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openai-chatkit-backend-python

Run a self-hosted ChatKit server with the OpenAI Agents SDK.

Updated Jan 2, 2026
Search Tags:#python#backend#streaming#apis#chatkit#agents-sdk
One-click install
npx skills add https://github.com/ZoyaAfzal/Hackathon2_Todo_SDD --skill openai-chatkit-backend-python-zoyaafzal
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openai-chatkit-backend-python
Source: https://github.com/ZoyaAfzal/Hackathon2_Todo_SDD/tree/main/.claude/skills/openai-chatkit-backend-python
Command: npx skills add https://github.com/ZoyaAfzal/Hackathon2_Todo_SDD --skill openai-chatkit-backend-python-zoyaafzal

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This guide provides a robust blueprint for building a self-hosted Python backend for ChatKit that cleanly separates transport logic from AI reasoning and wires into the OpenAI Agents SDK, enabling fully custom deployments.

Core Features & Use Cases

  • Exposes a compliant /chatkit/api endpoint and, if needed, an upload route for direct file uploads.
  • Streams assistant responses in real time via Server-Sent Events (SSE) and supports rich UI widgets via chatkit.widgets.
  • Enforces a secure, scalable architecture with a single model factory, authentication checks, domain allowlists, and a clear separation between transport protocol and agents' reasoning.

Quick Start

Start by wiring a FastAPI route at /chatkit/api, initialize an Agent from the model factory, and stream responses with Runner.run_streamed() to push SSE events to the client.

Frequently Asked Questions about openai-chatkit-backend-python

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

FAQPage Schema
How do I build a self-hosted ChatKit backend in Python?▼

A ChatKit backend for Python orchestrates conversations via the OpenAI Agents SDK, exposing a compliant endpoint that handles real-time streaming, rich UI widgets, and tool integrations securely.

How does streaming work in a Python ChatKit backend?▼

Streaming works by using Runner.run_streamed() to generate real-time assistant responses pushed to the client via Server-Sent Events (SSE), enabling live updates during the conversation.

Can I use the OpenAI Agents SDK with FastAPI for ChatKit deployments?▼

Yes, ChatKit backends enforce a secure, scalable architecture using authentication checks and domain allowlists, ensuring only authorized requests reach the agents' reasoning logic.

What is the best way to separate transport logic from AI reasoning in a Python backend?▼

The best way to separate transport logic from AI reasoning is using a single factory pattern for model creation, isolating the FastAPI transport protocol from the OpenAI Agents SDK reasoning execution.

Do I need domain allowlists to secure a ChatKit API endpoint?▼

Yes, implementing domain allowlists and authentication checks is required to secure a ChatKit API endpoint, ensuring the self-hosted server only processes requests from trusted sources.

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