All use casesUse Case

Build a production AI support agent in hours, not weeks.

Deploy a FastAPI backend that handles customer queries with LLM-powered responses, escalation logic, conversation history, and token-aware billing — all pre-configured.

FastAPIOpenAI / AnthropicPostgreSQLRedisCeleryStripe

The usual pain points

✕Setting up conversation history storage and context injection
✕Rate limiting per customer to prevent abuse
✕Tracking token usage for cost management
✕Escalation logic and fallback handling

How the kit solves them

Unified LLM layer with conversation context injection out of the box
Per-API-key rate limiting with customizable tiers
Per-request token tracking ready to wire into Stripe metering
Async background jobs for escalation notifications via Celery

Example implementation

main.py

@router.post("/v1/support/chat")
@require_api_key(tier=["support"])
@rate_limit(per_minute=20)
async def support_chat(
    body: SupportChatRequest,
    key: APIKey = Depends(get_api_key),
    db: AsyncSession = Depends(get_db),
):
    history = await get_conversation(db, body.session_id)
    response = await llm.chat(
        messages=[*history, {"role": "user", "content": body.message}],
        track_tokens=True,
    )
    await meter.record(key.id, response.tokens)
    return SupportChatResponse(reply=response.content)

Ready to build your ai customer support backend?

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

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