   Mosaic)    │ │                     │ │
│  └──────────┘ └──────────────┘ └─────────────────────┘ │
│           ▲          ▲                   ▲              │
│           │     Parquet/Arrow            │              │
│           │     via OPFS cache           │              │
└───────────┼──────────┼──────────────────┼──────────────┘
            │          │                  │
┌───────────┼──────────┼──────────────────┼──────────────┐
│  API (Hono + Node.js)                                  │
│  ┌──────────────────────────────────────────────────┐  │
│  │ Unified Agent (expertise modes: Query /          │  │
│  │   Dashboard / Sync Flow / React App / Transforms │  │
│  │   / Explore, switched via enable_mode)           │  │
│  └──────────────────────────────────────────────────┘  │
│  ┌──────────────┐ ┌───────────────┐ ┌──────────────┐  │
│  │  DB Drivers   │ │  Connectors   │ │  Dashboard   │  │
│  │  (9 drivers)  │ │  (8 sources)  │ │  Engine      │  │
│  │              │ │               │ │  (DuckDB     │  │
│  │              │ │               │ │   + Parquet) │  │
│  └──────────────┘ └───────────────┘ └──────────────┘  │
│                          ▲                             │
│                    ┌─────┴──────┐                      │
│                    │  Inngest   │ (scheduled refresh,   │
│                    │            │  incremental sync)    │
│                    └────────────┘                      │
└────────────────────────────────────────────────────────┘
            │                │
     ┌──────┴──────┐  ┌─────┴──────────┐
     │  MongoDB    │  │  User DBs      │
     │  (metadata, │  │  (PG, BQ, CH,  │
     │   warehouse)│  │   MySQL, etc.) │
     └─────────────┘  └────────────────┘

Key technology choices:

• DuckDB (both server-side via @duckdb/node-api and browser-side via @duckdb/duckdb-wasm): powers dashboard SQL execution, Parquet artifact generation, and in-browser cross-filtering with OPFS caching
• Mosaic (@uwdata/mosaic-core): coordinates cross-filtering across dashboard widgets
• Apache Arrow / Parquet: server materializes query results into Parquet, served to browser as Arrow IPC for zero-copy rendering
• Inngest: event-driven job queues for scheduled dashboard refresh and incremental data sync
• Hono: lightweight, fast HTTP framework for the API
• Monaco Editor: VS Code's editor for the SQL console
• Vercel AI SDK: multi-provider LLM abstraction (OpenAI, Anthropic, Google)

📊 Dashboard Engine

Dashboards are a core feature. The AI agent creates interactive dashboards from natural language:

1. Agent creates a dashboard spec with widgets, layouts, and SQL queries
2. Server materializes query results into Parquet artifacts (stored on filesystem, GCS, or S3)
3. Browser loads Parquet data into DuckDB-WASM, cached in OPFS for instant reloads
4. Mosaic cross-filtering lets users click on one chart to filter all others
5. Inngest cron keeps data fresh with scheduled re-materialization and stale-run detection

Dashboard Artifact Storage

Dashboard materialization stores Parquet artifacts on the backend. Three storage backends:

• filesystem -- default; stores files on local disk
• gcs -- Google Cloud Storage
• s3 -- S3-compatible bucket

DASHBOARD_ARTIFACT_STORE=filesystem

# Optional shared settings
DASHBOARD_ARTIFACT_PREFIX=dashboards
DASHBOARD_ARTIFACT_DIR=/absolute/path/to/artifacts  # filesystem only

Google Cloud Storage

DASHBOARD_ARTIFACT_STORE=gcs
GCS_DASHBOARD_BUCKET=your-bucket-name
DASHBOARD_ARTIFACT_PREFIX=dashboard-artifacts/prod

See the docs for full GCS/S3 provisioning instructions.

🛠️ Quick Start

1. Clone & Install

   bash

   Copy

   git clone https://github.com/mako-ai/mako.git
   cd mako
   pnpm install
   

2. Configure Environment Copy .env.example (if available) or create .env:

   env

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   # Local development connects to the shared `dev` database, an Atlas DB that is
   # refreshed nightly from production. Grab the `dev` connection string from the
   # team vault. (`staging` backs non-migration PR previews; never point local at
   # `production`.)
   DATABASE_URL=mongodb+srv://<user>:<password>@<cluster>.mongodb.net/dev
   ENCRYPTION_KEY=your_32_character_hex_key_for_encryption
   WEB_API_PORT=8080
   BASE_URL=http://localhost:8080
   CLIENT_URL=http://localhost:5173
   

3. Start Services

   bash

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   # Start the local notebook Python kernel so notebook `code` cells run
   # locally. Requires the KERNEL_* vars from .env.example in your .env.
   # (The dev database is hosted MongoDB Atlas, set via DATABASE_URL.)
   pnpm run docker:up
   
   # Start the full stack (API + App + Inngest)
   pnpm run dev
   

4. Analyze

   • Open http://localhost:5173 to access the app.
   • Add a Data Source (e.g., Stripe or Close.com).
   • Use the chat interface to ask questions about your data.

🌐 IP Whitelisting

If your database requires IP whitelisting, add the following static IP to your allowlist:

34.79.190.46