PocketFlow: A 100-Line Framework for Smarter LLMs
PocketFlow represents a paradigm shift in Large Language Model (LLM) application development. In just 100 lines of code, it offers a fully expressive, minimalist, and modular framework for building agent workflows, retrieval-augmented generation (RAG) pipelines, and custom LLM orchestration—all without the bloat.
Unlike traditional frameworks that balloon with thousands of lines of code and heavy dependencies, PocketFlow distills LLM development to its core abstraction:
💥 Why Existing LLM Frameworks Are Holding You Back
In 2025, the LLM toolchain is cluttered with over-engineered solutions:
Framework | LOC (Lines of Code) | Dependencies |
---|---|---|
LangChain | 405,000+ | 166MB |
CrewAI | 18,000 | 173MB |
SmolAgent | 8,000 | 198MB |
AutoGen | 7,000 | 26MB |
These frameworks were built with good intentions but have evolved into tangled webs of vendor-specific wrappers, internal tools, and obsolete abstractions. The result?
- ❌ Hard to understand and debug
- ❌ Difficult for AI agents to read and modify (blocking agentic coding)
- ❌ Slow to customize
- ❌ Heavy on compute and storage
PocketFlow flips the script: it’s built for clarity, agility, and compatibility with AI agents.
How PocketFlow Works: The Graph is the Core
PocketFlow is based on a single, powerful abstraction: the directed graph. Each graph node represents a logical step (e.g., "Summarize Email"), and flows can be composed, batched, branched, or looped.
🧰 Patterns You Can Build Instantly
With PocketFlow’s tiny-yet-powerful toolkit, you can easily replicate major LLM architectural patterns:
✨ Below are basic tutorials:
Name | Description |
---|---|
Workflow | A writing workflow that outlines, writes content, and applies styling |
Chat | A basic chat bot with conversation history |
RAG | A simple Retrieval-augmented Generation process |
Agent | A research agent that can search the web and answer questions interaction |
Streaming | A real-time LLM streaming demo with user interrupt capability |
📈 Real-World Use Cases Built with PocketFlow
PocketFlow powers real systems used in production and research today:
✅ Codebase Knowledge Builder
- Crawls GitHub repos
- Identifies abstractions
- Generates beginner-friendly code walkthroughs
- Adds visualizations for learning
✅ YouTube Summarizer
- Uses a map-reduce pipeline
- Extracts transcript from YouTube
- Summarizes in simple, natural language
🌐 Get started with Pocket Flow:
PocketFlow isn’t just a tool—it’s a growing movement. The ecosystem includes:
- 📃 To learn more, check out the documentation
- 📚 To learn the motivation, Here is the PocketFlow's story story.
- 💬 Join their Discord to connect with other developers building with Pocket Flow!
🧑💻 Built for the Future: Agentic Coding
The future of software is co-developed by humans and AIs. PocketFlow is built for agentic coding, where LLMs like GPT-4 or tools like Cursor actively read, write, and refactor your workflows.
In a world where developers and AI assistants work side by side, frameworks must be transparent and hackable—not black boxes.
✅ TL;DR
- 📦 Just 100 lines of Python
- 🚫 Zero dependencies, zero bloat
- 🧠 Graph-powered orchestration
- 🤖 Agent-friendly and AI-readable
- 🛠 Real patterns: RAG, chatbots, agents, workflows
- 🧪 Practical examples & tutorials
- 🔥 Built for humans and LLMs