AnkiConnect - Live Anki Integration API#

Type: Anki Add-on (not PyPI package) GitHub: FooSoft/anki-connect Purpose: RESTful API for live integration with running Anki desktop

Popularity Metrics#

• Anki Add-on ID: 2055492159
• GitHub Stars: Not found in search (add-on hosted on AnkiWeb)
• Maintenance: Active (maintained by FooSoft)
• API Port: localhost:8765
• Python Client: ankiapi package on PyPI

Quick Assessment#

Pros:

• ✅ Live integration - Real-time add/update cards while Anki running
• ✅ Full Anki features - Access all Anki functionality via API
• ✅ Bi-directional - Read AND write (not just export)
• ✅ Active ecosystem - Used by many Anki extensions

Cons:

• ❌ Requires Anki running - Desktop app must be open
• ❌ User installation - Must install add-on (extra friction)
• ❌ Not cross-platform - Anki desktop only (no mobile API)
• ❌ More complex - REST API vs simple .apkg export

Use Case Fit#

Scenario 1 (Create flashcards for existing Anki users): ⚠️ POSSIBLE BUT COMPLEX

• Can sync cards to user’s Anki
• But requires: Anki running + add-on installed
• More friction than .apkg download

Scenario 2 (Build software with custom interface): ❌ NOT APPLICABLE

• Still requires Anki desktop running
• Doesn’t provide custom flashcard UI

API Example#

# Install: pip install ankiapi
from ankiapi import AnkiAPI

api = AnkiAPI()  # Connects to localhost:8765

# Create deck
api.create_deck("Latin Vocabulary")

# Add flashcard
api.add_flashcard(
    deck="Latin Vocabulary",
    front="puella",
    back="girl"
)

Confidence for Language Learning App#

MEDIUM - Adds complexity vs genanki .apkg export Best for: Apps that need real-time sync with Anki desktop Not recommended: If users just want to import decks

Sources#

• AnkiConnect Add-on
• FooSoft/anki-connect GitHub
• ankiapi PyPI

ankipandas - Anki Database Analysis#

PyPI Package: ankipandas GitHub: klieret/AnkiPandas Purpose: Analyze Anki databases using pandas DataFrames

Popularity Metrics#

• Downloads: 357/week (~1,548/month)
• GitHub Stars: (part of klieret repos)
• Maintenance: Active (scanned July 2024)
• Latest Version: 0.3.15
• License: Not specified

Quick Assessment#

Pros:

• ✅ Pandas integration - Load Anki DB as DataFrame
• ✅ Analysis tools - Visualize, select, manipulate cards
• ✅ Export capabilities - CSV, Excel, HTML, JSON

Cons:

• ❌ Writing DISABLED - Cannot modify Anki database (issue #137)
• ❌ Read-only - Analysis tool, not creation tool
• ❌ Low popularity - 1,548 downloads/month (vs 33,970 for genanki)
• ❌ Not for card generation - Wrong tool for creating flashcards

Use Case Fit#

Scenario 1 (Create flashcards for existing Anki users): ❌ NOT APPLICABLE

• Cannot create cards (write functionality disabled)
• Only for analyzing existing Anki databases

Scenario 2 (Build software with custom interface): ❌ NOT APPLICABLE

• Analysis tool, not flashcard delivery system

Potential Use#

Only useful for: Research/analytics on Anki usage patterns

• Example: “Which cards are most difficult?”
• Example: “What’s average retention rate?”

Not useful for: Creating or delivering flashcards

Basic Usage#

from ankipandas import Collection

# Load Anki collection
col = Collection()

# Analyze as DataFrame
notes_df = col.notes.fields_as_columns()
cards_df = col.cards.merge_notes()

# Export analysis
cards_df.to_csv('analysis.csv')

Confidence for Language Learning App#

LOW - Wrong tool. Use genanki for card creation.

Verdict: Skip for flashcard generation use case.

Sources#

• ankipandas PyPI
• AnkiPandas GitHub
• ankipandas docs

S1: Rapid Discovery - Approach#

Methodology: Rapid Library Search (speed-focused) Time Box: 60-90 minutes maximum Goal: Identify viable Anki integration libraries and custom system patterns

Core Philosophy#

Quickly map the solution space:

• What Anki integration libraries exist?
• How mature/popular are they?
• Is building custom flashcard system feasible?
• What’s the 80/20 answer?

Discovery Process#

1. Anki Integration Landscape (25 min)#

• genanki: Programmatic deck generation
• ankipandas: Database analysis
• AnkiConnect: Live API integration
• Anki pylib: Direct database access
• Check: PyPI downloads, GitHub stars, maintenance status

2. Custom Flashcard System Patterns (20 min)#

• FSRS + SQLite (simplest DIY)
• Web frameworks (React + FSRS)
• Existing open-source flashcard apps
• Implementation complexity estimate

3. Anki Ecosystem Assessment (15 min)#

• Anki user base size (adoption risk)
• .apkg format stability (future-proofing)
• Mobile sync options (AnkiWeb vs self-hosted)
• Community momentum (growing vs declining)

4. Quick Comparison (15 min)#

• Anki export: Pros/cons, implementation hours
• Custom system: Pros/cons, implementation hours
• Hybrid approach: Feasibility assessment

5. Initial Recommendation (10 min)#

• Default path for language learning app
• When to consider alternatives
• Signal for S3 scenario analysis

Evaluation Criteria#

Primary:

• Implementation effort (hours to working flashcard delivery)
• User adoption friction (installation, learning curve)

Secondary:

• Data portability (can users export/migrate?)
• Long-term maintenance burden

Tertiary:

• UX control vs ecosystem benefits trade-off

Output Files#

• approach.md (this file)
• genanki.md - Programmatic deck generation
• ankiconnect.md - Live Anki integration
• ankipandas.md - Database analysis
• custom-system.md - Build-your-own patterns
• recommendation.md - Initial path forward

Success Criteria#

• Understand Anki integration options (3-4 viable libraries)
• Estimate custom system complexity (rough hours)
• Identify which path minimizes friction for users
• Provide input for S3 scenario analysis
• Total time: <90 minutes

Note for S3#

S3 will do heavy lifting on scenario comparison:

• Scenario 1: Anki export (existing Anki users)
• Scenario 2: Custom interface (dedicated app users)

S1 just maps what’s possible - S3 determines which scenario wins.

genanki - Programmatic Anki Deck Generation#

PyPI Package: genanki GitHub: kerrickstaley/genanki Purpose: Generate Anki .apkg files without Anki installation

Popularity Metrics#

• Downloads: 33,970/month (PyPI)
• GitHub Stars: 1,492
• Maintenance: Last release Nov 2023 (14 months ago)
• Latest Version: 0.13.1
• License: MIT

Quick Assessment#

Pros:

• ✅ No Anki dependency - generates .apkg files standalone
• ✅ Simple API - Create Note → Card → Deck → Package → write_to_file()
• ✅ Media support - Images, audio embedded in .apkg
• ✅ GUID management - Stable IDs for updates
• ✅ Most popular Anki generation library (34K downloads/month)
• ✅ MIT license - permissive

Cons:

• ⚠️ Maintenance concern - No releases in 14 months (last: Nov 2023)
• ⚠️ Not official - “Not affiliated with main Anki project”
• ⚠️ No scheduling - Only creates decks, doesn’t handle reviews
• ⚠️ User must have Anki - .apkg files require Anki to use

Use Case Fit#

Scenario 1 (Create flashcards for existing Anki users): ✅ PERFECT FIT

• Generates .apkg files users import into Anki
• No custom UI needed - leverages Anki ecosystem
• Minimal development: ~5-10 hours to generate decks

Scenario 2 (Build software with custom interface): ❌ NOT APPLICABLE

• Only generates export files, no flashcard delivery
• Users still need Anki installed

Basic Usage#

import genanki

# Create model (card template)
model = genanki.Model(...)

# Create notes
note = genanki.Note(model=model, fields=['Latin', 'English'])

# Create deck
deck = genanki.Deck(deck_id=123, name='Latin Vocabulary')
deck.add_note(note)

# Export to .apkg
genanki.Package(deck).write_to_file('output.apkg')

Confidence for Language Learning App#

HIGH - If targeting existing Anki users (Scenario 1) N/A - If building custom interface (Scenario 2)

Sources#

• genanki PyPI
• genanki GitHub
• PyPI Stats - 33,970 downloads/month

S1 Rapid Discovery - Recommendation#

Time Spent: ~75 minutes Confidence Level: HIGH (for library landscape)

Anki Integration Libraries Found#

Clear Winner: genanki#

Popularity: 22× more downloads than ankipandas (33,970 vs 1,548/month)

Why genanki wins:

• ✅ Highest popularity by far
• ✅ Simple API (Note → Card → Deck → Package)
• ✅ No Anki installation needed (generates .apkg standalone)
• ✅ MIT license (permissive)
• ✅ Media support (audio, images)
• ✅ Stable (v0.13.1, mature codebase)

Why skip alternatives:

• AnkiConnect: Requires Anki running + add-on install (more friction)
• ankipandas: Read-only, cannot create decks (wrong tool)

Quick Validation#

Basic Usage (confirmed from docs):

pip install genanki

import genanki
model = genanki.Model(...)
note = genanki.Note(model=model, fields=['Latin', 'English'])
deck = genanki.Deck(deck_id=123, name='Latin')
deck.add_note(note)
genanki.Package(deck).write_to_file('output.apkg')

Generates: .apkg file users import into Anki

S1 Library Recommendation#

Use genanki for Anki deck generation.

• Most popular option (33,970 downloads/month)
• Proven stable (1,492 GitHub stars)
• Simple API for programmatic deck creation

What S1 Did NOT Answer#

S1 is library discovery only. These questions belong in S2/S3:

For S2 (Comprehensive):

• Detailed genanki features (templates, media, updates)
• Alternative approaches (custom flashcard system architecture)
• API comparison (genanki vs direct Anki DB vs AnkiConnect)

For S3 (Need-Driven):

• Scenario 1: Does genanki .apkg export satisfy existing Anki users?
• Scenario 2: Build custom flashcard interface (no Anki dependency)?
• Implementation effort comparison (Anki export vs custom system)

S1 found the libraries. S2/S3 determine how to use them.

Sources#

• genanki PyPI - 33,970 downloads/month
• genanki GitHub - 1,492 stars
• AnkiConnect - Anki add-on
• ankipandas PyPI - 1,548 downloads/month
• FSRS PyPI - From 1.141 research
• Awesome FSRS
• Real Python Django Flashcards

S2-comprehensive content not found

S3-need-driven content not found

S4-strategic content not found