PocketFlow-Tutorial-Codebase-Knowledge
Generates tutorials from codebases using LLMs
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Decision brief
PocketFlow-Tutorial-Codebase-Knowledge is a tool designed to generate comprehensive tutorial documents from software project codebases using large language models.
Good fit when
- - When you need detailed and automatically generated documentation for complex codebases, ensuring that the tutorials are up-to-date with the latest source code.
- - If your development process involves frequent updates or refactors that necessitate maintaining accurate, dynamic documentation without manual intervention.
Avoid when
- - If the requirement is to generate tutorials for deeply domain-specific applications that don't match the training data of general-purpose LLMs used by PocketFlow.
- - In environments where API keys for external models are prohibited or not available, which limits the operation of this tool as it relies on third-party LLM providers.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (78d since push)
- As of 4d
- Provenance
- Not a fork · Organization account
- As of 4d
- Security (OSV)
- 23 low (23 low)
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install PocketFlow-Tutorial-Codebase-KnowledgeHow it fits your stack(6)
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Similar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
PocketFlow is a tool for generating comprehensive tutorial documents for software projects from their source code, utilizing large language models.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 17, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 17, 2026
- Languages
- python
Source: github.language · Aug 17, 2026
Categories
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 17, 2026)
python utils/call_llm.pySource link
Tags
README
🚀 Getting Started
-
Clone this repository
git clone https://github.com/The-Pocket/PocketFlow-Tutorial-Codebase-Knowledge -
Install dependencies:
pip install -r requirements.txt -
Set up LLM in
utils/call_llm.pyby providing credentials. To do so, you can put the values in a.envfile. By default, you can use the AI Studio key with this client for Gemini Pro 2.5 by setting theGEMINI_API_KEYenvironment variable. If you want to use another LLM, you can set theLLM_PROVIDERenvironment variable (e.g.XAI), and then set the model, url, and API key (e.g.XAI_MODEL,XAI_URL,XAI_API_KEY). If using Ollama, the url ishttp://localhost:11434/and the API key can be omitted. You can use your own models. We highly recommend the latest models with thinking capabilities (Claude 3.7 with thinking, O1). You can verify that it is correctly set up by running:python utils/call_llm.py -
Generate a complete codebase tutorial by running the main script:
# Analyze a GitHub repository python main.py --repo https://github.com/username/repo --include "*.py" "*.js" --exclude "tests/*" --max-size 50000 # Or, analyze a local directory python main.py --dir /path/to/your/codebase --include "*.py" --exclude "*test*" # Or, generate a tutorial in Chinese python main.py --repo https://github.com/username/repo --language "Chinese"--repoor--dir- Specify either a GitHub repo URL or a local directory path (required, mutually exclusive)-n, --name- Project name (optional, derived from URL/directory if omitted)-t, --token- GitHub token (or set GITHUB_TOKEN environment variable)-o, --output- Output directory (default: ./output)-i, --include- Files to include (e.g., "*.py" "*.js")-e, --exclude- Files to exclude (e.g., "tests/*" "docs/*")-s, --max-size- Maximum file size in bytes (default: 100KB)--language- Language for the generated tutorial (default: "english")--max-abstractions- Maximum number of abstractions to identify (default: 10)--no-cache- Disable LLM response caching (default: caching enabled)
The application will crawl the repository, analyze the codebase structure, generate tutorial content in the specified language, and save the output in the specified directory (default: ./output).
🐳 Running with Docker
To run this project in a Docker container, you'll need to pass your API keys as environment variables.
-
Build the Docker image
docker build -t pocketflow-app . -
Run the container
You'll need to provide your
GEMINI_API_KEYfor the LLM to function. If you're analyzing private GitHub repositories or want to avoid rate limits, also provide yourGITHUB_TOKEN.Mount a local directory to
/app/outputinside the container to access the generated tutorials on your host machine.Example for analyzing a public GitHub repository:
docker run -it --rm \ -e GEMINI_API_KEY="YOUR_GEMINI_API_KEY_HERE" \ -v "$(pwd)/output_tutorials":/app/output \ pocketflow-app --repo https://github.com/username/repoExample for analyzing a local directory:
docker run -it --rm \ -e GEMINI_API_KEY="YOUR_GEMINI_API_KEY_HERE" \ -v "/path/to/your/local_codebase":/app/code_to_analyze \ -v "$(pwd)/output_tutorials":/app/output \ pocketflow-app --dir /app/code_to_analyze
For agents
This page has a .md twin and JSON over the API.