Pocket Flow
Build AI apps with a 100‑line, dependency‑free framework
Pocket Flow is a 100‑line, zero‑dependency LLM framework that offers graph‑based agents, workflows, RAG, and multi‑language ports, enabling rapid, vendor‑agnostic AI application development.
Overview
Highlights
Pros
- Tiny footprint (~56 KB) makes it easy to audit
- No vendor lock‑in; works with any LLM API
- Fast to prototype and understand
- Cross‑language implementations broaden ecosystem
Considerations
- Limited out‑of‑the‑box integrations (e.g., vector stores)
- Smaller community compared to larger frameworks
- Advanced features may require custom code
- Performance optimizations depend on user implementation
Fit guide
Great for
- Developers who need a lightweight, transparent LLM core
- Educational settings teaching LLM orchestration concepts
- Rapid prototyping of custom AI workflows
- Projects targeting multiple programming languages
Not ideal when
- Enterprises requiring extensive pre‑built toolkits and support
- Production systems needing built‑in scaling and monitoring
- Teams that rely on ready-made vendor SDKs
- Use cases demanding heavy‑weight graph analytics out of the box
How teams use it
Chatbot with memory
A conversational agent that retains short‑term and long‑term context using Pocket Flow’s graph nodes.
Retrieval‑augmented generation pipeline
Combine external document retrieval with LLM generation for accurate Q&A without external wrappers.
Multi‑agent research assistant
Two agents coordinate via async graph to browse web, synthesize information, and produce reports.
Parallel image processing workflow
Process multiple images concurrently, achieving up to 8× speedup using Pocket Flow’s parallel flow support.
Tech snapshot
Frequently asked questions
How do I install Pocket Flow?
Run `pip install pocketflow` or copy the single 100‑line source file into your project.
Does Pocket Flow have any external dependencies?
No. The core library is dependency‑free; optional language ports may have their own standard libraries.
Can I use Pocket Flow with any LLM provider?
Yes. The framework abstracts the LLM call, so you can plug in OpenAI, Anthropic, or self‑hosted models by implementing a simple interface.
What languages are supported?
Beyond Python, there are community ports for TypeScript, Java, C++, Go, Rust, and PHP.
Is Pocket Flow suitable for production workloads?
It is ideal for prototyping and lightweight services; production use may require additional tooling for scaling, monitoring, and security.
Project at a glance
Active- Stars
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- Watchers
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- Forks
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