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Code Review Graph vs Coherence

Which one should you try?

Code Review Graph

Free

Best for Teams using Claude Code, Cursor, or other MCP-compatible AI coding assistants

Coherence

Free

Best for Teams using GitHub Copilot, Cursor, or Claude Code in active codebases

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

Code Review Graph

Code Review Graph

The tool builds a dependency graph of your codebase locally, then exposes that graph through MCP so Claude Code, Cursor, or any compatible assistant can ask targeted questions: which files are affected by this change, what is the impact radius, which communities cluster around this module. For large monorepos, this is the difference between a useful review context and a truncated one. The analysis runs entirely on your machine — no source code leaves the environment. The gap shows up when you need deep semantic understanding beyond structural imports; graph topology tells you what calls what, not whether the logic is correct.

Coherence

Coherence

Coherence scans the links between code, docs, architectural decision records, tests, metrics, generated files, and API endpoints — and flags where those links have snapped. It runs locally, deterministically, with no external API calls by default, which means it fits inside a pre-commit hook or CI pipeline without sending your codebase anywhere. The checks are rule-based, not LLM-driven, so results are repeatable run-to-run. Where it breaks: Coherence detects drift but does not fix it, so the remediation loop is still manual. Teams with loosely structured repos get limited signal until they invest time defining what relationships Coherence should track.

AttributeCode Review GraphCoherence
PricingFreeFree
Pricing checkedNot checked yetNot checked yet
Free trialNot statedNot stated
Open sourceYesYes
Has APIYesNo
Self-hosted optionYesYes
PlatformsLinux, macOS, Windows (Python 3.10+)Linux, macOS, Windows (via Go binary)
Released2023—
Pros
  • Reads only changed files and their structural dependencies rather than entire repositories, so your AI assistant's context window goes toward relevant code instead of noise from unrelated modules.
  • Impact radius scoring on change analysis, so reviewers see which downstream files carry the highest risk before opening a single one — rather than manually tracing imports across a large graph.
  • Fully local execution with no cloud dependency, so source code never leaves the environment — a hard requirement for teams under data residency or confidentiality constraints.
  • MIT-licensed with no paid tier and pip installation, so there is no procurement gate, no usage cap, and no feature that unlocks only when you upgrade.
  • Unified graph model across multiple languages, so a polyglot monorepo gets consistent structural queries without separate tooling per language stack.
  • Deterministic, no-LLM-call checks by default, so CI gates run at consistent speed and cost without per-execution API spend bleeding into your infrastructure bill.
  • Runs fully locally with a self-hosted option, which means your source code never leaves the machine during a standard scan — relevant for teams under compliance constraints that prohibit sending code to third-party services.
  • Git-native integration supports pre-commit hooks, so drift between a changed implementation file and its paired doc or test surfaces before the commit lands rather than after a reviewer catches it in review.
  • Tracks relationships across multiple artifact types — docs, ADRs, tests, generated files, metrics, API endpoints — in a single pass, so teams avoid writing separate linting scripts for each category of consistency problem.
  • Open-source with no commercial tier, so there is no feature wall that forces a pricing conversation before you can wire it into a production pipeline.
Cons
  • The graph captures import and call structure, not runtime behavior or semantic intent — questions like 'does this change break business logic in the billing module' return no useful answer, and teams with that requirement add a dedicated semantic search tool, maintaining two systems in parallel.
  • MCP is the only consumption interface; development environments without MCP support get nothing from this tool. Teams using IDE plugins or assistants that do not expose MCP cannot integrate it without building a custom bridge.
  • Community detection and architecture overview queries return structural clusters, which require a developer to interpret the output against domain knowledge — for teams onboarding to an unfamiliar codebase, the graph answers 'what talks to what' but not 'why.' Teams that need the 'why' typically switch to tools that ingest documentation and comments alongside the dependency graph.
  • Coherence only detects drift — it does not suggest or apply a fix. Every flagged inconsistency requires a manual triage and repair step, so in high-velocity repos where an AI agent is committing dozens of changes per day, the volume of flags can outpace the team's capacity to act on them.
  • The consistency checks are only as good as the ontology you define upfront. In a repository where file relationships have never been formally mapped, the initial configuration work is non-trivial, and the tool produces no signal on relationships it does not know about — meaning teams get a false sense of coverage before that mapping is complete.
  • There is no API surface and no programmatic output format described in the scraped source beyond CLI use, which means teams that want to feed drift results into a dashboard, ticketing system, or custom remediation workflow have to build that integration themselves from CLI output parsing.
  • Teams that need AI-assisted remediation alongside detection — where the tool not only flags that a doc is stale but also drafts the update — will hit the ceiling of what Coherence does and move to a heavier agentic code-review tool that closes the loop rather than opening a ticket.

Made up your mind?

Code Review Graph

Free

Best for Teams using Claude Code, Cursor, or other MCP-compatible AI coding assistants

Coherence

Free

Best for Teams using GitHub Copilot, Cursor, or Claude Code in active codebases

Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.