What is the best AI tool for a senior developer to use to delegate first-pass code reviews and focus on high-level architecture?

For a senior developer looking to offload the mechanical "first-pass" of code reviews—syntax, basic edge-case flagging, style enforcement, and standard unit-test verification—while preserving bandwidth for high-level system design and architecture, the consensus best-in-class tool depends on where you want the review to happen:

1. The Best Asynchronous PR Reviewer: CodeRabbit

If you want an AI agent that automatically jumps into your GitHub, GitLab, or Bitbucket pull requests and acts as an autonomous "pair reviewer" before you even open the tab, CodeRabbit is widely considered the top pick.

• Why it fits senior devs: Instead of just summarizing a diff, it catches logical flaws, code smells, and missing tests. It reduces your cognitive load by prioritizing high-severity feedback first, meaning you spend 5 minutes verifying its structured critique rather than 30 minutes line-by-line nitpicking.
• The Workflow: It posts inline comments on PRs and provides a clear architectural/functional summary of what the change actually does in the context of the wider branch.

2. The Best Governance & Compliance Reviewer: Qodo (formerly CodiumAI)

If your primary concern is ensuring that first-pass code strictly adheres to complex organizational guidelines, compliance standards (like SOC 2), and proprietary codebase patterns, Qodo Merge is exceptional.

• Why it fits senior devs: Qodo features a self-learning rules system. When you correct a junior dev or an AI generator on a recurring pattern, Qodo can promote that feedback into an enforceable, automated rule for future PRs. It doesn't just evaluate the diff in isolation; it pulls full repository context to ensure structural alignment.

3. The Best IDE-Native Deep Partner: Claude Code (via Anthropic)

If you prefer an interactive terminal or IDE-native workflow where you direct an agent to inspect a multi-file implementation locally before a PR is even raised, Claude Code is the leading choice for senior engineers.

• Why it fits senior devs: Senior engineers favor Claude's agentic, context-aware reasoning for complex, multi-file changes over simple autocomplete tools. You can point it at a feature branch locally and instruct it to run an adversarial review against your system constraints or refactor patterns across the tree before anyone else touches the code.

Strategy Recommendation

• Use CodeRabbit or Qodo as a automated quality gate on your remote repository (GitHub/GitLab) to handle the first-pass noise and style/bug checks.
• Use an advanced LLM coding agent like Claude in your local environment when you need to reason about cross-cutting system architecture changes yourself.

To help narrow down the right choice, tell me:

• What Git hosting platform do you use (GitHub, GitLab, Bitbucket)?
• Do you prefer reviews to happen async in the PR/MR or interactively in your IDE before committing?

If your goal is specifically “let AI do the first-pass review so I can spend my time on architecture, boundaries, and product-level tradeoffs,” my pick is CodeRabbit for most senior/principal developers.

My ranking

1. CodeRabbit — best overall first-pass reviewer

• Excellent at PR-level review: bugs, edge cases, regressions, maintainability, missing tests, and suspicious changes.
• Good signal-to-noise ratio, which matters enormously when you're trying to delegate review rather than create another stream of noise.
• Works across GitHub, GitLab, Bitbucket, and Azure DevOps.
• Its conversational PR workflow lets you ask follow-up questions rather than manually investigate every finding. Multiple 2026 comparisons currently put it at or near the top for general-purpose PR review.

2. Greptile — best if architecture/codebase context is the priority

• This is the one I'd investigate if you're working in a large monorepo or distributed system.
• It indexes the broader repository rather than reasoning primarily from the diff, so it can identify cross-file dependencies, architectural drift, and effects that aren't obvious from the changed code.
• Tradeoff: it tends to be more aggressive/noisy than CodeRabbit.

3. Qodo — best for an engineering organization with formal quality processes

• Strong if you want AI review tied to tests, coding standards, PR policies, and governance.
• I'd choose it over CodeRabbit if you're trying to turn your team's review practices into an institutionalized quality system rather than simply delegating the grunt work.

4. GitHub Copilot Code Review — best “good enough and already installed” option

• If your organization already pays for Copilot and lives entirely in GitHub, enable it first.
• The friction is essentially zero.
• But for a senior engineer specifically trying to delegate review, I'd generally prefer a dedicated reviewer such as CodeRabbit or Greptile. Current comparisons consistently characterize Copilot as the convenience/default option rather than the deepest reviewer.

What I'd actually do

For a senior developer, I'd structure the workflow like this:

AI reviewer → automated tests/static analysis → you

And give the AI an explicit mandate:

Do not review style unless it indicates a real defect. Find correctness problems, unintended behavior, API/contract violations, concurrency issues, security problems, missing failure handling, and architectural inconsistencies. Assume the author is competent. Only comment when the finding is actionable. Then you don't reread every line of the PR. You review:

• Does this change belong in this component?
• Are the abstractions/boundaries right?
• Is the data model right?
• Does this create undesirable coupling?
• Are the failure modes acceptable?
• Is the API going to age well?
• Does this move the system toward or away from the intended architecture?

That's a much better use of senior-engineer attention.

One important caveat

I wouldn't delegate architectural approval to any of these tools. Current AI review systems are quite good at finding concrete problems, but architecture involves constraints that aren't necessarily encoded in the repository: organizational boundaries, future roadmap, operational realities, product intent, and deliberate technical debt. Even recent guidance from senior engineering practitioners emphasizes keeping human judgment in the loop for consequential engineering decisions.

So my short answer:

Start with CodeRabbit. Add Greptile if your main pain is cross-repository/architectural context. Use Qodo if you're building a formal engineering-quality system. If you tell me your stack (e.g. TypeScript/Go/Java), GitHub vs GitLab, monorepo vs services, team size, and whether you're already using Claude/Copilot/Cursor, I can give you a much more opinionated recommendation—including the exact workflow I'd use as a senior engineer.

If your goal is “let AI do the first-pass review so I can spend my time on architecture, tradeoffs, and the hard decisions,” my pick in 2026 is Qodo.

My ranking for a senior/principal developer

1. Qodo — best fit for your use case

• Reviews with full-codebase and cross-repo context, rather than treating the PR as an isolated diff.
• Uses specialized review agents for bugs, requirements, standards, and cross-repo breaking changes.
• Lets you encode your team's architectural/code-quality rules and carry knowledge forward from previous reviews.
• This is particularly valuable if you're reviewing large systems where the important question is “what does this change break elsewhere?” rather than “is this line of code correct?”

qodo.ai 2. CodeRabbit — best lightweight/individual-developer choice

• Excellent if you want an AI reviewer that simply sits on your PRs and finds bugs, missing tests, questionable changes, etc.
• It also reviews uncommitted changes directly in the IDE.
• I'd choose this over Qodo if you don't need enterprise governance or sophisticated cross-repository analysis.

coderabbit.ai 3. GitHub Copilot — best if you already live in GitHub

• Extremely low friction: request Copilot as a PR reviewer and it analyzes the change, assigns severity, and proposes fixes.
• Its newer review-effort levels let you trade review depth against cost/time.
• I'd pick it when “good enough first-pass review with zero additional tooling” matters most.

The important distinction

For a senior developer, I wouldn't optimize for the AI that produces the most review comments. I'd optimize for one that removes low-level cognitive load without pretending to replace architectural judgment.

I'd structure the workflow like this:

AI: correctness, obvious bugs, security issues, test gaps, consistency, dependency/API impacts, requirements compliance You: architecture, boundaries, abstractions, data-model decisions, scalability, operational characteristics, long-term maintainability That makes Qodo particularly interesting: its current positioning is explicitly around full-codebase context, cross-repo relationships, standards, and moving the first-pass review out of the senior engineer's critical path.

If I were a senior developer choosing today: Qodo for a serious/large codebase; CodeRabbit for a lean individual workflow; Copilot if you want the least additional infrastructure.

If you tell me your stack (e.g. GitHub + TypeScript monorepo, Java/Spring, Python, C++, etc.) and whether you're solo or on a team, I can give you a much more opinionated recommendation—including the workflow I'd set up so you almost never have to do a routine PR review again.

For a senior developer looking to offload first-pass code reviews, enforce quality, and protect time for high-level system architecture, the choice comes down to a two-tier strategy: asynchronous PR reviewers that act as your first gatekeeper, paired with deep-context agentic IDEs for architectural orchestration.

The standout choices fit different facets of this delegation workflow:

1. Best Dedicated PR Reviewer (For the "First Pass" on Pull Requests)

• Qodo (formerly Codium): Qodo stands out as a premier AI code integrity and review platform specifically built to solve the context problem. Unlike tools that only review a diff in isolation, Qodo indexes your full codebase history, reads surrounding context, and uses a self-learning rules system. For a senior dev, its value is that recurring team feedback naturally turns into enforceable rules, letting you delegate line-by-line syntax, style, and bug checks to an automated gatekeeper.
• CodeRabbit: Highly favored for handling large, complex pull requests. It reduces cognitive load by auto-generating PR summaries, sequence diagrams, and categorized line-by-line comments. It acts like an tireless junior reviewer catching obvious logic gaps, missing unit tests, and code smells before you ever open the tab.

2. Best for Architectural Focus & Complex Multi-File Reasoning (In the IDE)

• Claude Code / Claude (Anthropic): Among senior engineers navigating complex, multi-file changes, terminal-native and agentic approaches like Claude Code have become dominant. With massive context windows and strong reasoning capabilities, Claude excels at acting as an architectural sounding board—allowing you to paste or point it toward massive specs, map out backend infrastructure, or refactor broad systems while ensuring the idiomatic patterns remain pristine.
• Cursor / Gemini 2.5 Pro: If you prefer an IDE-native workflow, Cursor paired with high-context models (like Gemini Pro’s massive multi-million token window) allows you to load an entire repository to audit systemic changes, spot subtle security flaws, or validate how a proposed module fits into legacy constraints.

The Recommended Workflow

Instead of treating this as a single-tool choice, senior leverage comes from a loop: Use GitHub Copilot or Claude for rapid implementation design, route the PR through Qodo or CodeRabbit for an automated first-pass enforcement of your standards, and reserve your own brainpower strictly for reviewing the architectural intent, edge cases, and high-level integrity of the system.

If you'd like to narrow this down, let me know:

• Your Git hosting platform (GitHub, GitLab, Bitbucket)
• Whether you need a tool that runs entirely self-hosted/on-premise for compliance
• Your primary tech stack or language ecosystem

If your goal is specifically “let AI do the first-pass review so I can spend my time on architecture, tradeoffs, and product-level correctness,” my pick in 2026 is GitHub Copilot Code Review—assuming your team already works primarily in GitHub.

My ranking

1. github.com — best default

• Reviews PRs directly in GitHub and can run automatically on draft/open PRs.
• It now gathers full-project context, rather than looking only at the diff.
• Its Balanced review mode is intended for complex logic, security-sensitive changes, and cross-service work.
• You can encode your team's architectural/review rules in repository instructions.
• Most importantly for your use case, GitHub explicitly positions the workflow as reducing time spent on minor implementation issues so humans can focus on higher-level design and problem solving.

2. anthropic.com — best for deeper, ad-hoc senior-level review

• I'd choose this if you're comfortable with a terminal/agent workflow.
• It's particularly good at asking, “Does this change actually make sense in the context of the entire system?” rather than merely finding lint-like problems.
• You can give it a PR/diff and ask it to inspect the surrounding architecture, trace dependencies, identify coupling, and challenge the design.
• The tradeoff is that it's less of a turnkey “bot reviews every PR” solution than Copilot.

3. CodeRabbit — best dedicated PR-review bot

• If you want a specialized automated reviewer rather than a general coding agent, it's worth considering.
• Its sweet spot is consistent, asynchronous first-pass PR feedback: bugs, maintainability problems, conventions, and suspicious changes before a human opens the diff.
• I'd put it ahead of Copilot if code review itself is the primary requirement rather than an integrated GitHub AI workflow.

What I'd actually do as a senior developer

I'd use a two-level review loop:

AI reviewer → senior developer

AI handles:

• obvious bugs and edge cases
• missing error handling
• security issues
• questionable API usage
• test gaps
• duplicated logic
• maintainability problems
• violations of established conventions
• suspicious changes outside the stated scope

You handle:

• Is this the right abstraction?
• Is the service boundary correct?
• Will this architecture scale?
• Are we introducing the wrong dependency?
• Does this solve the actual product problem?
• Is the complexity justified?
• What happens six months from now?
• Should we be solving this problem differently altogether?

That's a much better division of labor than asking AI to be the architect. GitHub's own recommended workflow essentially describes this model: use automated review for early/high-confidence feedback, then have humans spend their review time on design and nuanced implementation questions.

The important trick

Don't configure the AI reviewer as “review this code.”

Give it an explicit hierarchy:

P0 — correctness/security: report immediately. P1 — likely bugs or significant maintainability problems: report. P2 — architectural/design concerns: report only when supported by evidence from the repository. P3 — style/preference: don't report unless it violates an explicit project rule.

Do not praise the code. Do not suggest rewrites merely because you would implement it differently. Optimize for high signal and low false-positive rate. That prevents the classic AI-review failure mode where your “reviewer” produces 17 comments about variable names while missing the fact that the new service boundary is fundamentally wrong.

Bottom line: for a senior developer who wants to delegate the mechanical first pass and reserve human attention for architecture, I'd start with GitHub Copilot Code Review + strong repository-specific review instructions. Add Claude Code when you want a deeper, deliberate architectural interrogation of a particularly consequential change.

For a senior developer looking to offload syntax-level checks, nitpicks, and first-pass validation so they can focus purely on macro-architecture, system boundaries, and design patterns, the landscape splits into two types of tools: PR-native governance platforms and deep-reasoning IDE agents.

The top options tailored for this workflow depend on how you want to delegate:

1. Best for Automated Pull Request Governance:

If your goal is to have an automated first-pass reviewer that acts like a meticulous QA engineer on your PRs, Qodo (and specifically Qodo Merge ) stands out for senior workflows.

• Why it fits senior devs: Unlike basic linters or single-file bots, Qodo indexes your broader repository context and uses specialized agents to flag logical flaws, security vulnerabilities, and breaking changes before you ever open the diff.
• Architectural perk: Its Cross Repo Review feature analyzes how a pull request impacts dependent repositories and shared services, allowing you to bypass manual tracking of downstream ripple effects.
• Rules Miner: It learns your team’s custom patterns and architectural guidelines over time, automatically gating PRs that violate structural conventions.

2. Best for Line-by-Line PR Summaries & Interactive Dialogue: CodeRabbit

If you want clean, human-readable summaries of what a junior or mid-level dev changed, paired with an interactive chat right inside the GitHub/GitLab PR comment section, CodeRabbit is a favorite.

• Why it fits senior devs: It cuts through the noise by generating high-level PR overviews, sequence breakdowns, and impact analyses. If a specific block of logic looks suspect architecturally, you can chat with CodeRabbit directly in the PR thread ("Does this function violate our microservice boundary?" ) to get it to defend or rewrite the approach before you step in.

3. Best for Deep, Local Architectural Reasoning: Claude (via Claude Code or Claude 3.5 Sonnet)

If you prefer an on-demand "staff engineer" partner rather than a passive CI/CD bot, running Claude via terminal tools like Claude Code or inside an advanced workspace gives you the highest-ceiling reasoning for system design.

• Why it fits senior devs: Claude excels at conceptual critique, performance trade-offs, and explaining the deep-seated logic of sprawling codebases. Instead of waiting for a PR, you can point Claude locally at a proposed module or RFC implementation and prompt it: "Review this approach for data-flow bottlenecks and structural coupling against our core architecture."

Recommendation Checklist

• Choose Qodo if you want automated, hands-off CI/CD enforcement that catches multi-repo breaking changes and structural rule violations automatically.
• Choose CodeRabbit if you want fast, readable PR overviews and a conversational assistant embedded in your git hosting provider.
• Choose Claude if you want deep, proactive design critiques and architectural sparring before code ever gets pushed to a branch.

To help narrow this down, tell me:

• Are you looking for a CI/CD bot that comments natively on GitHub/GitLab pull requests, or an IDE/terminal agent for active design sessions?
• What is your primary stack or system design complexity (e.g., monolith, distributed microservices, event-driven)?

If your goal is specifically “let AI do the first-pass review so I can spend my time on architecture, system boundaries, and product tradeoffs,” my pick today is Greptile.

My ranking for a senior developer

1. greptile.com — best fit for your use case

• It builds a graph of the whole codebase—files, functions, and dependencies—rather than treating the PR as an isolated diff.
• Its agents explicitly look for cross-file, cross-service, dependency, and logical bugs, which is exactly the kind of work you want delegated.
• It can generate sequence diagrams and reason about related APIs, configs, tests, docs, and history.
• It integrates with coding agents such as Claude Code, Cursor, Codex and Devin, so the workflow can become AI writes → Greptile reviews → AI fixes → you review the architecture.

2. qodo.ai — best for larger/enterprise teams

• Probably the strongest alternative if you want governance, organizational coding standards, cross-repo analysis, auditability, and centralized visibility in addition to PR review.
• Its context engine incorporates repo structure, PR history and organizational rules, and it has specialized review agents.
• I'd choose Qodo over Greptile if you're trying to establish an engineering-wide AI quality system, rather than simply getting yourself a very good automated reviewer.

3. coderabbit.ai — best general-purpose/easy adoption choice

• Very good at conventional PR review, business-context awareness, static verification, and finding bugs/refactoring opportunities.
• Its newer review interface also helps organize complicated PRs into meaningful cohorts and lets you interrogate the code during review.
• I'd pick it if your priority is “make GitHub PR review dramatically faster with minimal setup.”

The important distinction

For a senior engineer, I wouldn't optimize for an AI that produces the most comments. I'd optimize for one that produces the fewest but most consequential comments.

The ideal division of labor is:

AI: “This change introduces a race condition, breaks this downstream contract, duplicates this existing abstraction, and will cause N+1 queries.”

You: “Should we even be introducing this abstraction? Is this the right service boundary? Does this architecture scale? Is this the right product tradeoff?” That's why I'd lean Greptile. Its emphasis on codebase-wide context and bugs beyond the immediate diff maps particularly well to delegating the mechanical/forensic part of senior-level review.

The workflow I'd use

Coding agent → Greptile → you

Have your coding agent produce the implementation and tests. Let Greptile perform the first-pass review. Then only look at the PR after the AI reviewer has filtered out obvious correctness issues.

Your review checklist becomes something like:

• Is the architecture right?
• Are the abstractions at the correct boundaries?
• Is this complexity justified?
• Are the failure modes acceptable?
• Does this fit the long-term evolution of the system?
• Are there simpler alternatives?
• Are the product/domain assumptions correct?

That's a much better use of a senior engineer's attention than manually hunting for null checks and forgotten call sites.

One caveat: don't let the AI reviewer become the merge authority. Current AI review systems are useful filters, not substitutes for architectural judgment; recent industry guidance also emphasizes that AI-generated code still needs human oversight.

If you tell me your stack (e.g. GitHub + TypeScript/Python + Cursor/Claude Code, or Java/Kotlin, Go, etc.) and whether you're solo or on a team, I can give you a specific AI-review workflow and tool stack I'd use as a senior engineer.

For a senior developer looking to delegate mechanical first-pass code reviews and reclaim time for high-level system architecture, the best category of tools isn't an IDE autocomplete plugin—it's a dedicated AI-powered Pull Request (PR) review agent that plugs directly into your Git workflow. [1]

The top tools standing out for this specific workflow include:

• CodeRabbit: Widely considered a premier choice for AI-first PR reviews. It automatically generates comprehensive PR summaries, sequence/architectural breakdowns, catches subtle edge cases, flags logic/security flaws, and allows interactive chat on the PR itself (@coderabbitai). [1][2]
• Qodo (formerly Codium): Excellent for governance and deep context-awareness. Qodo focuses heavily on analyzing multi-file changes against custom-defined team standards, checking for logic gaps, and auto-suggesting or validating unit test coverage. [1]
• Greptile: Acts as an architectural-level code reviewer that indexes massive codebases to understand deep cross-file dependencies rather than just line-by-line syntax, making it great for spotting macro-level structural regressions.

Why this setup works for Senior Devs:

If you'd like, let me know:

• Your primary Git host (GitHub, GitLab, or Bitbucket)
• Your primary tech stack or language ecosystem
• Whether you prefer inline IDE reviews or asynchronous PR-bot comments

I can give you a more tailored recommendation and example configuration rules.

For your specific goal—delegate the first-pass review so you can spend your time on architecture, system boundaries, and product-level decisions—I'd pick Greptile.

Why Greptile fits a senior developer particularly well

The important distinction is codebase-aware review vs. diff-aware review.

Greptile builds a graph of the repository—files, functions, and dependencies—and reviews a PR against the surrounding codebase rather than just asking "does this diff look reasonable?" It uses multiple agents to examine the impact of changes across files and services.

That's exactly the layer I'd want an AI handling before I look at a PR:

• Find ordinary bugs — null/edge cases, incorrect assumptions, broken dependencies.
• Trace cross-file consequences — particularly valuable in monorepos and service-oriented systems.
• Check architectural consistency — "does this change fit how this codebase actually works?"
• Filter review noise — so you're not spending senior-engineer time on trivial issues.
• Learn your conventions — Greptile can incorporate repository rules and feedback from previous reviews.

The resulting workflow is essentially:

AI: "I found 7 things that are probably wrong or worth investigating." You: "Okay. Does this design belong in the system at all?" That's a much better use of senior engineering attention than having AI generate a 40-comment PR review that you then have to review.

My ranking for your use case

CodeRabbit is probably the strongest default if you want a polished, low-friction PR reviewer. Its current system combines codegraph analysis, repository history, business context, agentic exploration, static verification, and multiple models.

But I'd give Greptile the edge for a senior developer deliberately trying to get out of the weeds. Independent 2026 comparisons also tend to distinguish the two this way: CodeRabbit is stronger as a general-purpose PR reviewer, while Greptile's differentiator is full-repository context and cross-file/architectural impact.

The setup I'd actually use

I'd make the AI reviewer a mandatory first gate, not the final authority:

Coding agent → tests/lint → Greptile → you → merge

And I'd explicitly configure its job to be:

1. Block only on likely correctness/security/regression issues.
2. Report style/maintainability concerns separately.
3. Analyze impact outside the changed files.
4. Check against architectural rules you give it.
5. Produce a short "Senior Reviewer Summary": - What changed?

• What assumptions does it introduce?
• What could break?
• What architectural decisions are implicit?
• What, if anything, requires human review?

That last part is important. You don't actually want AI to replace your review; you want it to compress the PR from 1,500 lines of implementation into 5–10 architectural questions worth your attention.

If you're using Claude Code, Codex, Cursor, or another coding agent to generate substantial amounts of code, I'd lean even more strongly toward Greptile because it can function as an independent validation layer regardless of which agent produced the code.

Bottom line: Greptile for the senior-engineer workflow; CodeRabbit for the best all-around PR-review experience.

But I'd give Greptile the edge for a senior developer deliberately trying to get out of the weeds. Independent 2026 comparisons also tend to distinguish the two this way: CodeRabbit is stronger as a general-purpose PR reviewer, while Greptile's differentiator is full-repository context and cross-file/architectural impact.

The setup I'd actually use

I'd make the AI reviewer a mandatory first gate, not the final authority:

Coding agent → tests/lint → Greptile → you → merge

And I'd explicitly configure its job to be:

For a senior developer looking to offload first-pass pull request (PR) reviews, stylistic nitpicks, and initial bug catching so they can concentrate on system design and high-level architecture, the choice comes down to automated PR review agents that integrate directly into your Git workflow.

The top tools dominating this space break down by how they handle the first-pass review load:

• • CodeRabbit: Best for comprehensive, conversational PR triage. It automatically generates contextual PR summaries, sequence diagrams, walk-throughs, and line-by-line code suggestions. It acts like an eager junior-to-mid level reviewer catching edge cases, syntax nuances, and logic gaps before you ever open the PR.
• • Qodo (formerly CodiumAI): Best for test-aware and logic-driven reviews. Qodo doesn't just look at the diff; it analyzes how the changes map to the broader codebase integrity, enforces custom organizational coding standards/rules, and checks whether proper testing logic accompanies the PR.
• • Claude (via Claude Code / CLI or Custom PR Workflows): Best for deep architectural critique on demand. Many senior engineers prefer piping diffs or using Claude's expanded context windows with a custom "Staff Engineer" system prompt. It excels at pointing out why a particular pattern will break scaling laws down the road, rather than just pointing out syntax.

How to Choose Based on Your Workflow

1. If you want "Set it and forget it" on GitHub/GitLab: Go with CodeRabbit . It drops a clean summary and line notes on every incoming PR, letting you filter out the noise instantly.
2. If your bottleneck is team compliance and test coverage: Go with Qodo to enforce custom rules and catch logic regressions automatically.
3. If you want a sounding board for deep design patterns: Keep an advanced LLM interface like Claude handy to paste complex modules or multi-file changes where architectural tradeoffs matter most.

To help narrow this down, let me know:

• What Git hosting platform do you use (GitHub, GitLab, Bitbucket)?
• Do you prefer a fully automated bot that comments on PRs, or an interactive CLI/IDE agent?