Mini LLM Flow - LLM Framework in 100 Lines
[](https://zachary62.github.io/miniLLMFlow/)
A [100-line](minillmflow/__init__.py) minimalist LLM framework for [agents](https://minillmflow.github.io/miniLLMFlow/agent.html), [task decomposition](https://minillmflow.github.io/miniLLMFlow/decomp.html), [RAG](https://minillmflow.github.io/miniLLMFlow/rag.html), etc.
- Install via ```pip install minillmflow```, or just copy the [source codes](minillmflow/__init__.py) (only 100 lines)
- **๐ก Pro tip!!** Build LLM apps with LLMs assistants (ChatGPT, Claude, Cursor.ai, etc.)
- **Claude Project (Sonnet 3.5 strongly recommended!):**
- Create a [project](https://www.anthropic.com/news/projects) and upload the [docs](docs) to project knowledge
- Set project custom instructions. For example:
```
1. check "tool.md" and "llm.md" for the required functions.
2. design the high-level (batch) flow and nodes.
3. design the shared memory structure: define its fields, data structures, and how they will be updated.
Think out aloud for above first and ask users if your design makes sense.
4. Finally, implement. Start with simple, minimalistic codes without, for example, typing.
```
- Ask it to build LLM application!
- **ChatGPT:** Check out [GPT assistant](https://chatgpt.com/g/g-677464af36588191b9eba4901946557b-mini-llm-flow-assistant)
- GPT store seems to use older models. It's good at explaining but now good at coding.
- Alternatively, send the [docs](docs) to newer models like O1 for coding.
Documentation: https://minillmflow.github.io/miniLLMFlow/
## Why Mini LLM Flow?
Mini LLM Flow is designed to be **the framework used by LLMs**. In the future, LLM projects will be *self-programmed* by LLMs themselves: Users specify requirements, and LLMs will design, build, and maintain. Current LLMs are:
1. **๐ Good at Low-level Details:** LLMs can handle details like *wrappers, tools, and prompts*, which don't belong in a framework. Current frameworks are over-engineered, making them hard for humans (and LLMs) to maintain.
2. **๐ Bad at High-level Paradigms:** While paradigms like *MapReduce, task decomposition, and agents* are powerful, LLMs still struggle to design them elegantly. These high-level concepts should be emphasized in frameworks.
The ideal framework for LLMs should (1) **strip away low-level implementation details**, and (2) **keep high-level programming paradigms**. Hence, we provide this minimal (100-line) framework that allows LLMs to focus on what matters.
Mini LLM Flow is also a *learning resource*, as current frameworks abstract too much away.
## How Does it Work?
The [100 lines](minillmflow/__init__.py) capture what we 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 for agent-like decision-making. From there, itโs easy to layer on more complex features.
- To learn more details, please check out documentation: https://minillmflow.github.io/miniLLMFlow/
- Beginner Tutorial: [Text summarization for Paul Graham Essay + QA agent](https://colab.research.google.com/github/zachary62/miniLLMFlow/blob/main/cookbook/demo.ipynb)
- Have questions for this tutorial? Ask LLM assistants through [this prompt](https://chatgpt.com/share/676f16d2-7064-8000-b9d7-f6874346a6b5)
- More coming soon ... Let us know youโd love to see!
