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RedNotebook AI
FreeOpen SourceAPISelf-Hosted
Most SQL notebook setups split your workflow across at least three tools — a query editor, a BI layer, and a separate AI chat window that knows nothing about your schema. RedNotebook AI is built to collapse that stack into one local-first notebook.
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Get This Tool
License: Apache-2.0 Any use incl. commercial
Local-run terms: Run via Docker, pip install, or source clone; full commercial use permitted under Apache 2.0.
Screenshots 1
Pricing
- Model
- Free
Summary
The tool runs a Next.js frontend over a FastAPI backend and connects to Trino, DuckDB, and eleven other SQL engines, so analysts working across mixed data infrastructure do not need a different client per engine. AI suggestions surface inside the notebook for SQL generation, chart selection, and data profiling — including PII detection — without sending your schema to a third-party SaaS layer. The NotebookLM-style knowledge layer lets you ask questions grounded in your actual query results rather than a generic model context. That said, the project carries a low star count and three open issues with no merged pull requests, which means production stability depends on how closely your use case matches what the maintainer has tested. Teams hitting edge cases in multi-engine joins or complex profiling jobs will be patching source code themselves.
Bottom line: A credible local-first SQL notebook for analysts who want AI assistance without shipping data to an external service — but if your team needs enterprise reliability, a community larger than one maintainer, or battle-tested multi-engine joins under production query volumes, you will outgrow this before you hit your second quarter.
Find your fit
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Pros
- Connects to thirteen SQL engines including Trino and DuckDB from a single notebook interface, so analysts switching between engines do not maintain separate query clients or context.
- Fully self-hosted under Apache 2.0, which means your query results and schema metadata never leave your infrastructure — removing the compliance conversation that blocks SaaS notebook adoption in regulated environments.
- AI SQL and chart suggestions are grounded in your actual query results and schema via a NotebookLM-style knowledge layer, so the model answers questions about your data rather than hallucinating schema structure it has never seen.
- Built-in PII detection inside the profiling workflow, so analysts catch sensitive column exposure during exploration rather than in a downstream audit.
- Notebook snapshots are publishable as shareable artifacts, so results reach stakeholders without requiring them to run the notebook themselves or access the data environment.
Cons
- The repository has one maintainer, a single-digit star count, and open issues with no merged pull requests — which means bugs you hit in production are bugs you fix yourself. Teams that cannot absorb that maintenance burden will move to a tool with an active contributor community before the first incident.
- AI assistance is non-agentic: it suggests SQL and charts inline but does not run multi-step tasks on its own. Teams expecting an agent that investigates data quality issues autonomously or chains queries without manual prompting will hit this ceiling immediately and need a different tool.
- Multi-engine federation at scale has no documented testing evidence beyond what the maintainer has personally validated. Teams running high-volume joins across Trino and DuckDB simultaneously are operating outside confirmed support and will encounter undefined behavior before they find documented fixes.
About
- Platforms
- Docker, Python, Web (Next.js)
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- June 12, 2026
Best For
Who it's for
- Data analysts working with multiple SQL engines
- Teams needing local-first notebook environments
- Users combining SQL workspaces with AI assistance
What it does well
- Interactive SQL data exploration and visualization
- Data profiling and PII detection in notebooks
- Grounded Q&A over query results and schemas
- Publishing shareable notebook snapshots
Integrations
Compare RedNotebook AI
Frequently Asked Questions
- Is RedNotebook AI free?
- RedNotebook AI offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is RedNotebook AI open source?
- Yes. RedNotebook AI is open source.
- Does RedNotebook AI have an API?
- Yes. RedNotebook AI exposes a developer API. See the official documentation at https://github.com/sanniheruwala/rednotebookai for details.
- Can I self-host RedNotebook AI?
- Yes. RedNotebook AI supports self-hosting on your own infrastructure.
- What platforms does RedNotebook AI support?
- RedNotebook AI is available on: Docker, Python, Web (Next.js).
People also compared
RedNotebook AI is an open-source data notebook that pairs a SQL workspace with AI assistance across thirteen SQL engines, including Trino and DuckDB. The core workflow is notebook-first: you write and run SQL, get AI-suggested rewrites or chart types inline, and the results feed a knowledge layer you can query in natural language — closer to asking a question about what your data actually returned than prompting a model that has never seen your tables.
The differentiating feature is local-first, self-hosted operation under Apache 2.0 license. You deploy via Docker or Python, your data stays on your infrastructure, and no query results leave your environment to power the AI features. For teams working under data residency requirements or internal security policies that block SaaS notebook tools, this architecture removes the negotiation entirely.
The tool fits a data analyst or small team that wants a single environment for querying, visualizing, profiling, and sharing notebook snapshots — without a procurement process. Where it breaks: the project has one active maintainer, a minimal community footprint, and open issues with no pull request activity. Complex use cases — multi-engine federated queries at scale, custom profiling pipelines, or integration into a broader data platform — will require direct source code work. Teams on those paths will eventually evaluate maintained alternatives with larger contributor bases.
Installation follows standard Docker Compose or Python package routes, with a Dockerfile and docker-compose.yml included in the repository. The API is available for programmatic access, and the frontend is built on Next.js with shadcn/ui components. Notebook snapshots can be published as shareable artifacts, supporting light collaboration without a shared server requirement.
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