A 100-Line LLM Framework
I've seen a lot of comments about how complex frameworks like LangChain can be. Over the holidays, I wanted to see how minimal an LLM framework could get if we stripped away everything non-essential. The result is an LLM framework in just 100 lines of code. These 100 lines capture what I see as the core abstraction of most LLM frameworks: a nested directed graph that breaks down tasks into multiple LLM steps, with branching and recursion to enable agent-like decision-making. From there, you can layer on more advanced features like agents, RAG, task decomposition, and more. I’ve intentionally…
A 100-Line LLM Framework launched on January 6, 2025 with 9 votes, #2,006 of 6,142 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Pocket Flow: 100-line LLM framework. Let Agents build Agents! - The-Pocket/PocketFlow
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! Current LLM frameworks are bloated... You only need 100 lines for LLM Framework! 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. 👀 Want to see other tutorials for dummies? Create an issue! 🚀 Through Agentic Coding —the fastest LLM App development paradigm-where humans design and agents codefrom github.com
In the maker’s words, at launch
I've seen a lot of comments about how complex frameworks like LangChain can be. Over the holidays, I wanted to see how minimal an LLM framework could get if we stripped away everything non-essential. The result is an LLM framework in just 100 lines of code. These 100 lines capture what I see as the core abstraction…
Does a similar job
all alternatives →
LLMFlows – LangChain alternative for explicit and transparent apps2023 · github.com · ▲49Hi HN! Over the last several weekends, I've been building LLMFlows as an alternative to langchain. There's been a lot of discussion on the shortcomings of langchain in the past few weeks, but when I first tried it in March, I thought there are 3 main problems: 1. Too many abstractions 2. Hidden prompts and opinionated logic in chains which makes it hard to customize 3. Hard to debug This inspired me to try and build a framework that solves these 3 issues, and therefore I started building LLFlows with the "philosophy" of being "simple, explicit, and transparent." A few weekends later, I think…
LLMWare – Small Specialized Function Calling 1B LLMs for Multi-Step RAG2024 · github.com · ▲51Hi, I was a corporate lawyer for many years working with a lot of financial services and insurance companies. In practicing law, I noticed there was a lot of repetition in the tasks I was working on even as a highly paid attorney that could be automated. I wanted to solve the problem of dealing with a lot information and data in a practical way, using AI. This motivated me to start AI Bloks/LLMWare with my husband, who had a deep background in software and is a very early adopter of AI. We have been on this journey with our open source project LLMWare for the past 4 months, producing a…
LLM, a Rust Crate/CLI for CPU Inference of LLMs (LLaMA, GPT-NeoX, etc.)2023 · github.com · ▲45G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
Building an LLM from Scratch in TypeScript8d ago · book.humanexperiences.io · ▲33The from-scratch guide to building large language models in NodeJS.
I made a state machine framework for guided conversations with LLMs2024 · github.com · ▲19It’s written in Python and I call it GoalChain. It lets you build a conversation flow graph that the user traverses. When there’s enough input it spits out a dictionary with the defined fields. Otherwise it will jump state to state as led by the user. It was fun to write, and it’s surprisingly effective if you keep in mind you’re prompt-engineering every string and field name. README.md has a mini-tutorial. Would be cool to get some ideas for how to build it further and what improvements I could make.
No-BS Database of 300+ real-world LLM/GenAI production implementations2024 · zenml.io · ▲7I've spent weeks curating technical implementation details of how companies are actually deploying LLMs and Generative AI in production. The database now contains over 300 case studies with detailed technical summaries (230,000+ words) focusing exclusively on architectural decisions, deployment patterns, and real engineering challenges. Key features: * Each case study is technically focused - no marketing fluff * 150+ entries from technical conference talks and panels (saving you 100+ hours of video watching) * Sophisticated filtering by technical stack, RAG implementations, monitoring…
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