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# Misc
*.bak
*.tmp
*.temp
*.temp
test.ipynb

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Minimalist LLM framework in [100 lines](minillmflow/__init__.py). Express popular paradigms like agents, task decomposition, RAG, and more.
- Install via ```pip install minillmflow```. Or just copy the [source codes](minillmflow/__init__.py) (it's only 100 lines)
- We **strongly recommened** using LLMs (e.g., Claude, ChatGPT, Cursor) to build LLM applications with [this prompt](minillmflow/docs/prompt)
- We **strongly recommened** using LLMs (e.g., Claude, ChatGPT, Cursor) to develop LLM applications with [this prompt](minillmflow/docs/prompt)
## Why miniLLMFlow?
The future of programming will be heavily LLM-assited, and LLMs:
1. **👍 Excel at Feature Implementation**:
Give proper context/docs, they know how to use APIs, tools, text chunking, prompt wrapping, etc.
These shouldn't be part of an LLM framework, as they're hard to maintain, update, and optimize.
1. **😀 Shine at Feature Implementation**:
With proper docs, LLMs can handle APIs, tools, chunking, prompt wrapping, etc.
These are hard to maintain and optimize, so they dont belong in the framework.
2. **👎 Suck at Paradigm Design**:
Paradigms like MapReduce, DAG workflows, and recent agents are powerful for reasoning about problems.
However, designing elegant paradigms is challenging, and LLMs tend to write redundant code.
2. **☹️ Struggle with Paradigm Design**:
Paradigms like MapReduce, task decomposition, and agents are powerful, even for LLMs.
However, designing these elegantly remains challenging for LLMs.
Can we build a framework that:
(1) removes redunant feature implementations,
(2) but keep core paradigms for LLMs to program against?
Turns out that we just need 100 lines of codes.
To enable LLMs to develop LLM applications, a framework should
(1) remove redunant feature implementations but
(2) keep core paradigms to build on.
It turns out that such a framework only needs 100 lines of code.
<div align="center">
<img src="./docs/minillmflow.jpg" width="400"/>