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Guide · Open source

How to use PocketFlow: install, build nodes and flows

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

PocketFlow is a small, open-source Python framework for building LLM applications. Its core is about 100 lines with no dependencies and no vendor-specific wrappers, and it models your code as a graph of small nodes connected into flows. This guide shows you how to install it, understand the graph abstraction, and find the cookbook examples that cover agents, workflows and retrieval-augmented generation (RAG).

PocketFlowThe-Pocket/PocketFlow · ★ 11.3k · MIT

Quick start

  1. 1Install the package. Run pip install pocketflow in a Python environment. Alternatively, copy the source file from pocketflow/__init__.py into your project.
  2. 2Read the core. Open the 100-line source to understand nodes and flows before building anything.
  3. 3Pick a cookbook example. Choose a dummy-level example such as Chat or RAG from the README table.
  4. 4Run and modify the example. Adapt the example to your own task, keeping the graph of nodes and flows clear.
  5. 5Read the documentation. Use the docs site for design patterns covering agents, workflows and RAG.

What is PocketFlow and who is it for?

PocketFlow is a minimalist framework for LLM applications. The README describes it as a 100-line LLM framework that is lightweight, with zero dependencies and zero vendor lock-in. It can be used to build agents, workflows and RAG systems.

It suits Python developers who want an LLM framework small enough to read in full, and people who would rather copy the source into a project than add a dependency. It is a code library, not a ready-made app, so it is not designed for non-programmers.

  • Good fit: readable LLM code you can inspect end to end
  • Good fit: agents, multi-step workflows and RAG pipelines in Python
  • Not a fit: teams that need many prebuilt vendor and vector database integrations, since the README's comparison table lists none for PocketFlow

Install PocketFlow with pip

The README gives the install command below. The listed prerequisite is Python. You can also skip the package and copy the source file, which the README says is only 100 lines.

  • Install from PyPI with the package manager
  • Or copy the source code from pocketflow/__init__.py directly into your project
pip install pocketflow

Understand the graph: nodes and flows

PocketFlow's core abstraction is a graph. Small nodes do one piece of work, and they are connected into flows. The README says popular patterns such as multi-agent systems, workflows and RAG are built on top of this graph abstraction.

Because the core is so small, you can read the whole mechanism before you rely on it. That is the main reason people choose it when they want to understand and control their LLM code.

Run the cookbook examples

The README lists basic tutorials in its cookbook folder, each with a difficulty rating. Start with the dummy-level examples, then move to the beginner ones. Each entry links to its own folder in the GitHub repository.

Examples listed in the README include a basic chat bot with conversation history, structured data extraction from resumes, a writing workflow, a research agent that searches the web, and a simple RAG process.

  • Chat: a basic chat bot with conversation history
  • Structured Output: extracting structured data from resumes by prompting
  • Workflow: a writing workflow that outlines, writes and applies styling
  • Agent: a research agent that can search the web and answer questions
  • RAG: a simple retrieval-augmented generation process
  • Multi-Agent: a Taboo word game for async communication between 2 agents

Learn more and get help

The README points to a video tutorial and the documentation site at the-pocket.github.io/PocketFlow. A Discord server is linked for connecting with other developers building with PocketFlow. Ports in other languages (TypeScript, Java, C++, Go, Rust and PHP) are linked from the README as separate repositories.

  • Documentation: https://the-pocket.github.io/PocketFlow/
  • Video tutorial linked from the README
  • Discord community linked from the README

Use PocketFlow with AI coding assistants

The README describes an approach it calls agentic coding, where AI agents such as Cursor AI are used to build agents with PocketFlow. It is a good fit for developers experimenting with AI coding assistants that generate agent code, since the small codebase is easy to inspect and verify.

What people use it for

  • A basic chat bot with conversation history
  • A research agent that searches the web and answers questions
  • A simple retrieval-augmented generation pipeline
  • A batch processor that translates markdown into multiple languages
  • A command-line joke generator with human-in-the-loop feedback

Frequently asked questions

Is PocketFlow free to use?

PocketFlow is released under the MIT licence, as shown in the repository's badge. The README does not describe any paid tier.

What do I need to use PocketFlow?

The listed prerequisite is Python. You install it with pip install pocketflow or by copying the source file.

How does PocketFlow compare to LangChain?

The README's comparison table lists LangChain with many app-specific and vendor-specific wrappers and about 405K lines. PocketFlow has no wrappers and about 100 lines, so it offers less prebuilt integration.

Is PocketFlow a good alternative to LangGraph?

Both use a graph-based approach. The README lists LangGraph with some prebuilt wrappers and about 37K lines, while PocketFlow has none and is much smaller. Choose PocketFlow if you want a small codebase you can read in full.

Does PocketFlow have a stable release?

The source material does not show a latest release, so the repository should be checked directly before relying on it for a formal, versioned project.

Can I use PocketFlow without installing a package?

Yes. The README says you can copy the source code, which is only 100 lines, directly into your project instead of adding a dependency.

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Our verdict

PocketFlow suits Python developers who want a small, readable LLM framework they can inspect in full and copy into a project. Teams that need many prebuilt vendor and vector database integrations, non-programmers, or projects that require a formal versioned release should pick a larger framework such as LangChain or LangGraph.

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