
onefilellm
Specify a github or local repo, github pull request, arXiv or Sci-Hub paper, Youtube transcript or documentation URL on the web and scrape into a text file and clipboard for easier LLM ingestion
Stars: 1691

OneFileLLM is a command-line tool that streamlines the creation of information-dense prompts for large language models (LLMs). It aggregates and preprocesses data from various sources, compiling them into a single text file for quick use. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, token count reporting, and XML encapsulation of output for improved LLM performance. Users can easily access private GitHub repositories by generating a personal access token. The tool's output is encapsulated in XML tags to enhance LLM understanding and processing.
README:
Content Aggregator for LLMs - Aggregate and structure multi-source data into a single XML file for LLM context.
OneFileLLM is a command-line tool that automates data aggregation from various sources (local files, GitHub repos, web pages, PDFs, YouTube transcripts, etc.) and combines them into a single, structured XML output that's automatically copied to your clipboard for use with Large Language Models.
git clone https://github.com/jimmc414/onefilellm.git
cd onefilellm
pip install -r requirements.txt
OneFileLLM is also available as a pip package. You can install it directly and use both the CLI and Python API without cloning the repository:
pip install onefilellm
This project can also be installed as a command-line tool, which allows you to run onefilellm
directly from your terminal.
To install the CLI, run the following command in the project's root directory:
pip install -e .
This will install the package in "editable" mode, meaning any changes you make to the source code will be immediately available to the command-line tool.
Once installed, you can use the onefilellm
command instead of python onefilellm.py
.
Synopsis:
onefilellm [OPTIONS] [INPUT_SOURCES...]
Example:
onefilellm ./docs/ https://github.com/user/project/issues/123
All other command-line arguments and options work the same as the script-based approach.
For GitHub API access (recommended):
export GITHUB_TOKEN="your_personal_access_token"
After installing via pip, OneFileLLM can be invoked directly from Python code.
from onefilellm import run
# Process inputs programmatically
run(["./docs/"])
usage: onefilellm.py [-h] [-c]
[-f {text,markdown,json,html,yaml,doculing,markitdown}]
[--alias-add NAME [COMMAND_STRING ...]]
[--alias-remove NAME] [--alias-list] [--alias-list-core]
[--crawl-max-depth CRAWL_MAX_DEPTH]
[--crawl-max-pages CRAWL_MAX_PAGES]
[--crawl-user-agent CRAWL_USER_AGENT]
[--crawl-delay CRAWL_DELAY]
[--crawl-include-pattern CRAWL_INCLUDE_PATTERN]
[--crawl-exclude-pattern CRAWL_EXCLUDE_PATTERN]
[--crawl-timeout CRAWL_TIMEOUT] [--crawl-include-images]
[--crawl-no-include-code] [--crawl-no-extract-headings]
[--crawl-follow-links] [--crawl-no-clean-html]
[--crawl-no-strip-js] [--crawl-no-strip-css]
[--crawl-no-strip-comments] [--crawl-respect-robots]
[--crawl-concurrency CRAWL_CONCURRENCY]
[--crawl-restrict-path] [--crawl-no-include-pdfs]
[--crawl-no-ignore-epubs] [--help-topic [TOPIC]]
[inputs ...]
OneFileLLM - Content Aggregator for LLMs
positional arguments:
inputs Input paths, URLs, or aliases to process
options:
-h, --help show this help message and exit
-c, --clipboard Process text from clipboard
-f {text,markdown,json,html,yaml,doculing,markitdown}, --format {text,markdown,json,html,yaml,doculing,markitdown}
Override format detection for text input
--help-topic [TOPIC] Show help for specific topic (basic, aliases,
crawling, pipelines, examples, config)
## Quick Start Examples
### Local Files and Directories
```bash
python onefilellm.py research_paper.pdf config.yaml src/
python onefilellm.py *.py requirements.txt docs/ README.md
python onefilellm.py notebook.ipynb --format json
python onefilellm.py large_dataset.csv logs/ --format text
python onefilellm.py https://github.com/microsoft/vscode
python onefilellm.py https://github.com/openai/whisper/tree/main/whisper
python onefilellm.py https://github.com/microsoft/vscode/pull/12345
python onefilellm.py https://github.com/kubernetes/kubernetes/issues?state=all
python onefilellm.py https://github.com/kubernetes/kubernetes/issues?state=open
python onefilellm.py https://github.com/kubernetes/kubernetes/issues?state=closed
You can retrieve issues for a repository by specifying the state
query parameter.
Use state=all
(default) to fetch all issues, state=open
for open issues only,
or state=closed
for closed issues.
Is it possible to use this tool with different branches on a GitHub repository?
Yes. When you supply a GitHub URL that includes a branch (e.g., https://github.com/openai/whisper/tree/main/whisper), the tool parses the tree/
portion and sends the request with a ref
parameter so that the specified branch or tag is retrieved.
python onefilellm.py https://docs.python.org/3/tutorial/
python onefilellm.py https://react.dev/learn/thinking-in-react
python onefilellm.py https://docs.stripe.com/api
python onefilellm.py https://kubernetes.io/docs/concepts/
python onefilellm.py https://www.youtube.com/watch?v=dQw4w9WgXcQ
python onefilellm.py https://arxiv.org/abs/2103.00020
python onefilellm.py arxiv:1706.03762 PMID:35177773
python onefilellm.py doi:10.1038/s41586-021-03819-2
python onefilellm.py https://github.com/jimmc414/hey-claude https://modelcontextprotocol.io/llms-full.txt https://github.com/anthropics/anthropic-sdk-python https://github.com/anthropics/anthropic-cookbook
python onefilellm.py https://github.com/openai/whisper/tree/main/whisper https://www.youtube.com/watch?v=dQw4w9WgXcQ ALIAS_MCP
python onefilellm.py https://github.com/microsoft/vscode/pull/12345 https://arxiv.org/abs/2103.00020
python onefilellm.py https://github.com/kubernetes/kubernetes/issues https://pytorch.org/docs
python onefilellm.py --clipboard --format markdown
cat large_dataset.json | python onefilellm.py - --format json
curl -s https://api.github.com/repos/microsoft/vscode | python onefilellm.py -
echo 'Quick analysis task' | python onefilellm.py -
python onefilellm.py --alias-add mcp "https://github.com/anthropics/mcp"
python onefilellm.py --alias-add modern-web \
"https://github.com/facebook/react https://reactjs.org/docs/ https://github.com/vercel/next.js"
# Create placeholders with {}
python onefilellm.py --alias-add gh-search "https://github.com/search?q={}"
python onefilellm.py --alias-add gh-user "https://github.com/{}"
python onefilellm.py --alias-add arxiv-search "https://arxiv.org/search/?query={}"
# Use placeholders dynamically
python onefilellm.py gh-search "machine learning transformers"
python onefilellm.py gh-user "microsoft"
python onefilellm.py arxiv-search "attention mechanisms"
python onefilellm.py --alias-add ai-research \
"arxiv:1706.03762 https://github.com/huggingface/transformers https://pytorch.org/docs"
python onefilellm.py --alias-add k8s-ecosystem \
"https://github.com/kubernetes/kubernetes https://kubernetes.io/docs/ https://github.com/istio/istio"
# Combine multiple aliases with live sources
python onefilellm.py ai-research k8s-ecosystem modern-web \
conference_notes.pdf local_experiments/
python onefilellm.py --alias-list # Show all aliases
python onefilellm.py --alias-list-core # Show core aliases only
python onefilellm.py --alias-remove old-alias # Remove user alias
cat ~/.onefilellm_aliases/aliases.json # View raw JSON
--alias-add NAME [COMMAND_STRING ...]
Add or update a user-defined alias. Multiple arguments
after NAME will be joined as COMMAND_STRING.
--alias-remove NAME Remove a user-defined alias.
--alias-list List all effective aliases (user-defined aliases
override core aliases).
--alias-list-core List only pre-shipped (core) aliases.
Web Crawler Options:
--crawl-max-depth CRAWL_MAX_DEPTH
Maximum crawl depth (default: 3)
--crawl-max-pages CRAWL_MAX_PAGES
Maximum pages to crawl (default: 1000)
--crawl-user-agent CRAWL_USER_AGENT
User agent for web requests (default:
OneFileLLMCrawler/1.1)
--crawl-delay CRAWL_DELAY
Delay between requests in seconds (default: 0.25)
--crawl-include-pattern CRAWL_INCLUDE_PATTERN
Regex pattern for URLs to include
--crawl-exclude-pattern CRAWL_EXCLUDE_PATTERN
Regex pattern for URLs to exclude
--crawl-timeout CRAWL_TIMEOUT
Request timeout in seconds (default: 20)
--crawl-include-images
Include image URLs in output
--crawl-no-include-code
Exclude code blocks from output
--crawl-no-extract-headings
Exclude heading extraction
--crawl-follow-links Follow links to external domains
--crawl-no-clean-html
Disable readability cleaning
--crawl-no-strip-js Keep JavaScript code
--crawl-no-strip-css Keep CSS styles
--crawl-no-strip-comments
Keep HTML comments
--crawl-respect-robots
Respect robots.txt (default: ignore for backward
compatibility)
--crawl-concurrency CRAWL_CONCURRENCY
Number of concurrent requests (default: 3)
--crawl-restrict-path
Restrict crawl to paths under start URL
--crawl-no-include-pdfs
Skip PDF files
--crawl-no-ignore-epubs
Include EPUB files
python onefilellm.py https://docs.python.org/3/ \
--crawl-max-depth 4 --crawl-max-pages 800 \
--crawl-include-pattern ".*/(tutorial|library|reference)/" \
--crawl-exclude-pattern ".*/(whatsnew|faq)/"
python onefilellm.py https://docs.aws.amazon.com/ec2/ \
--crawl-max-depth 3 --crawl-max-pages 500 \
--crawl-include-pattern ".*/(UserGuide|APIReference)/" \
--crawl-respect-robots --crawl-delay 0.5
python onefilellm.py https://arxiv.org/list/cs.AI/recent \
--crawl-max-depth 2 --crawl-max-pages 100 \
--crawl-include-pattern ".*/(abs|pdf)/" \
--crawl-include-pdfs --crawl-delay 1.0
python onefilellm.py ai-research protein-folding | \
llm -m claude-3-haiku "Extract key methodologies and datasets" | \
llm -m claude-3-sonnet "Identify experimental approaches" | \
llm -m gpt-4o "Compare methodologies across papers" | \
llm -m claude-3-opus "Generate novel research directions"
python onefilellm.py \
https://github.com/competitor1/product \
https://competitor1.com/docs/ \
https://competitor2.com/api/ | \
llm -m claude-3-haiku "Extract features and capabilities" | \
llm -m gpt-4o "Compare and identify gaps" | \
llm -m claude-3-opus "Generate strategic recommendations"
0 9 * * * python onefilellm.py \
https://arxiv.org/list/cs.AI/recent \
https://arxiv.org/list/cs.LG/recent | \
llm -m claude-3-haiku "Extract significant papers" | \
llm -m claude-3-sonnet "Summarize key developments" | \
mail -s "Daily AI Research Brief" [email protected]
All output is encapsulated in XML for better LLM processing:
<onefilellm_output>
<source type="[source_type]" [additional_attributes]>
<[content_type]>
[Extracted content]
</[content_type]>
</source>
</onefilellm_output>
- Local: Files and directories
- GitHub: Repositories, issues, pull requests
- Web: Pages with advanced crawling options
- Academic: ArXiv papers, DOIs, PMIDs
- Multimedia: YouTube transcripts
- Streams: stdin, clipboard
-
ofl_repo
- OneFileLLM GitHub repository -
ofl_readme
- OneFileLLM README file -
gh_search
- GitHub search with placeholder -
arxiv_search
- ArXiv search with placeholder
-
Alias Storage:
~/.onefilellm_aliases/aliases.json
-
Environment Variables:
-
GITHUB_TOKEN
- GitHub API access token -
OFFLINE_MODE
- Set to1
to skip network operations - Can use
.env
file in project root
-
python onefilellm.py --help-topic basic # Input sources and basic usage
python onefilellm.py --help-topic aliases # Alias system with real examples
python onefilellm.py --help-topic crawling # Web crawler patterns and ethics
python onefilellm.py --help-topic pipelines # 'llm' tool integration workflows
python onefilellm.py --help-topic examples # Advanced usage patterns
python onefilellm.py --help-topic config # Environment and configuration
-
YouTube transcript errors: Fetching YouTube transcripts requires the
yt-dlp
tool. If you see errors aboutyt-dlp
not being found or failing, install it with:pip install yt-dlp
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