How to Automate Code Review with LLMs: A 2026 Guide

How can code review be automated using Large Language Models (LLMs) today

Automated code review with LLMs is already a reality in 2026. Thanks to models like Anthropic's Claude Code, Mistral AI, and Palantir Foundry's integrated platforms, development teams can get immediate feedback, detect bugs, and enforce best practices without slowing down the workflow.

Why choose LLMs for code review

Large Language Models offer three key advantages for automated code review:

• Contextual understanding of source code and the application domain.
• Generation of detailed comments and correction suggestions in natural language.
• Native integration with existing CI/CD tools.

These advantages translate into a faster, more consistent, and less human-dependent review process.

The best LLM tools for code review in 2026

Claude Code with Plugin Evals

Anthropic's Claude Code introducedPlugin Evals, a system that evaluates plugins based on six types of assessments and integrates them directly into CI pipelines. Theclaude plugin evalcommand runs a series of realistic tests, assigning a score and providing suggestions to improve code quality.

Mistral AI for large-scale production

Samsung has chosen Mistral AI models for its semiconductor production processes. Mistral's solution offers code-optimized models for local devices, ideal for code review in environments with latency constraints and privacy requirements.

Palantir Foundry and cuOpt for supply chain

NVIDIA uses Palantir Foundry together with cuOpt to automate hardware distribution decisions for its supply chain. Foundry's integrated code review system analyzes code for inefficiencies and suggests optimizations based on production data.

Integrating LLM-based code review into CI/CD

A typical workflow starts with a commit to Git. A GitHub action or Jenkins job triggers an LLM check that performs:

1. Static analysis of source code.
2. Comment generation via LLM.
3. Automatic approval or error reporting.

Example GitHub action (YAML):

steps:
  - name: Run code review with Claude
    uses: anthropic/claude-code@v2
    with:
      github_token: ${{ secrets.GITHUB_TOKEN }}
      plugin_eval: true
      severity: high

Practical example: an end-to-end workflow

Below is a complete example that combines Claude Code, a custom linter, and automated change control:

# .github/workflows/code-review.yml
name: Code Review LLM
on: [push, pull_request]
jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Install dependencies
        run: npm ci
      - name: Run linter
        run: npm run lint
      - name: Run code review with Claude
        uses: anthropic/claude-code@v2
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
        with:
          files: src/
          output: review.json

The resultingreview.jsonfile contains structured comments that can be displayed directly in the pull request interface.

Best practices and considerations

• Security:Use protected API keys and limit access to sensitive repositories.
• Privacy:Run LLM models on-premise when handling proprietary data.
• Code quality:Combine LLM feedback with traditional static checks for comprehensive coverage.
• Iteration:Allow human reviewers to flag LLM suggestions as false positives to improve the model over time.

Future prospects

In 2026, integration between LLMs and development tools is becoming increasingly seamless. Upcoming innovations will include multimodal models capable of analyzing UML diagrams, flowcharts, and even video tutorials, making code review fully contextual and proactive.

Staying up to date with these rapidly evolving platforms not only improves code quality but also positions teams at the forefront of the next wave of AI-driven software development.

Conclusion: Put AI to work for code review

Automated code review with LLMs is no longer a futuristic idea, but a proven solution that accelerates development cycles, reduces bugs, and democratizes the review experience. By leveraging Claude Code, Mistral AI, Palantir Foundry, and the latest CI plugins, teams can achieve smarter, more scalable quality control that adapts to the pace of modern development.

Get started today: Configure an LLM action in your repository, experiment with an evaluation plugin, and watch as your code becomes safer and more maintainable automatically.