Mini LLM Flow

Agent Details

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.

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.

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.