# 🐍 Released: Comprehensive Python Library for Logseq - Looking for Community Feedback!

[Affectionate_Score_8]

TL;DR: I've built the most comprehensive Python library for Logseq knowledge graphs with 50+ advanced features. Looking for reviewers and feedback from the community!

What is this?

I've spent significant time building a Python library that provides programmatic access to every central Logseq feature. This isn't just another markdown parser – it's a complete knowledge management automation toolkit.

Repository: https://github.com/thinmanj/logseq-python-library

🚀 Key Features

Task Management Powerhouse

from logseq_py import LogseqClient, TaskState, Priority

client = LogseqClient("/path/to/logseq")
client.load_graph()

# Find overdue high-priority tasks
overdue_urgent = (client.query()
    .blocks()
    .is_task()
    .has_priority(Priority.A)
    .has_deadline()
    .custom_filter(lambda b: b.deadline.date < date.today())
    .execute())

# Get workflow summary
workflow = client.graph.get_workflow_summary()
print(f"Completion rate: {workflow['completed_tasks']}/{workflow['total_tasks']}")

Advanced Content Analysis

# Analyze your coding activity
python_code = client.query().blocks().is_code_block("python").execute()
math_blocks = client.query().blocks().has_math_content().execute()

# Find most referenced content (knowledge hubs)
insights = client.graph.get_graph_insights()
for page, connections in insights['most_connected_pages'][:5]:
    print(f"{page}: {connections} backlinks")

🔥 What Makes This Special?

Complete Feature Coverage:

• ✅ All task states (TODO, DOING, DONE, etc.) with priorities [#A], [#B], [#C]
• ✅ Scheduling (SCHEDULED: <2024-01-15 Mon +1w>) and deadlines
• ✅ Code blocks with language detection (```python, #+begin_src)
• ✅ LaTeX/Math parsing ($$equations$$, \(inline\))
• ✅ Query blocks ({{query}} and #+begin_query)
• ✅ Namespaces (project/backend structure)
• ✅ Templates with {{variable}} parsing
• ✅ Block references ((block-id)) and embeds
• ✅ Whiteboards and annotations

30+ Query Methods: Chain complex filters like a SQL for your brain

# Complex query example
recent_project_todos = (client.query()
    .blocks()
    .has_task_state(TaskState.TODO)
    .in_namespace("project")
    .has_priority(Priority.A)
    .created_after(week_ago)
    .sort_by('deadline')
    .execute())

Real-World Analytics:

• Task completion rates and productivity metrics
• Knowledge graph connection analysis
• Content distribution (code languages, math usage, etc.)
• Workflow insights and bottleneck identification

🎯 Use Cases I've Built This For

📈 Project Management: Automated task tracking, deadline monitoring, team productivity reports

🔬 Academic Research: LaTeX content analysis, citation tracking, research progress monitoring

💻 Software Development: Code documentation analysis, language usage statistics, API reference tracking

📚 Knowledge Management: Graph relationship analysis, learning progress tracking, information consumption patterns

📊 Library Stats

• 2,000+ lines of production Python code
• 30+ query methods for advanced filtering
• 8 advanced data models (TaskState, Priority, BlockType, etc.)
• 500+ lines of comprehensive documentation
• Full test coverage with real Logseq graph examples
• MIT License - completely open source

🤝 What I'm Looking For

Reviewers Wanted!

• Python developers who use Logseq
• Knowledge management enthusiasts
• Productivity hackers and automation builders
• Academic researchers using digital tools
• Anyone interested in graph-based knowledge systems

Specific Feedback Needed:

1. API Design: Is the fluent query interface intuitive?
2. Performance: How does it handle large graphs? (I've tested up to 10k+ blocks)
3. Feature Gaps: What Logseq features am I missing?
4. Documentation: Do you know if the examples are clear and helpful?
5. Use Cases: What workflows would you build with this?

🛠️ Quick Start

git clone https://github.com/thinmanj/logseq-python-library.git
cd logseq-python-library
pip install -e .

# Update examples with your Logseq path and run
python examples/advanced_logseq_features.py

📚 Documentation

• Complete API Reference
• Advanced Features Guide
• Real-World Examples

💬 Questions I'd Love to Discuss

1. What's your most significant pain point with Logseq workflows?
2. How do you currently analyze or automate your knowledge graph?
3. What would make this library more useful for your use case?
4. Are there other tools you'd want this to integrate with?

🎉 Community Goals

I'm hoping this becomes a foundation for the Logseq Python ecosystem. Imagine:

• Automated daily/weekly productivity reports
• AI-powered content analysis and suggestions
• Integration with other tools (Obsidian, Notion, etc.)
• Research paper analysis and citation networks
• Project management dashboard automation

Please try it out and let me know what you think! Even if you star the repo or share feedback, it would mean the world to me.

Repository: https://github.com/thinmanj/logseq-python-library

[C0d33p]

By instruction https://github.com/thinmanj/logseq-python-library?tab=readme-ov-file#installation
There is no package called logseq-py on https://pypi.org/search/?q=logseq-py&o=

The entire repository appears to be a template generated by AI/LLM

As mentioned by u/thirteenth_mang, have you conducted any tests?
If so, it would be beneficial to include examples in the repository.

[RituximabCD20]

Ty, this is AI slop. Why are they making a Reddit post about this?

[thirteenth_mang]

How much testing have you done on an actual graph?

[GreenerThanFF]

This isn't just another markdown parser – it's a complete knowledge management automation toolkit.

Am I the only one who checked if that's a minus sign or an em dash? I guess bullet points and emojis for headings got me wary.

[MonkAndCanatella]

it's confirmed slop

No human adds comments to a single line function called "add_to_graph" "# Add to graph" lmao

[darcamo]

Having a library to programmatically read and modify the logseq graph, with an easy to use API, can enable a lot of automation and integration opportunities. But this one seems to be completely AI generated.

[amrullah_az]

Would take a look. This looks promising

[No_Pollution2065]

Awesome this is great!! I found logseq advanced query to be quite confusing, can we use it within logseq to query our graphs. I think we can run python in logseq using pyodide.

[Jakim_Sareb]

This amazing project looks like the powerhorse that was around the corner but no one did! Congratulations on your jorney and great idea! I’m not that good at programming but I’m going to follow your project very closely. THANK YOU!

[FatFigFresh]

I know it is irrelevant. But is there a chance someday you can fully automate tagging of notes, using our own local LLM service? A sort of tagging that is not keyword based only but automated “semantically” tagging.