What is OneFileLLM?
Feeding complex information from multiple sources into Large Language Models often involves tedious manual work – finding, downloading, converting, and combining data before you can even start crafting your prompt. OneFileLLM is a command-line utility designed specifically to automate this data aggregation pipeline. It intelligently fetches, processes, and consolidates content from local files, code repositories, academic papers, web documentation, and more, delivering a single, structured text file directly to your clipboard, ready for LLM interaction. This lets you spend less time wrangling data and more time getting value from your AI assistants.
Key Features
🌐 Unify Disparate Sources: Automatically fetch and process data from local files/directories, GitHub repositories (including specific PRs and issues), ArXiv papers, Sci-Hub papers (via DOI/PMID), YouTube video transcripts, and web pages.
✨ Detect Sources Automatically: Simply provide a path, URL, or identifier, and OneFileLLM intelligently determines the source type and applies the correct processing logic.
📄 Handle Multiple File Formats: Natively processes various file types commonly found in projects and research, including
.py,.js,.md,.html,.ipynb(Jupyter Notebooks),.pdf, and more, extracting relevant text content.🕸️ Crawl Web Documentation: Scrape content not just from a starting URL but also from linked pages up to a configurable depth (
max_depth).⚙️ Preprocess Text Intelligently: Offers options for text cleaning, including stopword removal and lowercasing, and provides both compressed (cleaned) and uncompressed outputs.
🏷️ Structure Output with XML: Encapsulates the aggregated content within clear XML tags, indicating the source and type of each data chunk.
📋 Copy Output to Clipboard Automatically: Places the complete, uncompressed text output directly onto your system clipboard.
📊 Report Token Counts: Calculates and displays the estimated token count (using
tiktoken) for both the compressed and uncompressed outputs.🚫 Exclude Unwanted Content: Configure patterns to exclude specific files (like auto-generated code or test files) and entire directories from processing.
Use Cases
Codebase Comprehension for Developers: You need to understand a complex GitHub repository to contribute a fix or feature. Instead of manually browsing files, run OneFileLLM on the repo URL. It gathers code files (respecting your configured extensions and exclusions), READMEs, and potentially relevant documentation, placing it all into your clipboard. You can then ask an LLM questions like "Explain the main purpose of the
XYZmodule" or "Where is user authentication handled in this codebase?" using the aggregated context.Research Paper Analysis for Academics: You're exploring a new research area and have several ArXiv papers and PDFs stored locally. Point OneFileLLM to each ArXiv URL, DOI, or local PDF file path sequentially or combine them in a directory. The tool extracts the text from each paper, concatenates it, and provides it ready for your LLM. You can then prompt the LLM to "Summarize the key findings across these papers regarding topic Y" or "Identify the methodologies used in these studies."
Troubleshooting with Documentation and Issues: You're debugging an issue related to a specific GitHub library. Provide OneFileLLM with the URL of a relevant GitHub issue. It can pull the issue description, comments, and the relevant repository code, giving your LLM comprehensive context to help diagnose the problem or suggest solutions based on both the discussion and the actual codebase structure.
Conclusion
Stop wrestling with scattered data sources when preparing context for Large Language Models. OneFileLLM acts as your efficient data aggregation assistant, pulling together code, research, documentation, and discussions from diverse locations into a single, ready-to-use package. By automating the fetching, processing, and formatting, it saves you valuable time and allows you to construct more informed, context-rich prompts, ultimately helping you leverage the full capabilities of your LLMs more effectively.
More information on OneFileLLM
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OneFileLLM was manually vetted by our editorial team and was first featured on 2025-04-18.
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