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Dev tools · January 14, 2026

Datatune

Modern AI tools use SQL/Code generation agents or RAG to access the user data to perform transformations. However, the drawback is that this doesn't provide row-level intelligence, especially in case of semantic intelligence is required to understand how to perform the transfromations on each row of the data. We've released Datatune (https://github.com/vitalops/datatune) as a tool to let users connect entire user Data with LLMs and Agents, to help users access their entire data with just a prompt. While building Agents for a customer who had large amounts of data, we…

Datatune launched on January 14, 2026 with 38 votes, #594 of 14,585 launches that month and more than 95% of that year's launches.

Visit github.com ↗Datatune alternativestop 4% of January 2026

What it does

Modern Data tools rely on SQL or code generation to access the Data. Datatune is engineered to give complete access to the user data to LLMs and Agents. Datatune Solves the issue of context length and rate limits while connecting your large amounts of data with LLMs, while performing data operations scalably, using distributed computing.

Agentic data transformation on infinite amounts of data - vitalops/datatune

Datatune is not just another Text to SQL tool. With datatune, LLMs and Agents can have full access to infinite amount of data, and apply semantic intelligence in every record. Datatune's framework and evaluation are described in our peer-reviewed paper, "Beyond Context Windows: Data Transformations at Scale with LLMs" , presented at DATA 2026 (15th International Conference on Data Science, Technology and Applications), Porto, Portugal. pip install datatune Quick Start import datatune as dt from datatune . llm . llm import OpenAI import dask . dataframe as dd llm = OpenAI ( model_name = "gpt-3.5-turbo" ) df = dd . read_csv ( "products.csv" ) # Extract categories using natural language mapped…from github.com

In the maker’s words, at launch

Modern AI tools use SQL/Code generation agents or RAG to access the user data to perform transformations. However, the drawback is that this doesn't provide row-level intelligence, especially in case of semantic intelligence is required to understand how to perform the transfromations on each row of the data.…

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