skills.rest
NaimalArain13
NaimalArain13COMMUNITY
Explore all skills by this author →
GitHub

chatkit-python

Build a FastAPI chat backend with SSE streaming and conversation persistence.

2|Updated Dec 4, 2025
Search Tags:#backend#MCP#FastAPI#Gemini#chat#SSE#conversation-persistence
One-click install
npx skills add https://github.com/NaimalArain13/Hackathon-II_The-Evolution-of-Todo --skill chatkit-python
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chatkit-python
Source: https://github.com/NaimalArain13/Hackathon-II_The-Evolution-of-Todo/tree/main/.claude/skills/chatkit-python
Command: npx skills add https://github.com/NaimalArain13/Hackathon-II_The-Evolution-of-Todo --skill chatkit-python

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ChatKit Python backend skill provides a FastAPI-based backend to power the OpenAI ChatKit frontend, enabling a scalable chat API with SSE streaming, conversation persistence, and MCP integration.

Core Features & Use Cases

  • SSE streaming chat responses: Real-time, chunked responses delivered to chat clients with minimal latency.
  • Conversation persistence: Store and retrieve threads and messages to maintain context across sessions.
  • MCP and Gemini integration: Use MCP tools and Gemini via LiteLLM for enhanced agent-assisted workflows, including task management.

Quick Start

  • Create and activate a Python virtual environment.
  • Install dependencies: pip install fastapi sse-starlette "openai-agents[litellm]"
  • Run the server: uvicorn main:app --reload

Frequently Asked Questions about chatkit-python

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

FAQPage Schema
How do I build a FastAPI backend for OpenAI ChatKit with SSE streaming?▼

Building a FastAPI backend for OpenAI ChatKit with SSE streaming involves creating an HTTP /api/chat endpoint that delivers real-time, chunked responses to chat clients. This setup ensures minimal latency for real-time chat applications.

How does conversation persistence work in a FastAPI chat application?▼

Conversation persistence in a FastAPI chat application works by storing and retrieving threads and messages using SQLModel. This maintains conversation history and context across multiple user sessions for agent-assisted workflows.

Can I integrate MCP tools and Gemini into a FastAPI chat backend?▼

You can integrate MCP tools and Gemini into a FastAPI chat backend using LiteLLM and the OpenAI Agents SDK. This enables enhanced agent-assisted workflows and modular agent tooling for task management.

What dependencies do I need to run a FastAPI SSE chat server?▼

Running a FastAPI SSE chat server requires installing fastapi, sse-starlette, and openai-agents with litelll. You must also create and activate a Python virtual environment before starting the server with uvicorn.

What is the best way to add real-time chat API responses to an OpenAI ChatKit frontend?▼

Adding real-time chat API responses to an OpenAI ChatKit frontend is best achieved by building a compatible FastAPI backend with SSE streaming. This provides scalable chat API delivery and persistent conversations.

Related Skills

Explore More Software Engineering Skills→
zeeshan080zeeshan080
2

openai-chatkit-backend-python

Implement a Python ChatKit backend with OpenAI Agents SDK and SSE streaming.

Community
Advanced
shuremali02shuremali02

chatkit-python

Build a FastAPI chat backend with SSE streaming and SQLModel persistence.

Community
Advanced
shuremali02shuremali02

chatkit-js

Integrate OpenAI ChatKit UI components into Next.js applications with a configurable backend API.

Community
Intermediate
Tahasaif3Tahasaif3

chatkit-python

Provide a FastAPI chat backend with SSE streaming and conversation persistence.

Community
Intermediate