code-review-graph vs codebase-memory-mcp
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
Builds a local map of your codebase so AI coding tools read only the files a change touches.
Open source · MIT
A local tool that maps your code, docs and infrastructure into a graph so AI coding agents can query it.
The short answer
Choose code-review-graph if you need…
- Developers who use an MCP-compatible AI coding assistant on a large repository
- Teams that want code reviews to focus on the files a change affects
- People who want the code map kept local rather than sent to a hosted service
- Users of supported tools such as Claude Code, Cursor or Codex who want automatic setup
Choose codebase-memory-mcp if you need…
- Developers who use an MCP-capable AI coding agent on a large codebase
- Engineers who want to find where a feature lives and how calls connect across files
- Teams that want to estimate which code and tests a change could affect
- People who prefer keeping source analysis on their own machine
Side-by-side
| code-review-graph | codebase-memory-mcp | |
|---|---|---|
| Type | Dev tool | Dev tool |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 32.6k | 48k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Install with a package manager | See the README |
| Main language | Python | C |
| Last code update | 8 days ago | 4 days ago |
What each one is
code-review-graph
code-review-graph is a Python tool that scans a code repository with Tree-sitter and stores a graph of its functions, classes, imports and calls in a local database. AI coding assistants connected to it through MCP can then ask for a small set of relevant files instead of reading the whole project. The README says this is aimed at reducing the context an assistant needs during code reviews and on large repositories.
codebase-memory-mcp
codebase-memory-mcp is a server that reads a code repository and builds a persistent local knowledge graph of its symbols, files, documentation and infrastructure files. It exposes this graph through the Model Context Protocol (MCP) so an AI coding agent can ask structural questions, such as what calls a function or what depends on a file, instead of reading many files. Indexing and queries run on your own machine, and the README says no hosted service or API key is needed.
Best for
code-review-graph
- Developers who use an MCP-compatible AI coding assistant on a large repository
- Teams that want code reviews to focus on the files a change affects
- People who want the code map kept local rather than sent to a hosted service
- Users of supported tools such as Claude Code, Cursor or Codex who want automatic setup
codebase-memory-mcp
- Developers who use an MCP-capable AI coding agent on a large codebase
- Engineers who want to find where a feature lives and how calls connect across files
- Teams that want to estimate which code and tests a change could affect
- People who prefer keeping source analysis on their own machine
Strengths
code-review-graph
- Runs locally and stores the graph in a local database (SQLite, per the README's pipeline diagram)
- One install command configures many AI coding tools and reports which were detected
- Incremental updates re-parse only files whose SHA-256 hash changed, per the README
codebase-memory-mcp
- Runs locally with no hosted service or API key needed for indexing and queries, per the README
- Ships as a native binary; the README says no Docker or language runtime is required
- Integrates with many agent clients through an installer that writes their configuration
Not ideal for
code-review-graph
- Projects in languages outside the supported list, since parsing coverage is limited to the listed languages
- Users who do not use an MCP-compatible AI tool, since the main interface is through MCP
- Anyone expecting the token savings to hold exactly, since the README's numbers come from one 2026-08-02 snapshot and it says the repository has grown since
codebase-memory-mcp
- Users who want a hosted service or cloud-managed indexing, since the README describes local processing only
- People without an MCP-capable agent or CLI experience, since setup assumes some terminal comfort
- Projects needing change-aware test selection for languages beyond the supported C/C++ harnesses, since Smart CI is described as experimental and limited to supported C/C++ test harnesses
Trade-offs
code-review-graph
- The token-reduction figures are from a single snapshot and the README says current numbers are larger
- Parsing is limited to the listed languages, with fallbacks where no Tree-sitter grammar exists
- Requires Python 3.10 or newer and depends on MCP-compatible tools to be useful
codebase-memory-mcp
- CBM Lens, the visual companion, is in open beta (v0.0.1), and Smart CI is labeled experimental
- Support varies by agent client, so you should check the integration matrix for yours
- The README is truncated, so details such as full language coverage and exact limits are not confirmed here
Getting started
code-review-graph
Install from PyPI
pip install code-review-graphConfigure detected AI coding tools
code-review-graph installcodebase-memory-mcp
Install the latest release (macOS / Linux)
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bashStart CBM Lens after installation
codebase-memory-mcp daemon start --open --port=9749Frequently asked questions
What is the difference between code-review-graph and codebase-memory-mcp?
code-review-graph: Builds a local map of your codebase so AI coding tools read only the files a change touches. codebase-memory-mcp: A local tool that maps your code, docs and infrastructure into a graph so AI coding agents can query it. code-review-graph suits developers who use an mcp-compatible ai coding assistant on a large repository, while codebase-memory-mcp suits developers who use an mcp-capable ai coding agent on a large codebase.
Are code-review-graph and codebase-memory-mcp free?
code-review-graph: Free and open source. codebase-memory-mcp: Free and open source.
Which is easier to set up, code-review-graph or codebase-memory-mcp?
code-review-graph is easier to get started with (rated beginner); codebase-memory-mcp is rated intermediate and usually needs more setup.
Is code-review-graph or codebase-memory-mcp more popular?
codebase-memory-mcp has more GitHub stars (48k) than code-review-graph (32.6k). Popularity isn't the same as fit — check which one matches your use case above.
What are other alternatives to code-review-graph and codebase-memory-mcp?
Other options people compare them with include Trail-code, ECC, wshobson/agents, claude-context, CodeBurn.
Keep comparing
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.