Multi-Agent PR Reviews with Claude Code: Stop Relying on a Single AI Pass

Code review is the one part of the modern dev workflow that AI tools have only half-solved. You can ask Claude to review a pull request and get useful feedback, but a single-pass review has a fundamental ceiling: one context window, one frame of attention, one pass over the diff. The moment your PR touches security, business logic, and code style simultaneously, something gets missed.

Multi-agent PR review fixes this by doing what good human review teams already do. It assigns different agents to different concerns, runs them in parallel, and synthesizes the results. This is not a futuristic concept. With Claude’s subagent capabilities and a small orchestration layer, you can ship this pattern today. The open-source project adamsreview is a working reference implementation that shows exactly how.

Why Single-Agent Code Review Hits a Wall

Ask any developer who has used AI for PR review at scale and the complaints are consistent. The model gives you surface-level style feedback when you wanted deep logic analysis. It misses a subtle auth bypass because it was focused on the wrong file. It reviews the test file thoroughly but skips the service layer where the actual risk lives.

This is not a failure of Claude specifically. It is a structural problem with single-agent review:

• Context dilution. A large PR has hundreds of lines of diff. The model splits its attention across all of it equally. Critical lines get the same weight as boilerplate.
• Mixed concerns. Security review, performance review, and style review require different mental models. Asking one agent to hold all three simultaneously degrades quality across all three.
• No specialization. A generalist pass cannot match the depth of a reviewer who only looks at one thing. Human teams assign reviewers by expertise. AI review should too.

The fix is obvious in retrospect: decompose the review into specialized sub-tasks and run them in parallel.

How adamsreview Implements Multi-Agent Review

adamsreview is a straightforward open-source tool built around the Claude API. It takes a GitHub PR URL, fetches the diff, and routes it through a small fleet of specialized Claude agents running in parallel. Each agent gets a focused system prompt tuned to its domain.

The default agent configuration ships with three reviewers:

Each agent sees the full diff but is instructed to report only within its lane. The orchestrator collects all three responses, deduplicates overlapping comments, and produces a single structured review with findings grouped by category.

The result is a review that is simultaneously more thorough (each concern gets dedicated attention) and more readable (findings are organized by type rather than jumbled together).

Setting Up adamsreview: Step by Step

You need Python 3.11+, a Claude API key, and a GitHub token with pull_requests: read scope. The setup takes about 20 minutes.

Step 1: Clone and Install

git clone https://github.com/adam-s/adamsreview
cd adamsreview
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

Step 2: Configure Credentials

Create a .env file in the project root:

ANTHROPIC_API_KEY=sk-ant-...
GITHUB_TOKEN=ghp_...

adamsreview loads these automatically on startup. Do not commit this file.

Step 3: Run a Review

python -m adamsreview review https://github.com/your-org/your-repo/pull/42

The CLI fetches the diff, spawns three parallel Claude API calls (one per agent), streams responses as they complete, and prints the consolidated review to stdout. You can pipe the output to a file or post it back to the PR via the GitHub API.

Step 4: Post Feedback Back to GitHub

adamsreview ships with a --post flag that uses the GitHub API to create review comments on the PR directly:

python -m adamsreview review https://github.com/your-org/your-repo/pull/42 --post

This posts inline comments on specific diff lines where possible, and a top-level summary comment with the full structured report. The effect from the PR author’s side is indistinguishable from a human review with organized sections.

The Architecture Behind It: Claude Subagents and Parallel Orchestration

Understanding why this works (not just how to run it) helps you extend the pattern to other problems.

Claude’s API supports concurrent requests. adamsreview exploits this by using Python’s asyncio to fire all three agent calls simultaneously. The wall-clock time for a three-agent review is essentially the same as a single-agent review, because all three run in parallel. You get 3x the review coverage at 1x the latency cost.

The orchestrator is thin by design. It handles four concerns:

1. Diff extraction. Fetch the PR diff via the GitHub API and chunk it if needed (large PRs may exceed context).
2. Agent dispatch. Build per-agent messages (system prompt + diff) and issue concurrent API calls.
3. Response aggregation. Collect completions as they stream in, label each by agent type.
4. Deduplication. Run a lightweight pass to collapse identical findings raised by multiple agents.

This is the same pattern that powers more sophisticated agentic systems. If you have read our guide on how to build a multi-agent system with LangGraph, the orchestrator role here maps directly to the supervisor node concept. adamsreview just implements it with vanilla asyncio rather than a graph framework, which keeps the dependency footprint minimal.

Customizing the Agent Fleet

The default three-agent configuration is a starting point, not a prescription. Real teams will want to adapt it.

Adding a Domain-Specific Agent

If your codebase has a domain with its own failure modes (financial calculations, medical data handling, cryptographic operations), add a dedicated agent:

# agents/finance_agent.py
SYSTEM_PROMPT = """
You are a financial software code reviewer.
Focus exclusively on:
- Floating-point arithmetic in monetary calculations
- Off-by-one errors in interest or fee computation
- Rounding mode consistency (HALF_UP vs HALF_EVEN)
- Missing currency denomination handling
Report findings with the affected line number and a concrete fix suggestion.
"""

Register it in agents/__init__.py and it runs automatically alongside the defaults. Each new agent adds parallel capacity, not serial latency.

Tuning Agent Prompts for Your Stack

The default prompts are language-agnostic. For a TypeScript-heavy codebase, you might add TypeScript-specific guidance to the logic agent:

Pay special attention to:
- Missing null checks on optional chained properties
- Unhandled Promise rejections
- Implicit any types that bypass the type system

Prompt specificity is where most of the quality improvement lives. The model already knows what good code looks like. The system prompt’s job is to focus its attention on the failure modes your team actually cares about.

Handling Large PRs: Context Chunking

A PR touching 40 files is not unusual. Sending the entire diff in one context window is both expensive and quality-degrading (the model’s attention spreads thin). adamsreview handles this with a chunking strategy.

Large diffs are split by file. The security agent reviews security-sensitive files (auth handlers, middleware, config) first. The logic agent reviews core business logic files. Each agent gets a targeted subset of the diff rather than the full noisy whole.

This is a heuristic, not a perfect solution. Some bugs only appear at the intersection of two files. For those, the orchestrator runs a fourth “integration” agent over just the interface boundaries between changed modules. It is more expensive but catches the class of bugs that per-file review misses.

If you are evaluating LLM outputs in production workflows, this layered approach to chunking is a useful model. We covered the broader problem of production LLM evaluation in how to evaluate LLM outputs in production, including how to measure when your retrieval or chunking strategy is hurting rather than helping.

Cost Model: What Does This Actually Run?

Multi-agent review sounds expensive. In practice, the cost per PR is modest because diffs are small relative to the context window.

A typical PR review (200-line diff, three agents) uses roughly:

At 50 PRs per month, you are looking at $1.50 with Sonnet or $0.25 with Haiku. For most teams, this is noise in the AWS bill. For teams with strict budget controls, Haiku is accurate enough for style and security review, and Sonnet reserved for the logic agent where reasoning depth matters most.

You can mix models per agent in adamsreview:

AGENTS = [
    SecurityAgent(model="claude-sonnet-4-5"),
    LogicAgent(model="claude-sonnet-4-5"),
    StyleAgent(model="claude-haiku-4-5"),
]

This hybrid approach cuts cost by roughly 40% while preserving quality where it matters most.

Integrating with CI/CD

The most useful deployment is as a GitHub Actions step that posts automated review comments before human reviewers even open the PR:

# .github/workflows/ai-review.yml
name: AI PR Review

on:
  pull_request:
    types: [opened, synchronize]

jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - name: Install adamsreview
        run: pip install adamsreview
      - name: Run review
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
        run: |
          adamsreview review ${{ github.event.pull_request.html_url }} --post

With this in place, every PR gets a structured multi-agent review within 60-90 seconds of being opened. Human reviewers see the AI findings first and can focus their attention on the items the AI flagged or on architectural concerns the AI cannot evaluate.

This integrates naturally with the broader Claude Code workflow where Claude handles the mechanical review pass and humans handle judgment calls.

Limitations and When Not to Use It

Multi-agent review is a tool, not a replacement for human judgment. Be clear about what it cannot do:

• Architectural review. AI review agents see the diff, not the system. They cannot evaluate whether this PR’s approach is the right one for the codebase over the next two years.
• Team context. The model does not know your team’s conventions beyond what you encode in the system prompt. Undocumented tribal knowledge stays invisible.
• Intent verification. The agent can tell you what the code does. It cannot tell you if it does what the PR description claims.
• Performance profiling. Static review of a diff cannot reliably predict runtime behavior under production load.

The right mental model: multi-agent AI review is a high-coverage first pass. It catches the bugs that should be caught mechanically so human reviewers can spend their finite attention on the things that require genuine expertise and context.

What This Pattern Points To

adamsreview is a narrow application of a much broader principle: multi-agent decomposition. The insight that “one agent, one concern” produces better outcomes than “one agent, all concerns” applies far beyond code review.

The same pattern is being applied to document analysis (one agent for citations, one for logical consistency, one for tone), to customer support triage (one agent per product area), and to data pipeline validation (one agent per schema domain).

If you are building your own Claude-powered tools, the .claude/ folder anatomy guide and the Claude API vs OpenAI API comparison are useful starting points for understanding the infrastructure decisions underneath these systems.

Start Shipping Better Reviews Today

Clone the repo, set your API keys, and run it against an open PR in your codebase. The first run will show you concretely what single-pass review was missing. From there, tune the system prompts to your stack and plug the --post flag into GitHub Actions.

Multi-agent review is not a moonshot. It is a 20-minute setup that makes every future PR review systematically better. That is the kind of infrastructure investment that compounds.

Try adamsreview: github.com/adam-s/adamsreview

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