🌟 Pocket Flow: A Minimalistic LLM Framework in 100 Lines of Code

Popular frameworks turn simple tasks into a quest to decipher someone else’s code. Endless wrappers, version conflicts, outdated documentation… All this is not just annoying, it slows down development. After a year of struggling with overloaded tools like LangChain, Microsoft Research developer Zachary Huang dedicated his free time to creating Pocket Flow , a framework that fits all the magic of LLM into 100 lines of code.

Pocket Flow offers a radically different approach: minimalism. It is based on the idea that any LLM pipeline can be represented as a graph of nodes and transitions. No hidden layers, just logic and transparency.

To understand how Pocket Flow works, imagine a kitchen where each node is a cooking zone.

BaseNode performs three steps: preparation (collect data), execution (process the request), postprocessing (save the result).

Flow manages the "recipe": decides where to pass control next. All interactions occur through a common data store - like a table on which the ingredients for all the cooks are located.

An example? Let's say you're building a search agent. You create nodes: DecideAction (decides whether to search), SearchWeb (searches the web), AnswerQuestion (generates an answer). You link them into a graph, where the decision of one node determines the next step. If the model doesn't know the answer, then the search is launched, the results are added to the context, and the cycle repeats. All this is a couple hundred lines of code on top of the Pocket Flow core.

The main advantage of Pocket Flow is freedom. There is no binding to specific APIs, connect any models, even local ones. No dependencies: your project remains "lightweight", and interfaces do not break after updates. Do you want query caching or stream processing? Implement it yourself, without fighting with other people's abstractions.

Of course, minimalism has a price: you won’t get ready-made solutions for every task. But this is the power of Pocket Flow. It gives you control and insight into the process, rather than a ready-made, but black box.

If you are tired of monster frameworks and want to start from scratch, check out the Pocket Flow repository . There are examples of agents, RAG systems, and multi-agent scenarios.


📌 Licensing: MIT License.


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🔗 AI vs. ML

AI (Artificial Intelligence) refers to machines simulating human intelligence 🧠, like reasoning, learning, and decision-making.

🖥📚 ML (Machine Learning) is a subset of AI, focused on algorithms that allow machines to learn from data and improve over time without being explicitly programmed.

AI thinks, ML learns. Simple as that!