How does this skill handle the 'Thread already has an active run' error?

The skill provides specific implementation patterns to check for active runs and includes instructions for canceling them before attempting to add new messages or initiate new runs.

Can I use this skill to generate charts and graphs?

Yes, by utilizing the Code Interpreter feature, the assistant can execute Python code to process data and generate image files such as charts and graphs.

What are the file limits for the File Search tool in v2?

The Assistants API v2 supports up to 10,000 files per assistant, a significant increase from the 20-file limit found in version 1.

What is the recommended alternative for new projects?

For new AI agent projects, OpenAI and this skill directory recommend using the 'openai-responses' (Responses API) skill, which offers better reasoning and modern integration patterns.

Is the OpenAI Assistants API being deprecated?

Yes, OpenAI has announced that the Assistants API v2 will be sunset in H1 2026. This skill is intended for maintaining existing applications or managing migrations to the newer Responses API.

OpenAI Assistants API Integration

Name: OpenAI Assistants API Integration
Author: jezweb

byjezweb

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Data Science & ML

Streamlines the development and maintenance of stateful chatbots, RAG systems, and data analysis tools using the OpenAI Assistants API v2.

About

This skill provides specialized guidance for implementing and managing OpenAI's Assistants API v2 within Claude Code. It simplifies complex workflows including asynchronous run polling, thread management, and tool integration for Code Interpreter and File Search (RAG). Designed for developers maintaining legacy systems or performing phased migrations, the skill offers robust error handling for common API pitfalls like active run locks and vector store indexing delays, while providing clear transition paths toward the newer Responses API architecture.

Key Features

91 GitHub stars
Large-scale RAG implementation with Vector Store management (up to 10,000 files)
Comprehensive patterns for managing Threads, Messages, and asynchronous Runs
Support for Code Interpreter for sandboxed Python execution and data visualization
Advanced streaming support for real-time assistant responses and tool progress
Built-in troubleshooting for common errors like active run collisions and polling timeouts

Use Cases

Maintaining and scaling production-grade stateful chatbots built on OpenAI's legacy framework
Implementing complex document retrieval systems (RAG) with large-scale vector databases
Automating data science tasks through the sandboxed Python execution environment

About

Key Features

91 GitHub stars
Large-scale RAG implementation with Vector Store management (up to 10,000 files)
Comprehensive patterns for managing Threads, Messages, and asynchronous Runs
Support for Code Interpreter for sandboxed Python execution and data visualization
Advanced streaming support for real-time assistant responses and tool progress
Built-in troubleshooting for common errors like active run collisions and polling timeouts

Use Cases

Maintaining and scaling production-grade stateful chatbots built on OpenAI's legacy framework
Implementing complex document retrieval systems (RAG) with large-scale vector databases
Automating data science tasks through the sandboxed Python execution environment