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Basic Information

Pocket Flow is a minimalist LLM framework implemented in about 100 lines of Python that provides a small, dependency-free foundation for building LLM-powered applications. The repository is designed for developers who want a compact core abstraction based on a graph model to implement agents, workflows, retrieval-augmented generation, streaming, memory, and other common LLM design patterns. It contains the tiny pocketflow implementation that can be installed via pip or copied directly, a rich set of cookbook examples and tutorials that illustrate patterns like chat, structured output, workflow, multi-agent interaction, RAG, batch processing, streaming, supervisor patterns, parallel execution, text-to-SQL, code generation, and voice chat. The project also publishes language ports and promotes an "agentic coding" workflow where humans design and agents generate code. Documentation, video tutorials, and community channels accompany the code to help developers learn and extend the framework.

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Features
The repo emphasizes extreme simplicity and portability with a 100-line core, zero external dependencies, and minimal size. Its central abstraction is a graph that makes it easy to compose common LLM patterns such as multi-agents, workflows, RAG, map-reduce, and parallel flows. The codebase includes many cookbook examples demonstrating chat, structured output extraction, workflow orchestration, agent research patterns, batch translation, streaming LLM responses with interrupt capability, memory for chat, text-to-SQL auto-debugging, code generator loops, MCP for numerical tasks, A2A inter-agent protocols, and FastAPI/Streamlit integration examples. It provides cross-language variants in TypeScript, Java, C++, Go, Rust, and PHP and ships with documentation, tutorial videos, and community support resources to accelerate adoption.
Use Cases
Pocket Flow helps developers rapidly prototype and learn LLM application design without framework bloat by providing a tiny, readable core that captures essential abstractions. The graph-based design and cookbook examples shorten the learning curve for building chatbots, retrieval-augmented systems, agent workflows, and multi-agent experiments. Its minimalism reduces vendor lock-in and dependency management overhead, making it easy to inspect, modify, and port to other languages. The included tutorials, design patterns, and example applications enable hands-on exploration of agentic coding where agents can be used to generate or improve code. Community resources, documentation, and sample apps demonstrate practical integrations for web apps, streaming interfaces, and background job patterns so developers can move from concept to working prototype quickly.

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