Onefilellm

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OneFileLLM is an AI Agents & Automation tool that aggregates data from various sources into a single XML file for easier LLM ingestion. It supports local files, GitHub repos, web pages, PDFs, and YouTube transcripts.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is onefilellm?

OneFileLLM is a command-line tool designed to simplify data aggregation for Large Language Models (LLMs). It automates the process of collecting information from diverse sources, including local files, GitHub repositories, web pages, PDFs, and YouTube transcripts. The tool then combines this multi-source data into a single, structured XML output, which is automatically copied to your clipboard. This structured format is optimized for LLM context, making it easier for models to process and understand complex information. OneFileLLM also features an alias system for creating simple and complex shortcuts to frequently used inputs, and advanced web crawling options for comprehensive documentation sites and academic sources.

Best used for

Ideal for developers and data scientists who need to gather diverse information from various sources, prepare structured context for LLMs, and automate data collection workflows. Especially valuable for researchers analyzing academic papers, developers reviewing GitHub projects, or anyone needing to consolidate web documentation for AI processing.

Common actions

aggregate data

scrape content

prepare LLM context

structure information

automate data collection

low-code/no-codedeepfakecollaborationautomated workflowworkflowsopen-sourceface swappinggithub copilot"AI Agents"

Capabilities

Key features

Aggregate multi-source data
Scrape GitHub repos
Extract web content
Process YouTube transcripts
Generate structured XML
Clipboard output
Alias system

Target Audience

developerdata scientistcontent creatorresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of input sources does OneFileLLM support?

OneFileLLM supports a wide range of input sources including local files and directories, GitHub repositories, issues, and pull requests, web pages with advanced crawling options, academic sources like ArXiv papers, DOIs, and PMIDs, YouTube transcripts, and input streams like stdin and clipboard.

How does OneFileLLM structure its output for LLMs?

All output from OneFileLLM is encapsulated in a structured XML format. This includes a root <onefilellm_output> tag, with each source having its own <source> tag containing type and additional attributes, and the extracted content within a <[content_type]> tag. This format is designed for better LLM processing.

Can I customize web crawling behavior with OneFileLLM?

Yes, OneFileLLM offers extensive web crawling options. You can specify maximum crawl depth and pages, user agents, request delays, include/exclude URL patterns, timeouts, and choose whether to include images, clean HTML, or strip JavaScript/CSS. It also supports respecting robots.txt and concurrent requests.

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Also listed in

This tool also appears in

Data & Analytics › Data Cleaning & Prep Data & Analytics › Data Pipelines & Integration Data & Analytics › Web Scraping & Extraction

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