Does this skill support state persistence?

Yes, by passing a checkpointer to the @entrypoint decorator, the workflow automatically saves its state, allowing it to be resumed or recovered after an interruption.

What is the LangGraph Functional API?

It is a simplified way to build LangGraph workflows using Python decorators like @task and @entrypoint instead of manually constructing a StateGraph.

How does parallel execution work in this skill?

Tasks decorated with @task return futures. By calling multiple tasks before calling .result() on them, they execute in parallel, enabling high-performance fan-out patterns.

Can I implement human-in-the-loop with this API?

Absolutely. Use the interrupt() function within an @entrypoint to pause execution and wait for human input or approval before resuming with the Command pattern.

LangGraph Functional API

Name: LangGraph Functional API
Author: yonatangross

byyonatangross

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Data Science & ML

Builds complex AI workflows using decorator-based patterns for parallel execution, persistence, and human-in-the-loop interactions.

About

The LangGraph Functional API skill enables developers to construct sophisticated AI agents and workflows using a modern, decorator-driven approach rather than explicit graph construction. By utilizing @task and @entrypoint decorators, it simplifies the creation of sequential logic, parallel 'fan-out' processing, and stateful interactions. This skill is particularly useful for building production-ready systems that require checkpointing for long-running tasks, debugging via standard Python/TypeScript patterns, and seamless human-in-the-loop approval flows.

Key Features

Native human-in-the-loop support via interrupt() and Command patterns
Built-in state persistence and checkpointing for resumable workflows
Multi-language support for both Python and TypeScript implementations
Implicit parallel execution using futures and .result()
Decorator-based orchestration with @task and @entrypoint
29 GitHub stars

Use Cases

Implementing parallel data fetching or processing 'fan-out/fan-in' patterns
Building multi-step AI agents with conditional branching and loops
Creating resumable long-running workflows that require human approval

About

Key Features

Native human-in-the-loop support via interrupt() and Command patterns
Built-in state persistence and checkpointing for resumable workflows
Multi-language support for both Python and TypeScript implementations
Implicit parallel execution using futures and .result()
Decorator-based orchestration with @task and @entrypoint
29 GitHub stars

Use Cases

Implementing parallel data fetching or processing 'fan-out/fan-in' patterns
Building multi-step AI agents with conditional branching and loops
Creating resumable long-running workflows that require human approval