PandasAI is a Python library that allows users to query datasets using natural language. By leveraging large language models (LLMs), it assists both technical and non-technical individuals in performing data analysis, executing complex queries, and creating visualizations through simple conversation.
- Cross-dataframe query support
- Secure Docker sandbox option
- Multiple LLM provider compatibility via LiteLLm
- Cross-dataframe query support
- Secure Docker sandbox option
- Multiple LLM provider compatibility via LiteLLm
This article introduces agentic TRACE, an open-source framework designed to build LLM-powered data analysis agents that eliminate data hallucinations. TRACE shifts the LLM's role from analyst to orchestrator, ensuring all computations are deterministic and data-driven. The framework achieves this by having the LLM work with metadata instead of raw data, relying on the database as the source of truth, and providing a complete audit trail. Example use cases demonstrate the system's ability to deliver verifiable results on inexpensive models like Gemini 3.1 Flash Lite. The author provides a quick start guide and encourages contributions to the project.
agentic_TRACE is a framework designed to build LLM-powered data analysis agents that prioritize data integrity and auditability. It addresses the risks associated with directly feeding data to LLMs, such as fabrication, inaccurate calculations, and context window limitations. The core principle is to separate the LLM's orchestration role from the actual data processing, which is handled by deterministic tools.
This approach ensures prompts remain concise, minimizes hallucination risks, and provides a complete audit trail of data transformations. The framework is domain-agnostic, allowing users to extend it with custom tools and data sources for specific applications. A working example, focusing on stock market analysis, demonstrates its capabilities.
This approach ensures prompts remain concise, minimizes hallucination risks, and provides a complete audit trail of data transformations. The framework is domain-agnostic, allowing users to extend it with custom tools and data sources for specific applications. A working example, focusing on stock market analysis, demonstrates its capabilities.
This article introduces agentic TRACE, an open-source framework designed to build LLM-powered data analysis agents that eliminate data hallucinations. TRACE shifts the LLM's role from analyst to orchestrator, ensuring the LLM never directly touches the data. All computations are deterministic and executed by code, using the database as the single source of truth. The framework emphasizes auditability, security, and the ability to run effectively on inexpensive models. The author provides examples and a quick start guide for implementing TRACE, highlighting its potential for building verifiable agents across various data domains.
An analysis of the current LLM landscape in 2026, focusing on the shift from 'vibe coding' to more efficient and controlled workflows for software development and data analysis. The author advocates for tools like AI Studio and OpenCode, and discusses the strengths of models like Gemini 2.5 Pro and Claude Sonnet.
"Talk to your data. Instantly analyze, visualize, and transform."
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
Analyzia is a data analysis tool that allows users to talk to their data, analyze, visualize, and transform CSV files using AI-powered insights without coding. It features natural language queries, Google Gemini integration, professional visualizations, and interactive dashboards, with a conversational interface that remembers previous questions. The tool requires Python 3.11+, a Google API key, and uses Streamlit, LangChain, and various data visualization libraries
A technical blog post about setting up JupyterLab and integrating it with OpenWebUI's code interpreter feature, enabling the LLM to execute and generate code for tasks such as exploratory data analysis.
Google has enhanced Google Sheets with an AI-powered upgrade using its Gemini technology. This update allows users to automatically convert spreadsheets into charts, identify trends, and create advanced visualizations like heatmaps. Users can interact with the Gemini feature directly through a chat interface within Sheets.
Quadratic is a modern spreadsheet that combines the familiarity of a spreadsheet with the power of code, allowing you to work with data and code collaboratively in real-time. It supports popular programming languages like Python, SQL, and JavaScript, and offers features such as dynamic charts, APIs, multi-line formulas, and AI integration.
