Dependency Graph Generation and OSO Integration

[acul71]

Dependency Graph Generation and OSO Integration

Hi @seetadev! 👋

I've been working on building a comprehensive dependency graph generation system for py-libp2p, along with integration support for Open Source Observer (OSO). This work is now available in the branch feature/dependency-graph-oso-clean.

What's Included

Scripts

• scripts/generate_dependency_graph.py - Generates direct dependency graphs
• scripts/generate_transitive_dependency_graph.py - Generates full transitive dependency trees
• scripts/integrate_oso.py - Integration script for OSO API

Documentation

Complete documentation in docs/dependency_graph/ including:

• Main README with usage instructions
• OSO integration guide
• Preview instructions
• Structure analysis

Features

1. Multiple Output Formats: JSON, DOT, Mermaid, Markdown, PNG, SVG
2. Two Graph Types:
   • Direct dependencies (44 nodes, 52 edges - star-shaped)
   • Transitive dependencies (56 nodes, 78 edges - full tree with interconnections)
3. OSO Integration: Ready-to-use scripts for Open Source Observer API
4. Visualization: Pre-generated images for quick viewing

Quick Start

# Generate direct dependency graphs
python3 scripts/generate_dependency_graph.py

# Generate full transitive dependency tree
python3 scripts/generate_transitive_dependency_graph.py

# Preview the graphs
xdg-open docs/dependency_graph/dependencies.png

Graph Statistics

• Runtime dependencies: 20
• Optional dependencies: 32
• Direct graph: 44 nodes, 52 edges
• Transitive graph: 56 nodes, 78 edges

Use Cases

• Dependency analysis and visualization
• Security tracking and vulnerability identification
• Ecosystem insights via OSO integration
• Project documentation and transparency

Files Reference

See the complete summary: https://github.com/libp2p/py-libp2p/blob/feature/dependency-graph-oso-clean/docs/dependency_graph/SUMMARY.md

Next Steps

I'd love to get your feedback on:

1. Whether this approach aligns with py-libp2p's documentation and tooling goals
2. If there are any improvements or additional features you'd like to see
3. Whether this should be integrated into the main repository

The branch is ready for review and can be found here:
https://github.com/libp2p/py-libp2p/tree/feature/dependency-graph-oso-clean

Thanks for your time!

[seetadev]

Fantastic work, @acul71 👏

This is a highly valuable and forward-looking contribution — the dependency graph generation and OSO integration is not only technically solid but also strategically aligned with the broader goals of transparency, maintainability, and ecosystem visibility for py-libp2p.

The modular approach — separating direct and transitive graphs, supporting multiple export formats (JSON, DOT, Mermaid, PNG, SVG), and integrating seamlessly with the Open Source Observer (OSO) API — provides both internal clarity and external accountability. This is the kind of tooling that reinforces py-libp2p’s credibility as an open public good.

From a RetroPGF funding perspective, this initiative fits beautifully into the emerging structure we’re envisioning for 2026 — where contributors can receive recognition and RetroPGF-backed rewards for work across distinct areas such as:

• Core networking and transport (e.g., TCP, WebSocket, QUIC)
• Security layers (e.g., pnet, noise)
• DHT and routing improvements
• Testing and CI/CD reliability
• Tooling, documentation, and analytics infrastructure (like this OSO module)

I’d also recommend listing py-libp2p on oso.xyz so that this work (and future commits) are tracked transparently via the OSO registry. That will help us establish contribution analytics and dependency provenance data for RetroPGF reporting.

Let’s plan to integrate this module into main after review — and coordinate with OSO to make py-libp2p a visible, measurable open-source public good on their dashboard. 🚀