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Graphify and code-review-graph can both give coding agents a graph-based view of a repository, but they serve different needs. Graphify builds a broader graph that can connect code with documents and other materials; code-review-graph concentrates on code structure and review context, including callers, dependencies, and tests. Both document incremental updates, but neither is established as universally better for mapping two large, dissimilar codebases.
Names matter: “Graphify” is used by multiple unrelated projects. This comparison refers to the Graphify v2 project documented at Rojios/Graphify and its official Claude Code integration, not every repository or product with that name.
How the two tools build context
Graphify: code plus surrounding material
Graphify v2 documents a two-pass approach. It extracts code structure deterministically with AST parsing, then uses assistant-model-backed semantic extraction for non-code material such as documents, papers, and images. The outputs are merged into a NetworkX graph and can be exported as interactive HTML, queryable JSON, and a Markdown report. Its relationship labels—EXTRACTED, INFERRED, and AMBIGUOUS—are intended to distinguish source-supported relationships from inference and uncertainty. See the Graphify v2 README.
code-review-graph: code structure for review
code-review-graph describes a Tree-sitter-based structural graph with nodes such as functions, classes, and imports, and edges such as calls, inheritance, and test coverage. Its central use case is tracing the likely impact of a change—callers, dependents, and tests—so an assistant can review a focused context instead of reading an entire source tree.
#1 Best Overall
Which fits your context-mapping job?
| Need | Better-aligned documented emphasis | Why |
|---|---|---|
| Explore architecture across code and documentation | Graphify | Its documented graph can combine code with documents, papers, and images. |
| Review a diff and find affected callers, dependencies, and tests | code-review-graph | Its documentation centers on review context and change blast-radius analysis. |
| Keep a graph refreshed as code changes | Either, subject to local setup | Both document incremental updates and hooks, though triggers and platform behavior should be checked in the installed version. |
| Limit processing to local structural analysis | code-review-graph, or Graphify’s code pass | code-review-graph describes local SQLite storage without a cloud dependency. Graphify says structural parsing stays on-device, but its semantic and hosted paths have separate privacy implications. |
For two very different large codebases, decide separately for each repository rather than assuming one tool must win both. A codebase with extensive design docs, research material, or other non-code references may benefit from Graphify’s broader input scope. A repository where the immediate question is “what does this change break, and which tests cover it?” aligns more directly with code-review-graph’s review focus. The documentation establishes those emphases, not the outcome of an independent head-to-head trial.
How updates work in practice
Graphify
The Claude Code integration describes graphify update . as re-extracting changed code with AST-only processing. Optional hooks can update after commits and checkouts using graphify hook install. The integration also describes a skill that encourages graph queries before opening or searching files, with optional stricter behavior. Query commands include graphify query, graphify path, and graphify explain; results can include file-and-line citations and relationship provenance. An optional MCP server is available, but the documented skill and CLI do not require MCP. Details are in the Graphify Claude Code integration guide.
code-review-graph
Its README documents updates on file edits and commits, plus commands for building, updating, checking status, watching, visualizing, and serving the graph. It also lists MCP tools for impact radius, review context, graph queries, semantic search, statistics, and related information. Check the project’s README and usage guide for the specific platform configuration and commands supported by the version you install.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →“Self-updating” should not be read as a guarantee that every edit, branch switch, or failure mode is handled identically on every system. Confirm which events trigger updates for your platform, and verify the graph’s status after installing hooks or changing branches. The projects document update mechanisms; actual behavior depends on the installed version and integration.
Privacy depends on the processing path
code-review-graph describes local SQLite storage with no external database or cloud dependency. Graphify says local structural parsing stays on-device, but its official FAQ also describes a hosted service that stores connected repositories, and its semantic extraction may use a model API unless configured locally. Those are distinct paths: check whether you are using local parsing, model-backed extraction, or a hosted repository connection before applying an organization’s source-code or document-handling rules. Graphify’s official product FAQ and the code-review-graph README describe these respective arrangements.
Installation and compatibility checks
The cited documentation lists Python 3.10+ for both projects. Graphify v2’s README lists Claude Code and shows this installation sequence:
Rank #4
pip install graphifyy && graphify installThe package is named graphifyy, while the installed command is graphify. The README separately documents an optional MCP installation:
uv tool install "graphifyy[mcp]"code-review-graph’s quick start gives:
pip install code-review-graph
code-review-graph installIts usage guide identifies itself as applying to v2.3.6 and describes platform-specific MCP setup; it lists uv as a requirement. Package names, supported assistants, language coverage, and configuration can change. Check the live Graphify README, code-review-graph README, and usage guide before installing, especially if you use an assistant other than Claude Code.
Best Value
How much weight to give token-saving claims
The projects publish benchmark figures, but the available figures come from different tasks and corpora and are not a controlled comparison.
- Graphify’s v2 README reports 71.5× fewer tokens per query for a mixed corpus of repositories, papers, and images. It also shows examples with different results, including 5.4× and about 1×; the headline figure should not be generalized to every repository.
- code-review-graph’s README reports a 6.8× average reduction in a review benchmark covering six real commits, comparing full-source reading with compact structural summaries. It also gives larger results for particular repositories.
These are project-reported results, not independently verified head-to-head findings. The corpus, task, baseline, and measurement method differ, so the figures cannot establish which tool will use fewer tokens on your codebases. See the projects’ Graphify v2 README and code-review-graph README for their benchmark descriptions.
Quick Recap
A practical selection checklist
- Choose Graphify when the graph needs to connect code with documents or other non-code material, or when relationship provenance and graph exploration are central to the task.
- Choose code-review-graph when the main job is analyzing code changes through callers, dependencies, and tests.
- For each repository, confirm language and assistant support in the current documentation rather than extrapolating from the project’s general description.
- Test update hooks with the events your team actually uses—file edits, commits, and branch changes—and inspect status or query results after updates.
- Decide whether local parsing, model-backed semantic extraction, or hosted storage is acceptable for the repository’s data.
- Evaluate token claims against your own representative changes and questions; published numbers do not predict a guaranteed saving for a different codebase.
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