AI coding agents are powerful writers but terrible navigators. When faced with a large, complex repository, they often fall into a loop of grep, open, re-read, and repeat. This inefficiency costs time and tokens. Enter Graphify, an open-source CLI tool that solves this by converting your codebase into a local knowledge graph, giving agents a structural map rather than a pile of files.

The Graph as a Map

Graphify transforms files, functions, and concepts into nodes, while mapping relationships like calls, imports, and dependencies as edges. This structure allows an agent to jump directly to relevant code and trace connections logically. The tool requires Python 3.10 or later and can be installed via uv, pipx, or pip. The package is named graphifyy (note the double 'y'), but the command remains graphify. Once installed, running graphify install automatically registers the skill with detected assistants like Cursor, Claude, or Copilot.

Building and Querying the Graph

Users can map their repository either through their assistant's chat interface using /graphify . or directly from the terminal with graphify extract . --code-only, which does not require an API key. The output is stored in a graphify-out/ folder containing an interactive HTML graph, a markdown report, and the graph.json file used by the CLI and MCP tools. The markdown report serves as a surprisingly effective tour of the project, highlighting main concepts and communities.

Precision and Transparency

When querying the graph, such as with graphify query "how does a request reach the database?", the tool returns context including nodes, relations, and file references. It is crucial to note that this is context, not a verified answer. The default token budget is 2000, adjustable via flags. Transparency is built-in: every edge is tagged as EXTRACTED, INFERRED, or AMBIGUOUS, allowing developers to distinguish between parsed facts and educated guesses. Users should always open the referenced lines before relying on the agent's output.

Key Takeaways

  • Graphify creates local knowledge graphs to improve AI agent navigation in large codebases.
  • The tool supports multiple installation methods and integrates with major AI coding assistants.
  • Output includes interactive HTML visualizations and JSON data for programmatic querying.
  • Edges are explicitly tagged (EXTRACTED, INFERRED, AMBIGUOUS) to maintain transparency.
  • The MCP server option allows agents to call graph tools directly for deeper integration.

The Bottom Line

Stop letting your AI agents waste tokens on circular grepping. Graphify provides the structural context they are missing, turning chaotic codebases into navigable maps.