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FastAPI AI Kit
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A production LLM integration layer, not just an API key and a prompt.

Calling an LLM API directly is a few lines of code. Making it production-ready — streaming, token tracking, provider fallback, retries, and billing — is the part most 'LLM boilerplate' searches are actually looking for. That's the layer FastAPI AI Kit ships.

FastAPIOpenAIAnthropicSSEStripePostgreSQL

The usual pain points

  • ✕Writing separate integration code for every LLM provider you want to support
  • ✕Implementing streaming correctly across providers with different SSE formats
  • ✕Tracking token usage consistently for cost visibility or billing
  • ✕Handling rate limits and transient failures without ad-hoc retry code

How the kit solves them

  • One llm.chat() interface across OpenAI, Anthropic, and OpenAI-compatible providers
  • Streaming responses normalized to a single SSE format regardless of provider
  • Token tracking built into every call, ready to feed Stripe metering or internal dashboards
  • Automatic retry with exponential backoff on provider rate limits

Example implementation

main.py
# Same call regardless of provider — swap via env var
response = await llm.chat(
    messages=[{"role": "user", "content": prompt}],
    stream=True,
    track_tokens=True,
)
# response.tokens is normalized across OpenAI/Anthropic/OpenAI-compatible providers

Ready to build your llm boilerplate?

FastAPI AI Kit ships with everything shown above, pre-configured and production-ready. Clone the repo and start building in minutes.

Ready to ship your AI backend this weekend?

Join developers who skipped weeks of boilerplate and went straight to building.

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