Pocket Flow is a 100-line minimalist LLM framework

• Lightweight: Just 100 lines. Zero bloat, zero dependencies, zero vendor lock-in.

• Expressive: Everything you love—(Multi-)Agents, Workflow, RAG, and more.

• Agentic Coding: Let AI Agents (e.g., Cursor AI) build Agents—10x productivity boost!

• To install, pip install pocketflowor just copy the source code (only 100 lines).

• To learn more, check out the documentation. To learn the motivation, read the story.

• 🎉 Join our discord!

• 🎉 Thanks to @zvictor, @jackylee941130 and @ZebraRoy, we now have a TypeScript version!

Why Pocket Flow?

Current LLM frameworks are bloated... You only need 100 lines for LLM Framework!

	Abstraction	App-Specific Wrappers	Vendor-Specific Wrappers	Lines	Size
LangChain	Agent, Chain	Many (e.g., QA, Summarization)	Many (e.g., OpenAI, Pinecone, etc.)	405K	+166MB
CrewAI	Agent, Chain	Many (e.g., FileReadTool, SerperDevTool)	Many (e.g., OpenAI, Anthropic, Pinecone, etc.)	18K	+173MB
SmolAgent	Agent	Some (e.g., CodeAgent, VisitWebTool)	Some (e.g., DuckDuckGo, Hugging Face, etc.)	8K	+198MB
LangGraph	Agent, Graph	Some (e.g., Semantic Search)	Some (e.g., PostgresStore, SqliteSaver, etc.)	37K	+51MB
AutoGen	Agent	Some (e.g., Tool Agent, Chat Agent)	Many [Optional] (e.g., OpenAI, Pinecone, etc.)	7K (core-only)	+26MB (core-only)
PocketFlow	Graph	None	None	100	+56KB

How does Pocket Flow work?

The 100 lines capture the core abstraction of LLM frameworks: Graph!

From there, it's easy to implement popular design patterns like (Multi-)Agents, Workflow, RAG, etc.

✨ Below are basic tutorials:

Name	Difficulty	Description
Chat	☆☆☆ Dummy	A basic chat bot with conversation history
Structured Output	☆☆☆ Dummy	Extracting structured data from resumes by prompting
Workflow	☆☆☆ Dummy	A writing workflow that outlines, writes content, and applies styling
Agent	☆☆☆ Dummy	A research agent that can search the web and answer questions
RAG	☆☆☆ Dummy	A simple Retrieval-augmented Generation process
Map-Reduce	☆☆☆ Dummy	A resume qualification processor using map-reduce pattern for batch evaluation
Streaming	☆☆☆ Dummy	A real-time LLM streaming demo with user interrupt capability
Chat Guardrail	☆☆☆ Dummy	A travel advisor chatbot that only processes travel-related queries
Multi-Agent	★☆☆ Beginner	A Taboo word game for asynchronous communication between two agents
Supervisor	★☆☆ Beginner	Research agent is getting unreliable... Let's build a supervision process
Parallel	★☆☆ Beginner	A parallel execution demo that shows 3x speedup
Parallel Flow	★☆☆ Beginner	A parallel image processing demo showing 8x speedup with multiple filters
Majority Vote	★☆☆ Beginner	Improve reasoning accuracy by aggregating multiple solution attempts
Thinking	★☆☆ Beginner	Solve complex reasoning problems through Chain-of-Thought
Memory	★☆☆ Beginner	A chat bot with short-term and long-term memory
MCP	★☆☆ Beginner	Agent using Model Context Protocol for numerical operations

👀 Want to see other tutorials for dummies? Create an issue!

How to Use Pocket Flow?

🚀 Through Agentic Coding—the fastest LLM App development paradigm-where humans design and agents code!

✨ Below are examples of more complex LLM Apps:

App Name	Difficulty	Topics	Human Design	Agent Code
Build Cursor with Cursor We'll reach the singularity soon ...	★★★ Advanced	Agent	Design Doc	Flow Code
Ask AI Paul Graham Ask AI Paul Graham, in case you don't get in	★★☆ Medium	RAG Map Reduce TTS	Design Doc	Flow Code
Youtube Summarizer Explain YouTube Videos to you like you're 5	★☆☆ Beginner	Map Reduce	Design Doc	Flow Code
Cold Opener Generator Instant icebreakers that turn cold leads hot	★☆☆ Beginner	Map Reduce Web Search	Design Doc	Flow Code

• Want to learn Agentic Coding?

  • Check out my YouTube for video tutorial on how some apps above are made!

  • Want to build your own LLM App? Read this post! Start with this template!

  • Want to learn the detailed steps? Read this Guide!