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CodeGraph: Enhancing Codebase Understanding with Graphs and LLMs

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Scenario:

Modern software development often involves massive codebases spread across multiple repositories, teams, and services. Engineers and AI-based coding copilots struggle to maintain a clear mental model of how components interrelate. For instance:
  • Large Repositories: A large software project might have a large of GitHub repository, containing thousands of files.
  • Complex Dependencies: Services often call each other via APIs, share data models, or rely on specific configuration files. Finding the right function, class, or module can become tedious.
  • Evolving Code: As code evolves, comments get stale, architectural assumptions shift, and documentation becomes outdated, making it hard for coding copilots to reliably generate correct, context-aware suggestions.

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Challenges:

  1. Fragmented Knowledge: It’s difficult to piece together the entire dependency graph across the entire repository.
  2. Limited Context for LLMs: Large Language Models struggle with providing accurate code completions or refactoring suggestions if they lack a broader view of the project’s architecture.
  3. Time Lost: Developers spend significant time searching through repositories, reading documentation, and attempting to piece together the “big picture” of the codebase.

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Solution: Creating a CodeGraph

A CodeGraph is a knowledge graph that models the Python codebase at multiple levels of granularity. It goes beyond just indexing code: it captures entities and relationships within and across repositories.
  • Entities: Functions, classes, modules, services, configuration files, APIs, tests, CI/CD pipelines, and documentation pages.
  • Relationships: Who-calls-what (function call graphs), import dependencies, version histories, code ownership, and semantic links (e.g., “this module implements a particular design pattern” or “this API endpoint is deprecated and replaced by another”).
How we constructed a CodeGraph:
  • Build chain access direct dependency
  • Build init mediated direct dependency
  • Define pydantic data structures that describe a single knowledge nodes for all nodes
  • Create a knowledge graph
  • Write an in-memory retriever that gets the graphs skeletons and extracts triplets
Here is an example graph generated with cognee: code_assistants_graph_example Read more about our approach in our blog. Enriching CodeGraph with LLMs: To make this knowledge even more actionable, integrate Large Language Models that understand code semantics and developer documentation. The LLM can:
  1. Ingest the Graph: The LLM has access to structured context from the CodeGraph, so when a developer asks, “Where is the function that parses user inputs for our search engine?”, the LLM can quickly locate that function by following the graph’s relationships rather than brute-forcing file searches.
  2. Provide Context-Rich Suggestions: When the coding copilot suggests a code snippet, it can reference related modules, highlight deprecations, or warn about known compatibility issues. For example, “You might want to call FunctionParseUserInput from Utils/InputProcessor.js. It’s used in SearchEngine.js and depends on InputSchema.json.”
  3. Explain Architectural Decisions: Developers can query the LLM about architectural choices: “Why does ServiceD depend on ServiceE?”. The LLM, using the CodeGraph, responds: “ServiceD calls ServiceE’s authentication endpoint to validate tokens, as documented in ServiceE/docs/auth.md.”
  4. Link to Documentation and Commit Histories: The LLM can connect a piece of code to its associated design docs, recent commit messages, or open pull requests. If a developer asks, “How has UserProfileAPI.js changed over the last quarter?” the LLM can summarize major refactoring steps, point to relevant issues that were closed, and link to architectural decision records.

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Outcomes:

  • Improved Developer Productivity: Instead of wading through multiple repositories, developers get immediate, context-aware guidance, saving countless hours of manual searching and guesswork.
  • More Accurate Code Suggestions: Coding copilots armed with a CodeGraph context deliver more reliable and secure code completions, better refactoring strategies, and insightful recommendations.
  • Evolving with the Codebase: As repositories grow, the CodeGraph and the LLM continuously update. This ensures that as code evolves, the memory and context available to developers—and their automated assistants—stays fresh and relevant.

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Run a Demo Yourself!

Curious about how this works with cognee? Try it out in our notebook here.

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