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On October 26, 2026, Shao Meng published an article introducing the open-source project Graphify: first build a persistent knowledge graph for the entire code repository, then let the coding agent query the graph to modify code, rather than re-grepping and reading files on every round of a task. The article states that Graphify performs local tree-sitter AST parsing on the code, labels edges as EXTRACTED or INFERRED, and outputs the artifacts to graph.json, graph.html, and GRAPH_REPORT.md under graphify-out/, after which they can be queried using query, path, and explain. Shao Meng points out that whether marketing figures such as a stable 80% cost reduction can be achieved depends on the repository size, whether the graph stays fresh, and whether the agent is truly wired into graph queries; integration methods include registering the skill via graphify install, or mounting graph.json into Claude Code, Cursor, and Codex using graphify-mcp.
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Oct 26
Shao Meng
Graphify Open-Source Tool: Building a Persistent Knowledge Graph for the Entire Repository for Coding Agents to Query
The Graphify open-source project proposes first building a persistent knowledge graph for the entire code repository, then letting coding agents query the graph to modify code, rather than re-running grep and reading files on every task round. It performs local tree-sitter AST parsing on the code, labels edges as EXTRACTED or INFERRED, and outputs artifacts to graph.json, graph.html, and GRAPH_REPORT.md under graphify-out/, after which query, path, and explain can be used for querying. The author points out that whether marketing figures such as a stable 80% cost reduction can be achieved depends on repository size, whether the graph stays fresh, and whether the agent actually connects to graph queries; connection methods include registering a skill via graphify install, or mounting graph.json into Claude Code, Cursor, and Codex via graphify-mcp.