Mini LLM Flow

About Mini LLM Flow

Launched Jan 04, 2025

Description

Minimalist LLM Framework in 100 Lines. Enable LLMs to Program Themselves.

A 100-line minimalist LLM framework for agents, task decomposition, RAG, etc. Mini LLM Flow is designed to be the framework used by LLMs. 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. The 100 lines 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, it’s easy to layer on more complex features.

Mini LLM Flow Key Features

Node-based task orchestration for building complex LLM workflows.
Flow nesting and recursion for hierarchical and dynamic task execution.
Batch processing for large datasets with efficient MapReduce patterns.
Express paradigms like agents, Retrieval-Augmented Generation (RAG), task decomposition, and more.
Work seamlessly with coding assistants like ChatGPT, Claude, and Cursor.ai for real-time workflow development and iteration.

Mini LLM Flow Use Cases

General-purpose framework for building LLM-driven applications across domains.
Low-level customization for prototyping and developing tailored workflows.
Processing and summarizing large datasets efficiently using MapReduce.
Building AI agents for research, analysis, and task automation.
Rapid integration with coding assistants for real-time workflow creation and debugging.

Pros

Lightweight framework with only 100 lines, making it easy to understand and modify.
Enables LLMs to program themselves, promoting autonomous decision-making.
Focuses on high-level programming paradigms, simplifying complex task management.
Supports task decomposition, RAG (Retrieval-Augmented Generation), and agent-like decision-making.
Provides a core abstraction with nested directed graphs for advanced LLM operations.
Easy to layer additional complex features due to its minimalist design.

Cons

Limited functionality out of the box, requiring additional development for advanced use cases.
May oversimplify certain complex implementations that require more extensive code.
Lacks detailed documentation, which might pose a learning curve for new users.
The minimalist approach might not be suitable for all LLM applications, especially those needing robust functionality.