code-review-graph vs ECC
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 set of skills, memory, and security rules that makes AI coding agents work more carefully.
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 ECC if you need…
- Developers who already use Claude Code and want a shared workflow for planning, testing and review
- Teams that want consistent agent behavior across several coding tools
- People who want an agent to reuse past fixes through saved skills and memory
- Developers who care about security checks on agent-generated code
Side-by-side
| code-review-graph | ECC | |
|---|---|---|
| Type | Dev tool | Dev tool |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 32.6k | 295k |
| Setup difficulty | Beginner | Intermediate |
| How you get it | Install with a package manager | Install with a package manager |
| Main language | Python | JavaScript |
| 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.
ECC
ECC is an open-source add-on for AI coding agents such as Claude Code. It bundles reusable skills, workflows, memory and security checks so the agent plans before building, tests its changes, reviews its own work, and keeps useful notes between sessions. You install it once instead of writing the same process instructions into every prompt. The README says it works best with Claude Code and has native Codex and Qoder plugin paths, with more limited adapters for Cursor, OpenCode, Gemini and Zed.
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
ECC
- Developers who already use Claude Code and want a shared workflow for planning, testing and review
- Teams that want consistent agent behavior across several coding tools
- People who want an agent to reuse past fixes through saved skills and memory
- Developers who care about security checks on agent-generated code
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
ECC
- MIT-licensed and open source, with a large public following on GitHub
- Installs as a plugin through a documented slug, so setup is mostly guided
- Explicit about which tools have full support and which have limited adapters
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
ECC
- People who do not use any AI coding agent, since the project only adds value on top of one
- Users who need a single, simple tool with a short learning curve, because the README covers many harnesses and components
- Anyone who needs full feature parity across Cursor, Gemini or Zed, since the README describes those adapters as capability-limited
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
ECC
- The README describes a large system with many parts, which makes it hard to know what to use first
- Best support is for Claude Code; other tools get capability-limited adapters
- Some paid hosted features (ECC Pro GitHub App for private repos) are mentioned alongside the free project
Getting started
code-review-graph
Install from PyPI
pip install code-review-graphConfigure detected AI coding tools
code-review-graph installECC
Install the npm package listed in the README (exact command not shown in the truncated text)
git clone https://github.com/affaan-m/ECCFrequently asked questions
What is the difference between code-review-graph and ECC?
code-review-graph: Builds a local map of your codebase so AI coding tools read only the files a change touches. ECC: A set of skills, memory, and security rules that makes AI coding agents work more carefully. code-review-graph suits developers who use an mcp-compatible ai coding assistant on a large repository, while ECC suits developers who already use claude code and want a shared workflow for planning, testing and review.
Are code-review-graph and ECC free?
code-review-graph: Free and open source. ECC: Free and open source.
Which is easier to set up, code-review-graph or ECC?
code-review-graph is easier to get started with (rated beginner); ECC is rated intermediate and usually needs more setup.
Is code-review-graph or ECC more popular?
ECC has more GitHub stars (295k) 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 ECC?
Other options people compare them with include Trail-code, codebase-memory-mcp, 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.