Guide · Open source

How to use PocketFlow-Tutorial-Codebase-Knowledge: setup and first tutorial

Updated 2026-10-29 · written with AI from the project's own documentation and checked against our catalogue data

PocketFlow-Tutorial-Codebase-Knowledge is an open-source Python tool that crawls a codebase, uses an AI language model to find its core concepts and how they connect, and writes a beginner-friendly tutorial with visualizations. It is built as a tutorial project on top of Pocket Flow, a small LLM framework. This guide shows you how to install it, configure an LLM, and generate your first tutorial from a GitHub repository or a local folder.

Codebase to TutorialThe-Pocket/PocketFlow-Tutorial-Codebase-Knowledge · ★ 12.7k · MIT Install options & downloads

Quick start

1Clone the repository. Run git clone on the project URL to get the code onto your machine.
2Install dependencies. Run pip install -r requirements.txt from the project folder.
3Set your LLM credentials. Set GEMINI_API_KEY, or set LLM_PROVIDER and its model, URL and key variables, in a .env file or your environment.
4Verify the LLM setup. Run python utils/call_llm.py to confirm the model responds.
5Generate a tutorial. Run python main.py with --repo or --dir, and add include, exclude or language flags as needed.
6Open the output. Find the generated tutorial in ./output, or in your chosen --output directory.

What you need before you start

The tool runs on Python 3 and uses pip to install its dependencies. It also needs a language model to generate anything, so you need an LLM API key, for example a GEMINI_API_KEY, or a local Ollama setup.

Python 3
pip
An LLM API key (for example GEMINI_API_KEY) or a local Ollama setup
Comfort editing a .env file and running Python scripts

Install PocketFlow-Tutorial-Codebase-Knowledge

Clone the repository, then install its dependencies from the requirements file. The project is distributed under the MIT licence.

git clone https://github.com/The-Pocket/PocketFlow-Tutorial-Codebase-Knowledge
pip install -r requirements.txt

Configure your LLM

LLM setup happens in utils/call_llm.py. You can put the values in a .env file. By default, the tool uses the AI Studio key with a Gemini Pro 2.5 client, set through the GEMINI_API_KEY environment variable. To use another model, set LLM_PROVIDER (for example XAI), then set the model, URL and API key variables (for example XAI_MODEL, XAI_URL and XAI_API_KEY). For Ollama, the URL is http://localhost:11434/ and the API key can be omitted.

The README recommends models with thinking capabilities, such as Claude 3.7 with thinking or O1. To check the setup, run the verification script.

Gemini: set GEMINI_API_KEY
Other providers: set LLM_PROVIDER, then the matching _MODEL, _URL and _API_KEY variables
Ollama: URL http://localhost:11434/, API key can be omitted

python utils/call_llm.py

Generate a tutorial from a GitHub repo or local folder

Run main.py with either --repo (a GitHub URL) or --dir (a local path). The two options are mutually exclusive. The tool crawls the code, analyzes its structure, generates tutorial content in the chosen language, and saves the output to ./output by default.

Use --include and --exclude to choose files by pattern, and --max-size to set a file size limit in bytes. The default size limit is 100KB.

--repo or --dir: GitHub URL or local directory (required, mutually exclusive)
-n, --name: project name (optional, derived from URL or directory)
-t, --token: GitHub token, or set GITHUB_TOKEN
-o, --output: output directory (default ./output)
-i, --include and -e, --exclude: file patterns
-s, --max-size: maximum file size in bytes (default 100KB)
--language: tutorial language (default english)
--max-abstractions: maximum abstractions to identify (default 10)
--no-cache: disable LLM response caching (caching is on by default)

python main.py --repo https://github.com/username/repo --include "*.py" "*.js" --exclude "tests/*" --max-size 50000
python main.py --dir /path/to/your/codebase --include "*.py" --exclude "*test*"
python main.py --repo https://github.com/username/repo --language "Chinese"

Run it with Docker (optional)

The README includes a Docker route. Build the image, pass your API key as an environment variable, and mount a local folder to /app/output to read the generated tutorials on your host. Mount a local codebase to /app/code_to_analyze when analysing a local directory. Add a GITHUB_TOKEN if you analyse private repositories or want to avoid rate limits.

docker build -t pocketflow-app .
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/repo

Read the output with care

The tutorial is AI-generated. The README does not describe a verification process, so treat the result as a first draft. Check important claims against the source code before you rely on them, especially for production or compliance work.

Use the tutorial to find the main concepts and where to read the code next
Verify specific behaviour in the source before acting on it

What people use it for

Getting oriented in a large open-source library before contributing
Studying how a real project such as Flask or FastAPI is structured
Producing a first-draft onboarding guide for an internal codebase
Generating a tutorial in another language with --language

Frequently asked questions

Is PocketFlow-Tutorial-Codebase-Knowledge free?

The project is released under the MIT licence. You still need an LLM API key or a local Ollama setup to generate tutorials, and the source does not list costs for those services.

What do I need to install PocketFlow-Tutorial-Codebase-Knowledge?

You need Python 3, pip, and an LLM API key such as GEMINI_API_KEY or a local Ollama setup. Clone the repository and run pip install -r requirements.txt.

Can I use it without a Gemini key?

Yes. The README describes setting LLM_PROVIDER with the model, URL and API key for another provider, and running Ollama at http://localhost:11434/ with the API key omitted.

How do I check that my LLM is set up correctly?

Run python utils/call_llm.py. The README gives this as the way to verify the LLM setup.

Can it analyse private repositories?

The Docker section says to provide a GITHUB_TOKEN when analysing private GitHub repositories. You can also pass a token with -t or --token, or set GITHUB_TOKEN.

How does it compare to DeepWiki or GitHub Copilot Chat?

DeepWiki, Repo2Txt and GitHub Copilot Chat are comparable tools for understanding code. This tool is a self-run Python project that writes a tutorial from your own LLM setup, so it suits people comfortable with scripts. The source does not include a feature comparison, so test each option on a repo you know.

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Our verdict

Choose this tool if you already have an LLM API key, can run Python scripts, and want a tutorial-style first pass through an unfamiliar codebase. Pick something else if you need guaranteed accurate documentation, a no-setup hosted service, or you cannot install dependencies or edit a .env file.

Install options & downloads