Pillar B — Build guides

Published 2026-06-05 · Updated 2026-06-19 · By Avinash Chandan, Founder, Navamsha

Build a Kundli API Backend in Python (FastAPI) with Navamsha

A Kundli API backend accepts birth details from your app, calls Navamsha's calculation engine server-side, and returns structured chart JSON. FastAPI is a strong fit: async httpx calls, Pydantic request models, and OpenAPI docs for your own mobile clients.

Project setup

• Python 3.11+ with FastAPI and httpx.
• NAVAMSHA_API_KEY in environment — never expose in mobile or browser bundles.
• Optional: pydantic-settings for typed config.

from fastapi import FastAPI, HTTPException
import httpx, os

app = FastAPI()
NAVAMSHA = "https://api.navamsha.in"

@app.get("/v1/kundli")
async def kundli(date: str, time: str, latitude: float, longitude: float):
    async with httpx.AsyncClient(timeout=30.0) as client:
        r = await client.get(
            f"{NAVAMSHA}/api/v1/kundali/basic",
            headers={"X-API-Key": os.environ["NAVAMSHA_API_KEY"]},
            params={"date": date, "time": time, "latitude": latitude, "longitude": longitude},
        )
    if r.status_code != 200:
        raise HTTPException(status_code=r.status_code, detail=r.text)
    return r.json()

Production tips

• Cache responses by birth hash (date + time + lat/lng rounded) to stay within free tier limits.
• Surface meta.ayanamsa and meta.engine in your API response for client-side audit labels.
• Add rate limiting per user session — Navamsha enforces 10 req/sec on the free tier.