codegraph vs graphify
Local code knowledge graphs compared
codegraph and graphify both build queryable knowledge graphs to help developers and AI coding assistants understand project structure and relationships. codegraph focuses on keeping a local code index current, while graphify can also include documentation and other project files and generate reusable graph outputs.
codegraph: Give Coding Agents a Local Code Knowledge Graph
CodeGraph builds and maintains a local graph of code structure so AI coding agents can retrieve relevant source, call paths, and change impact without exploring files one by one. It connects to multiple agents through MCP and supports many languages.
graphify: Turn Codebases into Queryable Knowledge Graphs
Graphify builds a queryable knowledge graph from code, documentation, schemas, and other project files. It helps developers and AI coding assistants explore relationships, trace paths, and understand codebases without relying on a vector store.
| codegraph | graphify | |
|---|---|---|
| Language | C | Python |
| License | MIT | Apache-2.0 |
| Stars | 73.1k | 124k |
| Forks | 4.7k | 11.9k |
| Last analyzed | Oct 3, 2026 | Oct 3, 2026 |
Key differences
- codegraph indexes code symbols, relationships, imports, and calls in local SQLite; graphify analyzes code with tree-sitter and can also extract information from documents, media, schemas, and configuration files.
- codegraph watches files and incrementally syncs changes; graphify generates a graph for exploration, with no ongoing file-watching capability described.
- codegraph offers an MCP server and CLI with commands for symbol search, caller and callee tracing, impact checks, and finding affected tests; graphify supports graph queries, shortest paths, concept explanations, and graph visualizations and reports.
- codegraph is written in C and licensed under MIT; graphify is written in Python and licensed under Apache-2.0.
- codegraph stores project data locally in SQLite with FTS5 search; graphify uses local code parsing, while semantic extraction for documents and media can require an assistant model or configured API backend.
- Both integrate with AI coding assistants through MCP, while graphify also offers a skill and codegraph provides an installer for configuring supported agents.
Choose codegraph if you…
- need a local index that incrementally stays current as project files change.
- want CLI tools to trace callers, check change impact, or find affected tests.
- prefer code-focused retrieval of source, call paths, and impact context through MCP or a CLI.
Choose graphify if you…
- want one graph to connect code with documentation, media, schemas, or configuration files.
- need graph visualizations, Markdown reports, or JSON graph output for reuse.
- want tree-sitter-based local code extraction and can use a configured model or backend for semantic extraction of non-code files.
Compare the numbers side by side, or add more projects
This comparison is generated with AI from the OSRepos analyses of both projects. Always check each project's repository and documentation before choosing.