instaharvest-v2 1.0.23

Powerful Instagram Private API — anti-detection, async, Pydantic models

📸 InstaHarvest v2

Powerful Instagram Private API — async, anti-detection, Pydantic models, AI Agent

32 sync + 32 async modules • 230+ functions • Pydantic models • AI Agent • CI/CD • 489 tests passed

📖 Documentation: mpython77.github.io/instaharvest_v2

---

Installation

pip install instaharvest-v2

With extras:

pip install instaharvest-v2[dev]      # pytest, pytest-cov, pytest-asyncio
pip install instaharvest-v2[agent]    # AI providers (Gemini, OpenAI, Claude)
pip install instaharvest-v2[web]      # FastAPI web playground
pip install instaharvest-v2[all]      # everything

---

Quick Start

With cookies (.env file)

from instaharvest_v2 import Instagram

ig = Instagram.from_env(".env")
user = ig.users.get_by_username("cristiano")
print(user.username)        # cristiano
print(user.followers)       # 650000000
print(user["bio"])           # dict-like access works too

With login

ig = Instagram()
ig.login("username", "password")
ig.auth.save_session("session.json")   # save for next time

Load saved session

ig = Instagram()
ig.auth.load_session("session.json")   # no re-login needed

Async mode (50x faster for bulk)

import asyncio
from instaharvest_v2 import AsyncInstagram

async def main():
    async with AsyncInstagram.from_env(mode="fast") as ig:
        # Parallel — all at once!
        tasks = [ig.users.get_by_username(u) for u in usernames]
        profiles = await asyncio.gather(*tasks)

asyncio.run(main())

Challenge auto-resolver

ig = Instagram(
    challenge_callback=lambda ctx: input(f"Code sent to {ctx.contact_point}: ")
)
# Email/SMS challenges resolved automatically!

Anonymous (no login)

ig = Instagram.anonymous()
profile = ig.public.get_profile("cristiano")
posts = ig.public.get_posts("cristiano", max_count=12)

🎬 Reel / Post Scraping (no login, no cookies!)

ig = Instagram()

# By shortcode (from URL: instagram.com/reel/ABC123/)
post = ig.public.get_post_by_shortcode("ABC123")
print(post["likes"])         # 69603
print(post["video_url"])     # full video download URL
print(post["audio"])         # {'title': 'Original audio', 'artist': '...'}

# By full URL
post = ig.public.get_post_by_url("https://instagram.com/reel/ABC123/")

# Works for ALL types: Reels, Photos, Carousels, Videos
# Returns: video_url, display_url, likes, views, comments, audio, owner, carousel_media

🤖 AI Agent

from instaharvest_v2 import Instagram
from instaharvest_v2.agent import InstaAgent, Permission

ig = Instagram.from_env(".env")
agent = InstaAgent(
    ig=ig,
    provider="gemini",        # 13 AI providers
    permission=Permission.FULL_ACCESS,
    memory=True,
)

agent.ask("Get @cristiano's last 10 posts and save to CSV")
agent.ask("Compare @nike and @adidas engagement rates")
agent.ask("Find the best posting time for my account")

.env Format

SESSION_ID=your_session_id
CSRF_TOKEN=your_csrf_token
DS_USER_ID=your_user_id
MID=optional
IG_DID=optional
DATR=optional
USER_AGENT=optional

---

API Reference

👤 Users

user = ig.users.get_by_username("cristiano")   # → User model
user = ig.users.get_by_id(123456789)           # → User model
users = ig.users.search("cristiano")           # → List[UserShort]
profile = ig.users.get_full_profile("cristiano")  # merged User
bio = ig.users.parse_bio(user)                 # → BioParsed

📷 Media

media = ig.media.get_info(media_pk)            # → Media model
media = ig.media.get_by_shortcode("ABC123")    # → Media model
likers = ig.media.get_likers(media_pk)         # → List[UserShort]
comments = ig.media.get_comments_parsed(pk)    # → List[Comment]

ig.media.like(media_pk)
ig.media.comment(media_pk, "Nice! 🔥")
ig.media.save(media_pk)
ig.media.edit_caption(media_pk, "New caption")
ig.media.pin_comment(media_pk, comment_pk)

📰 Feed (GraphQL v2 + REST fallback)

# Home feed (GraphQL v2 — fast and reliable)
timeline = ig.feed.get_timeline(count=12)     # → {posts, has_next, end_cursor}
all_tl = ig.feed.get_all_timeline(max=50)     # full pagination

# User posts
ig.feed.get_user_feed(user_pk)                # REST endpoint
posts = ig.feed.get_all_posts(user_pk, max_posts=100, delay=2)

# Reels (GraphQL v2)
reels = ig.feed.get_reels_feed(count=10)      # Trending reels

# Saved/Liked/Tag
ig.feed.get_saved()                           # Saved collections
ig.feed.get_liked()                           # Liked posts
ig.feed.get_tag_feed("fashion")               # By hashtag
ig.feed.get_location_feed(location_pk)        # By location

📖 Stories

ig.stories.get_tray()
ig.stories.get_user_stories(user_pk)
ig.stories.mark_seen(story_pks)
ig.stories.get_highlights_tray(user_pk)
ig.stories.create_highlight("My Highlight", story_pks)
ig.stories.react_to_story(story_pk, "🔥")

🤝 Friendships

ig.friendships.follow(user_pk)
ig.friendships.unfollow(user_pk)
followers = ig.friendships.get_all_followers(user_pk)  # → List[UserShort]
following = ig.friendships.get_all_following(user_pk)  # → List[UserShort]
ig.friendships.block(user_pk)
ig.friendships.remove_follower(user_pk)
ig.friendships.mute(user_pk)
ig.friendships.restrict(user_pk)
ig.friendships.add_close_friend(user_pk)
ig.friendships.get_mutual_followers(user_pk)

💬 Direct Messages

ig.direct.get_inbox()
ig.direct.send_text(thread_id, "Hello!")
ig.direct.send_media(thread_id, media_pk)
ig.direct.create_thread([user_pk1, user_pk2])
ig.direct.send_link(thread_id, "https://example.com")
ig.direct.send_reaction(thread_id, item_id, "❤️")

📤 Upload

ig.upload.post_photo("photo.jpg", caption="My post")
ig.upload.post_video("video.mp4", caption="My reel")
ig.upload.post_story_photo("story.jpg")
ig.upload.post_reel("reel.mp4", caption="Trending")
ig.upload.post_carousel(["img1.jpg", "img2.jpg"], caption="Album")
ig.upload.delete_media(media_pk)

📥 Download

ig.download.download_media(media_pk)
ig.download.download_stories(user_pk)
ig.download.download_highlights(user_pk)
ig.download.download_profile_pic(username="cristiano")
ig.download.download_user_posts(user_pk, max_posts=50)
ig.download.download_by_url("https://instagram.com/p/ABC123/")

🔍 Search

ig.search.top_search("query")
users = ig.search.search_users("cristiano")    # → List[UserShort]
ig.search.search_hashtags("fashion")
ig.search.search_places("New York")

⚙️ Account

ig.account.get_current_user()
ig.account.edit_profile(full_name="New Name")
ig.account.set_private()
ig.account.set_public()
ig.account.get_blocked_users()
ig.account.get_login_activity()

🔐 Auth

ig.login("username", "password")
ig.auth.save_session("session.json")
ig.auth.load_session("session.json")
ig.auth.validate_session()
ig.auth.logout()

More Core Modules

ig.hashtags.get_info("fashion")        # Hashtag info
ig.insights.get_account_insights()     # Analytics
ig.notifications.get_activity_feed()   # Notifications
ig.graphql.get_followers(user_pk)      # GraphQL queries
ig.location.search("New York")         # Location search
ig.collections.get_list()              # Saved collections

🔬 GraphQL API v2 (Modern doc_id Transport)

Direct access to Instagram's modern GraphQL endpoints with verified doc_ids. All methods use POST /graphql/query — faster and more reliable than REST.

# 👤 Profile — mini profile info (fast and lightweight)
card = ig.graphql.get_hover_card(user_id="173560420", username="cristiano")
print(card["follower_count"])    # 650000000
print(card["biography"])         # bio text
print(card["is_verified"])       # True
print(card["mutual_followers"])  # [{username, pk, ...}]

# 🔗 Similar accounts (80+ !)
suggested = ig.graphql.get_suggested_users(user_id="173560420")
for user in suggested["users"]:
    print(f"@{user['username']} | {user['full_name']}")
# → @messi, @neymar, @ronaldo, ... (~80 similar accounts)

# 📷 User posts (GraphQL — with pagination)
data = ig.graphql.get_user_posts_v2("cristiano")
for post in data["posts"]:
    print(f"{post['shortcode']} | ❤️{post['like_count']} | {post['caption'][:50]}")

# 💬 Comments (GraphQL v2)
data = ig.graphql.get_comments_v2(media_pk="3843229444966511752", count=20)
for c in data["comments"]:
    print(f"@{c['user']['username']}: {c['text'][:50]} | ❤️{c['like_count']}")

# ❤️ Like a post (mutation)
result = ig.graphql.like_media(media_id="3843229444966511752")
print(result["success"])  # True

# 📺 Home timeline (GraphQL v2 — root + pagination)
timeline = ig.graphql.get_timeline_v2(count=12)
for post in timeline["posts"]:
    print(f"@{post['user']['username']} | {post['product_type']} | ❤️{post['like_count']}")

# 🎬 Trending reels
reels = ig.graphql.get_reels_trending_v2(count=10)
for reel in reels["posts"]:
    print(f"@{reel['user']['username']} | ❤️{reel['like_count']}")

# 🔖 Saved collections
saved = ig.graphql.get_saved_v2()
for col in saved["posts"]:
    print(f"{col['collection_name']} | {col['media_count']} items")

Available doc_ids (16 verified):

Key	Endpoint	Status
profile_posts	User posts pagination	✅ Verified
profile_reels	User reels tab	✅ Verified
profile_tagged	User tagged posts	✅ Verified
profile_hover_card	Mini profile popup	✅ Verified
profile_highlights	Story highlights tray	✅ Verified
profile_suggested	Similar accounts	✅ Verified
feed_timeline	Home feed (initial)	✅ Verified
feed_timeline_pagination	Home feed (scroll)	✅ Verified
feed_reels_trending	Trending reels	✅ Verified
feed_saved	Saved collections	✅ Verified
media_comments	Post comments	✅ Verified
like_media	Like mutation	✅ Verified
stories_seen	Story seen mutation	✅ Stored
search_null_state	Search initial state	✅ Stored
feed_liked	Liked posts	REST fallback
feed_tag	Hashtag feed	REST fallback

---

🛠️ Advanced Tools

📊 Analytics

report = ig.analytics.engagement_rate("cristiano")
times = ig.analytics.best_posting_times("nike")
analysis = ig.analytics.content_analysis("adidas")
summary = ig.analytics.profile_summary("messi")
result = ig.analytics.compare(["nike", "adidas", "puma"])

📤 Export (CSV / JSON)

ig.export.followers_to_csv("nike", "followers.csv", max_count=5000)
ig.export.following_to_csv("nike", "following.csv")
ig.export.post_likers("media_pk", "likers.csv")
ig.export.to_json("cristiano", "profile.json", include_posts=True)

🌱 Growth Engine

ig.growth.follow_users_of("competitor", count=20)
ig.growth.unfollow_non_followers(max_count=50)
non_followers = ig.growth.get_non_followers()
fans = ig.growth.get_fans()
ig.growth.add_whitelist(["friend1", "friend2"])

🤖 Automation

ig.automation.comment_on_hashtag("fashion", templates=["Nice! 🔥", "Love it! ❤️"])
ig.automation.auto_like_feed(count=20)
ig.automation.auto_like_hashtag("travel", count=30)
ig.automation.watch_stories("target_user")

📅 Scheduler

ig.scheduler.post_at("2026-03-01 09:00", photo="post.jpg", caption="Scheduled!")
ig.scheduler.story_at("2026-03-01 12:00", photo="story.jpg")
ig.scheduler.reel_at("2026-03-01 18:00", video="reel.mp4", caption="Reel time!")
ig.scheduler.start()  # Background worker

👁️ Account Monitor

watcher = ig.monitor.watch("cristiano")
watcher.on_new_post(lambda data: print("New post!"))
watcher.on_follower_change(lambda old, new: print(f"{old} → {new}"))
watcher.on_bio_change(lambda old, new: print(f"Bio changed!"))
ig.monitor.start(interval=300)  # Check every 5 min

📥 Bulk Download

ig.bulk_download.all_posts("cristiano", output_dir="./downloads")
ig.bulk_download.all_stories("cristiano", output_dir="./downloads")
ig.bulk_download.everything("cristiano", output_dir="./downloads")

🔬 Hashtag Research

analysis = ig.hashtag_research.analyze("python")
related = ig.hashtag_research.related("python", count=30)
suggestions = ig.hashtag_research.suggest("coding", count=20, mix="balanced")
comparison = ig.hashtag_research.compare(["python", "javascript", "rust"])

🗃️ Data Pipeline (SQLite / JSONL)

ig.pipeline.to_sqlite("cristiano", "data.db", include_posts=True, include_followers=True)
ig.pipeline.to_jsonl("cristiano", "data.jsonl", max_posts=100)

🧠 AI Hashtag Suggester

result = ig.ai_suggest.hashtags_from_caption("Beautiful sunset at the beach")
profile_tags = ig.ai_suggest.hashtags_for_profile("cristiano")
captions = ig.ai_suggest.caption_ideas("travel", style="casual", count=5)

👥 Audience Finder

lookalike = ig.audience.find_lookalike("competitor", count=50)
overlap = ig.audience.overlap("account_a", "account_b")
insights = ig.audience.insights("my_account")

💬 Comment Manager

comments = ig.comment_manager.get_comments(media_pk, sort="top")
ig.comment_manager.auto_reply(media_pk, keyword="price?", reply="DM us!")
ig.comment_manager.delete_spam(media_pk)
sentiment = ig.comment_manager.sentiment(media_pk)

🧪 A/B Testing

test = ig.ab_test.create("Caption Test", variants={
    "A": {"caption": "Short and sweet"},
    "B": {"caption": "Long detailed caption with hashtags #test"},
})
ig.ab_test.record(test["id"], "A", likes=100, comments=20)
ig.ab_test.record(test["id"], "B", likes=150, comments=30)
result = ig.ab_test.results(test["id"])
print(f"Winner: {result['winner']}")

---

Pydantic Models

All API methods return typed Pydantic models with dict-like access:

user = ig.users.get_by_username("cristiano")

# Typed attributes
print(user.username)          # "cristiano"
print(user.follower_count)    # 650000000

# Dict-like access (backward compatible)
print(user["username"])       # "cristiano"

# Convert to dict
data = user.to_dict()

# Extra fields preserved (API changes won't break)
print(user.some_new_field)    # works!

Available models: User, UserShort, Media, Comment, Story, Highlight, DirectThread, DirectMessage, Location, Hashtag, Notification

---

Features

Feature	Description
🛡️ Anti-detection	Browser fingerprint rotation, Gaussian delays, escalation
🔄 Multi-account	Automatic session rotation
🌐 Proxy support	SOCKS5/HTTP, weighted rotation, health checking
⏱️ Rate limiting	Per-endpoint sliding window limits
🔐 Login	NaCl encrypted password, 2FA, checkpoint handling
💾 Session persistence	Save/load sessions, no re-login needed
🧩 Challenge handler	Auto-resolve email/SMS/consent challenges
⚡ Full async parity	32 sync + 32 async modules — complete feature match
📦 Pydantic models	Typed returns, dict-like access, backward compatible
🤖 AI Agent	13 providers, natural language control, memory, webhooks
📊 12 Advanced tools	Analytics, Export, Growth, Automation, Monitor, Pipeline, etc.
✅ CI/CD	GitHub Actions — lint, test (3 Python versions), security, build
🧪 489 tests	35% coverage, pytest-cov, comprehensive unit & integration tests

Speed Modes (Async)

# 🐢 SAFE  — 5 concurrent, human delays
async with AsyncInstagram.from_env(mode="safe") as ig: ...

# ⚡ FAST  — 15 concurrent, moderate delays
async with AsyncInstagram.from_env(mode="fast") as ig: ...

# 🚀 TURBO — 50 concurrent, minimal delays (proxy required)
async with AsyncInstagram.from_env(mode="turbo") as ig: ...

---

Testing & Quality

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=instaharvest_v2 --cov-report=term-missing

Current status:

• ✅ 489 tests passed
• 📊 35.3% code coverage

---

Project Structure

instaharvest_v2/
├── instagram.py           # Main class (sync)
├── async_instagram.py     # Main class (async)
├── client.py              # HTTP client (curl_cffi)
├── async_client.py        # Async HTTP client
├── anon_client.py         # Anonymous scraping client (sync)
├── async_anon_client.py   # Anonymous scraping client (async)
├── parsers.py             # Shared parser functions (8 standalone)
├── challenge.py           # Challenge auto-resolver
├── anti_detect.py         # Anti-detection system
├── smart_rotation.py      # Multi-signal rotation coordinator
├── session_manager.py     # Session auto-refresh
├── proxy_manager.py       # Proxy rotation
├── rate_limiter.py        # Rate limiting
├── multi_account.py       # Multi-account manager
├── exceptions.py          # Error classes
├── models/                # Pydantic models
│   ├── user.py            # User, UserShort, BioParsed
│   ├── media.py           # Media, Caption
│   ├── comment.py         # Comment
│   ├── story.py           # Story, Highlight
│   ├── direct.py          # DirectThread, DirectMessage
│   ├── location.py        # Location
│   ├── notification.py    # Notification models
│   └── public_data.py     # PublicProfile, PublicPost
├── api/                   # API modules (33 sync + 33 async)
│   ├── users.py           # User profiles
│   ├── media.py           # Post interactions
│   ├── feed.py            # User feeds
│   ├── friendships.py     # Follow/unfollow
│   ├── search.py          # Search
│   ├── stories.py         # Stories & highlights
│   ├── direct.py          # Direct messages
│   ├── upload.py          # Photo/video upload
│   ├── download.py        # Media downloads
│   ├── auth.py            # Login/logout
│   ├── analytics.py       # Engagement analytics
│   ├── export.py          # CSV/JSON export
│   ├── growth.py          # Smart follow/unfollow
│   ├── automation.py      # Bot framework
│   ├── scheduler.py       # Post scheduling
│   ├── monitor.py         # Account monitoring
│   ├── bulk_download.py   # Bulk media download
│   ├── hashtag_research.py # Hashtag analysis
│   ├── pipeline.py        # Data pipeline (SQLite/JSONL)
│   ├── ai_suggest.py      # AI hashtag/caption
│   ├── audience.py        # Lookalike audience
│   ├── comment_manager.py # Comment management
│   ├── ab_test.py         # A/B testing
│   ├── public_data.py     # Public data analytics
│   ├── discover.py        # Similar user discovery
│   └── async_*.py         # All 33 async mirrors
├── agent/                 # AI Agent system
│   ├── core.py            # InstaAgent main class
│   ├── providers/         # AI providers (Gemini, OpenAI, Claude, etc.)
│   ├── tools.py           # 10 built-in tools
│   ├── memory.py          # Conversation memory
│   ├── templates.py       # 10 task templates
│   ├── tui.py             # Terminal UI (Rich)
│   ├── web.py             # Web UI (FastAPI)
│   ├── webhook.py         # Notifications (Telegram, Discord)
│   ├── cost_tracker.py    # Token usage & pricing
│   └── vision.py          # Multimodal image analysis
tests/                     # 489 tests
docs/                      # MkDocs documentation
.github/workflows/         # CI/CD pipeline

CI/CD

GitHub Actions pipeline (.github/workflows/ci.yml):

Job	Description
Lint	flake8 + mypy
Test	Python 3.10, 3.11, 3.12 + coverage
Security	bandit + safety
Build	Package + twine check

⚠️ Legal Disclaimer

This library is an unofficial, third-party tool designed for educational and research purposes only. It is not affiliated with, authorized, maintained, sponsored, or endorsed by Instagram or Meta Platforms, Inc.

By using this software, you agree that:

1. You are solely responsible for any actions you take using this library.
2. The authors and maintainers are NOT responsible or liable for any bans, blocks, suspensions, or other penalties applied to your Instagram accounts or IP addresses.
3. You will use this tool in compliance with Instagram's Terms of Service and all applicable local laws.

Use at your own risk. The software is provided "AS IS", without warranty of any kind.

---

License

MIT License. See LICENSE for details.