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Code Review Graph vs CoreTex

Which one should you try?

Code Review Graph

Free

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

Visit Code Review Graph See full review

CoreTex

Free

Best for Teams running multiple coding agents on the same repo

Visit CoreTex See full review

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.

CoreTex

CoreTex

Orbit pulls one dependency-ordered task at a time from your backlog, hands it to whichever coding agent you connect, then refuses to mark it done unless tests, lint, and type checks pass. Every run writes four JSON or markdown artifacts: what the agent returned, how the work scored against a rubric, a human-readable mission log, and a recommendation to accept, iterate, or stop. The agent-neutral contract means you can swap Claude for Codex behind the same harness and compare structured artifacts instead of vibes. The ceiling appears fast on large repos: Orbit is intentionally small, so teams needing parallel agent execution, complex branching between task types, or CI integration will find themselves extending the harness manually.

AttributeCode Review GraphCoreTex
PricingFreeFree
Pricing checkedNot checked yetNot checked yet
Free trialNot statedNot stated
Open sourceYesYes
Has APIYesNo
Self-hosted optionYesYes
PlatformsLinux, macOS, Windows (Python 3.10+)Python (macOS, Linux, Windows via WSL)
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.
  • Validation gates block task advancement until tests and lint actually pass, so you never merge agent output that only claimed to work.
  • Structured artifacts (agent-result.json, evaluation.json, review.json, progress.md) persist across sessions, which means long-running workflows have a durable audit trail rather than a chat history no one can reproduce.
  • Agent-neutral JSON contract lets you swap Claude, Codex, Cursor, or any CLI-based agent behind the same harness, so comparing two agents produces structured rubric scores instead of anecdotes.
  • Dependency-aware backlog selection keeps each orbit scoped to one task at a time, which means agents are not handed an ambiguous chunk of work that produces unverifiable, sprawling diffs.
  • MIT licensed and self-hosted, so the audit trail lives on your infrastructure and there is no external service holding your agent execution history.
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.
  • Orbit executes one orbit at a time in sequence — there is no built-in mechanism for running agents in parallel on separate tasks. Teams with high-volume backlogs hit this ceiling immediately and end up scripting their own concurrency layer, at which point they are maintaining two systems.
  • There is no native CI integration described in the vendor documentation. Teams that want Orbit's validation gates to run automatically on pull requests must wire that connection themselves, which adds maintenance surface that defeats the 'intentionally small' design goal.
  • Complex multi-step workflows with branching logic — where the next task depends on what the prior orbit returned beyond a simple pass/fail — have no built-in expression mechanism. Teams that need conditional routing between task types switch to a full agent orchestration framework and use Orbit's artifact format, if at all, as a reference model rather than a runtime.

Made up your mind?

Code Review Graph

Free

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

Visit Code Review Graph See full review

CoreTex

Free

Best for Teams running multiple coding agents on the same repo

Visit CoreTex See full review

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

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