Code Property Graph for code comprehension

[qdrddr]

Code Property Graph (CPG) is a powerful concept that creates a dynamic graph representation of your system design / architecture diagram, code that tracks dependencies in your code, searches to dead or duplicated code, helps finding bugs and do security/vulnerability and impact analysis but most importantly helps with the code comprehension: high-level of function, class and module dependencies, interact queries to your codebase. Helps LLMs to better reason.

If we can incorporate CPG principle into roo/cline copilot so LLMs could better comprehand and query code structure and its dependancies. Here are a few frameworks for this:
CodeQL
ShiftLeftSecurity/codepropertygraph
jQAssistant
joern
Fraunhofer-AISEC/cpg
FalkorDB: code-graph & backend
GitDiagram Convert any GitHub repository structure into a system design / architecture diagram

[qdrddr]

Please vote for my other ideas:

1. Code Property Graph for code comprehension with AI (This one)
2. Long-Term Conversation Memory
3. Add features from the Continue project: Autocomplete, Embeding indexing, Rerank
4. CodeAct architecture implementation of architecture used by Manus

[TomLucidor]

Does Roo-Code support gitingest (or faster alternatives)? All-Hands-AI/OpenHands#2185

[qdrddr]

Git ingest now has an MCP
https://github.com/TrelisResearch/trelis-gitingest-mcp

[qdrddr]

https://github.com/davidkingzyb/SCAST

[TomLucidor]

Do you think these tools are compatible with Task Master? https://github.com/eyaltoledano/claude-task-master

[BradKML]

Thinking how this is tied to the concept of the "RepoMap"

[BradKML]

There are multiple graphs to consider: code structure graph (as written without observing use needs), and architecture graph (capturing key details and requirements). This applies to (a) optimizing context management, (b) bridging the gap between tested code and design notes, (c) creating new note-taking structures, and (d) synthesizing knowledge from debugging and user feedback.

List of current discussions about graphing + note-taking #1954 #2009 #544 #2854 #2740

@dosu please observe related project discussions and their referenced links, summarize the most nuanced and cutting-edge memory designs (e.g. memory graphs, knowledge synthesis procedures) into a comprehensive checklist of requirements
eyaltoledano/claude-task-master#487 eyaltoledano/claude-task-master#356 eyaltoledano/claude-task-master#102 (comment) eyaltoledano/claude-task-master#284 (reply in thread)

[dosubot[bot]]

Here’s a comprehensive checklist of requirements for cutting-edge memory designs and knowledge synthesis procedures, synthesized from the most nuanced project discussions and referenced links:

1. Memory Graphs & Knowledge Graphs: Move beyond conventional memory banks by capturing complex relationships and dependencies between code, tasks, errors, and features. Use knowledge graphs to cluster related errors and solutions, group tightly coupled features, and link user requirements for idea generation. These graphs should support iterative refactoring and documentation, enabling dynamic knowledge synthesis as the project evolves (source).

2. Task & Context Graph Integration: Implement task graphs that reflect relationships between tasks and their corresponding code, enhancing workflow and context management. Integrate memory graphs with task management systems to provide a holistic view of project progress and dependencies (source).

3. Persistent, Structured Context: Ensure persistent context across sessions, maintaining a consistent understanding of the codebase. Use structured documentation with technical decision tracking and automated progress monitoring to bridge gaps between tested code and design notes (source).

4. Smart Workflows & Automatic Context Switching: Support mode-based operation with automatic context switching and project-specific customization, so the system adapts to different workflows and user needs (source).

5. Active Summarization & Condensation: Employ active summarization techniques and intelligent context condensation to reduce redundant content, optimize context size, and prevent context loss during long sessions. This includes asynchronous condensing operations and token window management to keep LLMs performant (source, source).

6. Structured Data Storage: Use structured data files (e.g., JSON) or lightweight embedded databases (like SQLite) for efficient context retrieval and management of complex relationships. Consider local graph databases for advanced context management (source, source).

7. Memory Bank Directory & Knowledge Base: Introduce a dedicated memory-bank/ directory with core files such as projectbrief.md, activeContext.md, and systemPatterns.md to retain project knowledge, decisions, and patterns. This helps create self-documenting projects and reduces repetitive explanations (source).

8. Bridging Code and Design Documentation: Develop tools and workflows that actively bridge the gap between code, design notes, and debugging/user feedback. This includes linking production ergonomics with debugging capabilities and supporting flexible project structures that evolve with TDD and error-driven development (source).

9. Efficient Context Management for Large Projects: Use strategies like breaking down tasks, summarizing code, prioritizing recent history, and prompt caching to manage context windows effectively in large projects (source).

10. Agentic and UX Considerations: Prioritize user experience and agentic testing in memory and context management integrations, ensuring seamless collaboration between tools and minimizing friction in workflows (source).

This checklist reflects the most advanced and nuanced approaches discussed in the Roo Code and Task Master communities, focusing on memory graphs, knowledge synthesis, and context management for modern AI-assisted development.

To reply, just mention my name @dosu.

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[qdrddr]

Here is an interesting project that produces a graph in JSON with connected functions in your code base. Though it misses MCP Server.

https://github.com/git-probe/gitprobe