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Graphify and code-review-graph both build repository graphs that can give Claude Code and other AI coding workflows a more focused view of a codebase, but they solve different problems. Graphify targets broader context across code and non-code materials; code-review-graph concentrates on code structure and review impact. They are separate projects, and “Graphify” is also used by unrelated repositories, so this comparison refers specifically to Rojios/Graphify v2 and the nxpatterns/code-review-graph project.
Which one fits your work?
| Need | Better starting point | Why |
|---|---|---|
| Map relationships across source code and documentation, papers, or images | Graphify | Its v2 documentation describes a graph that combines deterministic code-structure extraction with semantic extraction of non-code material. |
| Understand a code change’s callers, dependents, and related tests | code-review-graph | Its documentation emphasizes structural code context, review queries, and change blast-radius analysis. |
| Keep a repository graph current as code changes | Either, subject to setup | Both projects document incremental updating and hooks; actual triggers and platform support depend on the installed version and configuration. |
| Choose based on a guaranteed token saving | Neither on the available benchmark claims alone | The projects report results from different corpora and methods, not a controlled head-to-head trial. |
For architecture exploration across mixed materials, start with Graphify. For code review and tracing the impact of a diff, start with code-review-graph. That is a workflow-based recommendation, not evidence that one tool performs better across every large repository.
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How Graphify builds broader context
The Graphify v2 README describes two distinct processing passes. Its code pass uses deterministic AST extraction to capture structure. A separate assistant-model-backed pass extracts semantic relationships from non-code sources such as documents, papers, and images. The results are merged into a NetworkX graph, clustered, and exported as interactive HTML, queryable JSON, and a Markdown report. Relationships are labeled EXTRACTED, INFERRED, or AMBIGUOUS, helping distinguish source-supported links from inference or uncertainty. See the Graphify v2 README for its described pipeline and output formats.
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Graphify’s Claude Code integration documentation describes installing a skill and hooks that can steer Claude toward graph queries before it opens or greps files. The documented CLI includes graphify query, graphify path, and graphify explain. Query results can include file-and-line citations and relationship provenance labels. The documentation also describes optional strict behavior and an optional MCP server; the skill and CLI do not require MCP.
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Updating the graph
Graphify documents graphify update . as re-extracting changed code with AST-only processing. Its integration page also describes graphify hook install for updating after commits and checkouts. The documented update path is therefore not the same as rerunning the semantic extraction pass over non-code material. Check the current integration documentation for the behavior relevant to your setup.
How code-review-graph supports code review
The code-review-graph README describes a Tree-sitter-based structural graph. Its nodes include functions, classes, and imports; its edges include calls, inheritance, and test coverage. The review workflow uses relationships around changed code to identify callers, dependents, and tests, then supplies a smaller review context to an assistant.
Commands and assistant interfaces
The project lists commands for building, updating, checking status, watching, visualizing, and serving a graph. It also documents MCP tools for impact radius, review context, graph queries, semantic search, statistics, and related information. These interfaces make it a more directly review-oriented choice when the key question is what a code change may affect.
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Keeping code context current
code-review-graph documents hooks for file edits and commits. Its README’s quick start uses pip install code-review-graph followed by code-review-graph install, and lists Python 3.10+ and uv as requirements. Its separate usage guide identifies itself as applying to v2.3.6 and describes platform-specific MCP configuration. Treat version and platform details as changeable: verify the current guide and confirm which events your installed hooks actually handle.
Installation and compatibility checks
The documented commands below are source snapshots, not a guarantee that package names, requirements, or integrations will remain unchanged. Check the linked primary documentation before installing.
| Project | Documented quick start | Documented prerequisites or notes |
|---|---|---|
| Graphify v2 | pip install graphifyy && graphify install |
README lists Python 3.10+ and Claude Code. The package is named graphifyy, while the command is graphify. Optional MCP installation is documented as uv tool install "graphifyy[mcp]". Source |
| code-review-graph | pip install code-review-graph, then code-review-graph install |
README lists Python 3.10+ and uv. Consult the usage guide for version-specific and platform-specific MCP configuration. README; usage guide |
Do not assume that a listed assistant integration implies identical support for every AI coding agent, operating system, or programming language. Check the current installation matrix and language coverage for the version you plan to use.
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Privacy depends on which processing path you enable
Graphify distinguishes local structural parsing from other processing. Its official product FAQ says structural parsing stays on-device, while its hosted service stores connected repositories and semantic extraction can use a model API unless configured locally. That means “local parsing” alone does not establish that every part of a Graphify workflow stays local. Review the official Graphify product FAQ and the configuration for any semantic or hosted features you enable.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchescode-review-graph describes local SQLite storage without an external database or cloud dependency in its README. As with any tool, confirm the installed version’s behavior and review its configuration before using it with sensitive repositories.
Best Value
How much weight should token benchmarks carry?
The published numbers are project-reported and not directly comparable. Graphify’s v2 README reports 71.5× fewer tokens per query for a mixed corpus of repositories, papers, and images; it also lists examples with different results, including 5.4× and about 1×. The page does not state a year for the figure. These results describe the project’s benchmark, not a guaranteed saving for a particular codebase. See the Graphify v2 README.
code-review-graph’s README reports a 6.8× average reduction from a review benchmark covering six real commits, comparing full-source reading with compact structural summaries. It also presents larger results for specific repositories, including a Next.js example. The page does not state a year for the figure. These are the project’s reported results, not an independent evaluation or a Graphify comparison. See the code-review-graph README.
Because the corpora, tasks, and methods differ, neither figure establishes which tool uses fewer tokens on the same repository under the same conditions. Treat token savings as a hypothesis to validate against your own review or exploration tasks, not as the deciding score.
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Quick Recap
A practical selection checklist
- Choose Graphify when the useful context spans code plus documents or other non-code sources, and its graph provenance labels suit how you want to inspect relationships.
- Choose code-review-graph when your main job is tracing the impact of changed code through callers, dependencies, and tests.
- For either option, verify language coverage, assistant support, operating-system compatibility, and hook triggers in the current version’s documentation.
- If repository data must remain local, inspect every enabled processing path—not just where the structural graph is stored—including semantic extraction, hosted features, and model API configuration.
- Test updates on a working branch: make a small code change, run the documented update or hook flow, and check whether the graph reflects it. Separately verify how documentation or other non-code changes are handled.
- Evaluate usefulness on representative tasks: whether the graph finds relevant relationships, provides traceable evidence, and reduces the context you actually need to inspect. Do not infer those outcomes from another project’s benchmark.
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