Overview
Pocket Flow is a minimalist LLM framework implemented in ~100 lines of Python. Its core idea is to capture the essential abstraction of LLM frameworks — a computation/communication Graph — and from that single concise core enable common LLM design patterns like agents, workflows, streaming, RAG (retrieval-augmented generation), and parallel/batch processing.
Key characteristics
- Lightweight: the main implementation is intentionally tiny (~100 lines) with zero runtime dependencies and minimal surface area. This prioritizes clarity, auditability, and ease of embedding in other projects.
- Graph-first abstraction: Pocket Flow models LLM apps as nodes and edges in a graph, making it straightforward to express chains, multi-agent interactions, and complex workflows while keeping the core simple.
- Expressive patterns: the repo includes cookbooks and examples for Multi-Agent, Workflow, RAG, Map-Reduce, Streaming, Memory, Text2SQL, structured outputs, and other common patterns.
- Agentic Coding support: designed to enable "agentic coding" workflows where agents can generate or modify agent code, facilitating rapid prototyping and agent-of-agents patterns.
Ecosystem & examples
Pocket Flow provides a documentation site, many tutorial cookbooks, and demo apps (website chatbot, YouTube summarizer, parallel batch demos, voice chat, etc.). The project also publishes language ports and related repositories (TypeScript, Java, C++, Go, Rust, PHP) so developers can experiment across runtimes.
Installation & getting started
- Install via pip:
pip install pocketflowor simply copy the 100-line source file if you prefer zero-install usage. - Documentation and design patterns are hosted on the official docs site (see official_url). The repo contains step-by-step cookbooks for beginners and advanced examples for building agentic apps.
Who maintains it
The project is published by the GitHub organization "The-Pocket" and includes community channels such as a Discord server and tutorial videos to help learners onboard quickly.
Typical use cases
- Learning and teaching LLM design patterns and agents.
- Rapid prototyping of small LLM-powered applications where simplicity and transparency matter.
- Building agent workflows, RAG prototypes, and experimenting with multi-agent coordination without the overhead of large frameworks.
Notes
Pocket Flow emphasizes minimalism over completeness: it is intentionally small to be readable and easily composable, rather than a full-featured enterprise framework. For larger production needs you may integrate Pocket Flow concepts into a broader stack or use more feature-rich frameworks as needed.
