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sinaptik-ai/pandas-ai
Chat with your database or your datalake (SQL, CSV, parquet). PandasAI makes data analysis conversational using LLMs and RAG.
PandasAI lets you query DataFrames, CSVs, and SQL databases with natural language by having an LLM generate and execute Python/SQL code against your data. It targets data analysts and BI-adjacent users who want to skip writing pandas or SQL for exploratory questions. The v3 rewrite shifted from a monolithic package to a plugin architecture with separate LLM and connector extensions.
The Docker sandbox for code execution is the right call — LLM-generated code running in-process is a real attack surface, and offering an isolated execution environment out of the box is more than most comparable tools do. The extension/plugin split (connectors, LLMs, vector stores all as separate packages) keeps the core install weight low and makes swapping LLM providers a one-liner. Support for querying multiple DataFrames in a single chat call handles the join-like questions that trip up simpler text-to-SQL tools. Test coverage is decent for this category — unit tests plus integration tests against real file formats, not just mocked responses.
Code generation + execution is fundamentally unreliable for anything beyond toy queries: the LLM hallucinates column names, gets aggregation logic wrong on edge cases, and there's no easy way to audit what code ran without digging into internals. The Python version ceiling of 3.11 is a real friction point in 2025 — most teams are on 3.12+ and hitting this will cause environment management headaches. The EE licensing split buried inside both `pandasai/ee/` and `extensions/ee/` means you don't know which features are commercial until you try to use them; the docs don't make this obvious upfront. Telemetry is enabled by default (`pandasai/helpers/telemetry.py` exists and is wired in), which will be a non-starter for anyone with data governance requirements who doesn't read the fine print.