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SHAJAR5110
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chatkit-backend

Deploy a FastAPI ChatKit backend with multi-provider AI integration.

Updated Feb 6, 2026
Search Tags:#fastapi#backend#gemini#deploy#multi-provider#chatkit#liteLLM
One-click install
npx skills add https://github.com/SHAJAR5110/hackathon-II-phase-4 --skill chatkit-backend
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: chatkit-backend
Source: https://github.com/SHAJAR5110/hackathon-II-phase-4/tree/main/.claude/skills/chatkit-backend
Command: npx skills add https://github.com/SHAJAR5110/hackathon-II-phase-4 --skill chatkit-backend

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a ready-to-use ChatKit backend scaffold enabling teams to deploy a production-ready Python FastAPI server with multi-provider AI support (Gemini, OpenAI, Anthropic) and an integrated LiteLLM workflow, including an ID collision fix.

Core Features & Use Cases

  • FastAPI-based ChatKit backend server with an extensible integration layer for AI providers.
  • Multi-provider AI support (Gemini, OpenAI, Anthropic) with provider-specific configuration.
  • Safe, deterministic streaming and memory of conversation history with ID-collision mitigation for LiteLLM.
  • Clear setup steps and standard environment configuration (.env) for reproducible deployments.

Quick Start

Configure environment variables (GEMINI_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY as needed) and start the server with uvicorn main:app --host 0.0.0.0 --port 8000. Then verify the /health endpoint.

Frequently Asked Questions about chatkit-backend

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

FAQPage Schema
How do I deploy a FastAPI backend with multi-provider AI support?▼

Deploy a FastAPI multi-provider AI backend by installing Python 3.10+ and pip dependencies, configuring environment variables, and running a uvicorn server to enable immediate testing and integration with Gemini, OpenAI, and Anthropic providers.

Can I use LiteLLM to connect Gemini, OpenAI, and Anthropic in one Python server?▼

Yes, you can use LiteLLM to connect Gemini, OpenAI, and Anthropic in a Python server. This integration routes requests across multiple AI providers within a FastAPI workflow while applying fixes for ID collisions during conversation streaming.

What is the best way to prevent LiteLLM ID collisions in streaming chat history?▼

Prevent LiteLLM ID collisions in streaming chat history by deploying a backend scaffold that implements deterministic streaming and memory management with built-in ID collision mitigation, ensuring safe conversation tracking across multiple AI providers.

How do I configure environment variables for a FastAPI ChatKit backend?▼

Configure environment variables for a FastAPI ChatKit backend by setting GEMINI_API_KEY, OPENAI_API_KEY, and ANTHROPIC_API_KEY in a standard .env file to ensure reproducible deployments and authenticate with respective AI providers.

Do I need Python 3.10 to run a production-ready ChatKit backend?▼

Yes, you need Python 3.10 or higher to run a production-ready ChatKit backend. The Skill requires Python 3.10+ and pip-installed dependencies to configure the FastAPI server and execute the uvicorn server configuration for immediate testing.

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