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pocketflow

Build graph-based LLM workflows with PocketFlow's Node and Flow abstractions.

2|Updated Oct 24, 2025
Search Tags:#workflow automation#LLM#RAG#multi-agent#agents#task decomposition#AI framework
One-click install
npx skills add https://github.com/nickth3man/claude_market --skill pocketflow
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pocketflow
Source: https://github.com/nickth3man/claude_market/tree/main/skills/pocketflow
Command: npx skills add https://github.com/nickth3man/claude_market --skill pocketflow

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pocketflow, PyYAML, nltk, openai, anthropic, google-generativeai, and includes scripts (resource) and references (resource) and assets (resource) components.

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

Frequently Asked Questions about pocketflow

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build graph-based LLM workflows with agents and task decomposition?▼

Graph-based LLM workflows orchestrate agents and tasks using PocketFlow's Node and Flow abstractions. Nodes follow a 3-step pattern (prep/exec/post) and Flows coordinate execution via a shared Graph and Store, enabling dynamic agent decisions and task decomposition within a structured workflow.

Can I implement RAG pipelines and multi-agent systems with workflow automation?▼

PocketFlow supports both offline and online RAG pipelines alongside multi-agent coordination through graph-based orchestration. Agents interact asynchronously, share state via a central Store, and decompose complex tasks across the workflow graph.

How do I process large datasets and handle batch operations in LLM workflows?▼

PocketFlow enables batch processing and asynchronous I/O workflows through its graph structure and shared Store. Chain multiple Nodes to process datasets efficiently, with each Node handling prep, execution, and post-processing stages.

What LLM providers does PocketFlow support?▼

PocketFlow integrates with OpenAI, Anthropic, and Google Generative AI. Configure your provider by implementing the call_llm() function in utils.py and setting the corresponding API key environment variable.

Do I need prior framework experience to use PocketFlow?▼

PocketFlow is designed as a minimalist 100-line framework with clear abstractions, lowering the entry barrier. The quick start requires only Python, pip, and an LLM API key; understanding Nodes and Flows is sufficient to build complex workflows.

When should I choose workflow graphs over direct LLM chains?▼

Graph-based workflows excel when tasks require conditional logic, multi-step dependencies, agent autonomy, or state sharing across steps. Direct chains suit simple sequential calls; graphs handle agentic behavior, task decomposition, and complex orchestration.

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