PandasAI
Verified Oct 2, 2026 ActiveOpen-source Python library for asking questions of dataframes and databases in plain English, plus a hosted dashboard product.
Listed on EffectHub ·
Pricing
- Pricing model
- Freemium
The library is free under the MIT licence; you pay your own LLM provider for model calls. The hosted dashboard is a paid product, but its homepage renders client-side and shows no figures to a plain fetch, and third-party sources quote euro prices, so no USD price is recorded here.
Use tool ↗About PandasAI
PandasAI is a Python library that turns natural-language questions into Python and SQL code, runs it against your data and returns tables, charts or text answers. It connects to CSV and Excel files, Postgres, MySQL, BigQuery, Snowflake, Databricks and other sources, supports a semantic layer for describing datasets, and works with any model through LiteLLM. The core is MIT-licensed, with an enterprise-licensed directory for extra features, and the vendor also sells a hosted business-intelligence dashboard built on it. It suits analysts and data scientists who want conversational access to data without hand-writing every query.
- Platforms
- Web · API
- API
- Yes
- Last verified
- Oct 2, 2026
Verified sources
- Docs: library turns natural-language questions into Python and SQL, supports many data sources, MIT licence except the enterprise directorydocs.pandas-ai.com ↗
- Source repository, MIT-style licence and copyright held by Sinaptik GmbHgithub.com ↗
- Vendor homepage presents an AI dashboard for business intelligencepandas-ai.com ↗
Is PandasAI legit?
We don’t label tools safe or unsafe. Below are the records we checked ourselves, each with the date we read it.
- Last checked
- Oct 2, 2026The site answered
- On the market since
- 2023The vendor's own material, read by us
- Operated by
- Sinaptik GmbH, GermanyOur research, card verified Oct 2, 2026
Pros & cons of PandasAI
A practical way to put a natural-language layer on top of pandas data or a SQL database, especially for analysts who know Python. It runs generated code, so the sandbox and privacy guidance in the docs deserve a careful read before pointing it at sensitive data. The hosted dashboard is a different, paid product, and its pricing is not visible without a browser.
Pros
- MIT-licensed core library with an active public repository
- Works with many models through LiteLLM
- Connectors for common files and warehouses
- Semantic layer lets you describe columns and metrics
Cons
- Answers depend on the model and can be wrong for ambiguous questions
- Some features sit behind a separate enterprise licence
- Hosted dashboard pricing is not published in a readable form
Best for
- Data scientists who want to query dataframes by chat inside notebooks
- Analysts who need quick charts and answers from SQL or CSV sources
- Teams that want an open-source base they can self-host with their own model
Not for
- Anyone who cannot accept LLM-generated code executing against their data
- Non-technical users who want a no-code app and will not touch Python
Watch out: The library asks an LLM to write code that then runs on your machine or sandbox; use the documented sandbox options for untrusted data, and remember that data context sent to your model provider is billed by that provider.
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