pocketflow

What problem does it solve?

Building robust and complex LLM applications can be daunting. This Skill provides PocketFlow, a minimalist (100-line) framework that simplifies the development of advanced AI systems by offering clear abstractions for graph-based workflows, agentic behaviors, and data processing. It helps you manage complexity and accelerate your LLM project development.

Core Features & Use Cases

• Graph-based LLM Workflows: Design and orchestrate complex AI logic using intuitive Nodes (building blocks) and Flows (orchestration).
• Agentic Applications & Task Decomposition: Create autonomous agents that make dynamic decisions, break down complex tasks, and interact with tools.
• RAG & Batch Processing: Implement Retrieval Augmented Generation (RAG) systems for context-aware answers and efficiently process large datasets with LLMs.
• Use Case: Automate content creation by chaining LLM calls to generate an outline, write a draft, and then refine the article, all within a structured workflow.

Quick Start

To get started with the PocketFlow project template:

1. Install dependencies: pip install -r skills/pocketflow/assets/template/requirements.txt
2. Configure your LLM: Edit skills/pocketflow/assets/template/utils.py and implement call_llm() for your provider.
3. Set API key (e.g., for OpenAI): export OPENAI_API_KEY=sk-...
4. Run the example: python skills/pocketflow/assets/template/main.py