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openai-chatkit-backend-python

Build a Python ChatKit backend with the OpenAI Agents SDK for FastAPI or Django.

Updated Feb 8, 2026
Search Tags:#authentication#fastapi#python#backend#streaming#chatkit#agents-sdk
One-click install
npx skills add https://github.com/abdulahad139/Todoapp-HackathonII --skill openai-chatkit-backend-python-abdulahad139
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: openai-chatkit-backend-python
Source: https://github.com/abdulahad139/Todoapp-HackathonII/tree/main/.claude/skills/openai-chatkit-backend-python-old
Command: npx skills add https://github.com/abdulahad139/Todoapp-HackathonII --skill openai-chatkit-backend-python-abdulahad139

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides guidance to design, implement, and debug a custom Python ChatKit backend, enabling self-hosted deployments of ChatKit without Agent Builder and with integration to the OpenAI Agents SDK (and Gemini via OpenAI-compatible endpoints).

Core Features & Use Cases

  • Architecture guidance for a self-hosted ChatKit backend: browser/chatkit widget → Python backend → Agents SDK.
  • Patterns for streaming responses, tool integration, authentication, and tenant context.
  • Real-world usage examples and troubleshooting patterns for production readiness.

Quick Start

Set up a minimal FastAPI backend exposing /chatkit/api and wire it to a model factory via create_model() to enable ChatKit integration.

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 with Python and FastAPI?▼

Build a self-hosted ChatKit backend by creating a minimal FastAPI application that exposes a /chatkit/api endpoint and wires it to the OpenAI Agents SDK via a model factory using create_model().

Can I use the OpenAI Agents SDK with Django instead of FastAPI for ChatKit?▼

Yes, the ChatKit backend patterns apply to both FastAPI and Django environments, using a single model factory to control provider selection and orchestrate conversations through the OpenAI Agents SDK.

Does a self-hosted ChatKit backend support streaming responses and authentication?▼

Yes, self-hosted ChatKit backends enforce secure authentication, tenant context, and streaming responses while keeping the ChatKit transport separate from the reasoning layer.

What is the best way to integrate Gemini models into a Python ChatKit backend?▼

Integrate Gemini by using OpenAI-compatible endpoints within the model factory, allowing the Python ChatKit backend to orchestrate conversations across different providers through the single factory interface.

How do I handle file uploads in a self-hosted ChatKit backend?▼

Implement optional upload handling in your Python ChatKit backend by extending the FastAPI or Django application to process file inputs alongside the /chatkit/api endpoint.

Why should I keep the ChatKit transport separate from the reasoning layer in my backend?▼

Separating ChatKit transport from the reasoning layer ensures clean architecture, allowing the OpenAI Agents SDK to manage conversation logic independently of the API delivery mechanism.

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