instagrapi vs aiograpi: sync or async for the Instagram private API?

aiograpi is the async sister project to instagrapi. Same maintainer team (subzeroid on GitHub and PyPI), same API shape, same pydantic response models — the difference is the runtime: instagrapi.Client is synchronous, aiograpi.Client is asyncio-native. Most of this guide is a decision tree (when does async actually buy you anything for an Instagram workload?) plus a short migration recipe for projects that want to switch.

TL;DR

For one-shot scripts and single-account CLIs, stay on instagrapi. The synchronous code is shorter, the stack traces are easier to read, and the runtime cost of “I could have run two requests in parallel” is rounding error against the latency of the Instagram private API itself. For services that fan out — many accounts, many users, many requests interleaved with other I/O — switch to aiograpi and use asyncio.gather or TaskGroup to actually pay off the async overhead. The migration is mechanical because the API surface is identical.

Same surface, different runtime

The two libraries set out to expose the same functionality and they succeed at it. Every method on instagrapi.Client — login, dump_settings, user_info_by_username, media_likers, direct_threads, photo_upload — exists on aiograpi.Client with the same name and signature. The pydantic models that come back (User, Media, Story, DirectThread) are identical. The session storage format, the device fingerprinting, the proxy plumbing — all of it transfers across.

What differs is the call shape. instagrapi returns objects directly; aiograpi returns coroutines. That single difference cascades into everything downstream — your function signatures become async def, your loops become asyncio.gather calls or TaskGroup blocks, and your test fixtures have to drive an event loop. None of that is hard, but it is real, and there’s no point absorbing the cost if your workload doesn’t benefit.

When async actually pays off

asyncio only buys you something when you have I/O to overlap. Instagram private-API requests are I/O-bound, but a single sequential script doesn’t have anything to overlap them with — you’re issuing one request, waiting, then issuing the next. The savings come from one of two patterns: many requests fanned out at once (gather), or many independent agents (handlers, accounts, workers) sharing a single event loop.

The honest test is: “if I made my script three times faster on this dimension, would it matter?” For a once-a-day cron that pulls profile metadata for ten target accounts, no — the script runs in two seconds either way. For a FastAPI service taking ten requests per second, each of which needs to fetch one Instagram profile, yes — the difference between blocking the worker thread and yielding to the event loop is the difference between handling ten and handling a hundred. For a multi-account orchestrator running fifty Instagram accounts in parallel, definitely — the per-account work is mostly waiting on Instagram, and asyncio lets you run all fifty wait-states on one process.

Workload	Pick
One-off CLI, one account, one task	instagrapi
Daily cron, dozens of profiles, sequential is fine	instagrapi
FastAPI / aiohttp service, IG calls inline with handler	aiograpi
Batch scrape — hundreds of profiles to fetch	aiograpi (use gather with semaphore)
Multi-account orchestrator (10+ accounts in one process)	aiograpi
Telegram / Discord bot built on aiogram / discord.py	aiograpi (matches the host loop)

If the workload sits in the middle and you’re not sure, default to instagrapi. Async is a tax you pay even when you’re not using it — debugger ergonomics, traceback noise, the difficulty of dropping a pdb.set_trace() in the middle of a coroutine. Pay the tax when the speed-up is real.

Migration recipe

Migration from instagrapi to aiograpi is mechanical: change the import, mark the function async, and await every aiograpi call. Below is the same starting point — log in with session reuse, fetch a profile — under both libraries.

# instagrapi (sync)
from instagrapi import Client

cl = Client()
cl.load_settings("session.json")
cl.login("user", "password")
user = cl.user_info_by_username("instagram")
print(user.full_name, user.follower_count)

# aiograpi (async)
import asyncio
from aiograpi import Client

async def main():
    cl = Client()
    cl.load_settings("session.json")
    await cl.login("user", "password")
    user = await cl.user_info_by_username("instagram")
    print(user.full_name, user.follower_count)

asyncio.run(main())

The pattern that actually justifies the migration is concurrent fan-out. Below is fetching profile metadata for fifty usernames; with aiograpi and a semaphore to bound concurrency, this finishes in roughly the latency of the slowest single request rather than the sum of all fifty.

# aiograpi — concurrent fan-out with bounded concurrency
import asyncio
from aiograpi import Client

async def fetch(cl: Client, username: str, sem: asyncio.Semaphore):
    async with sem:
        return await cl.user_info_by_username(username)

async def main(usernames: list[str]):
    cl = Client()
    cl.load_settings("session.json")
    await cl.login("user", "password")
    sem = asyncio.Semaphore(8)  # cap at 8 concurrent requests
    return await asyncio.gather(*(fetch(cl, u, sem) for u in usernames))

Two notes worth pinning down. First, the semaphore is not optional — without it you will fan out fifty simultaneous calls, hit Instagram’s anti-abuse system, and the answer to “did async help?” becomes “I got rate-limited faster.” Pick a concurrency cap (4–10 is reasonable for a healthy session, lower for a cold one) and tune from there. Second, share one Client instance across the gathered tasks rather than constructing one per call — the device fingerprint, cookie jar, and login state all live on the client, and Instagram’s risk system rewards consistency.

Session and challenge handling

Both libraries expose the same callbacks for challenge_required, the same dump_settings / load_settings for persistence, and the same TOTP and SMS 2FA paths. The 2FA and challenge guide and the session persistence guide apply unchanged — the difference is one extra await on the login call. Worth saying explicitly because async failure modes can look unfamiliar: a challenge_required raised inside a coroutine surfaces just like the synchronous version, your handler returns the code, and the call resumes.

Wrapping up

aiograpi is the right pick when you have something to overlap — many requests in parallel, many accounts in one process, or an existing async host loop (FastAPI, aiogram, discord.py) that you want to match. For everything else instagrapi stays the safer default: shorter code, cleaner stack traces, no event-loop overhead. The migration path is mechanical — same API, same models, same session format — so the switch when you actually need it is a one-evening job, not a rewrite.