How to Automate Code Review with LLMs in Lucien 2026

How to automate code review today with LLMs

Software development companies constantly ask themselves:How can we make code review faster, more accurate, and always available? The answer, in 2026, lies in large language models (LLMs) that have transformed simple code completion into a virtual reviewer capable of analyzing entire codebases in seconds.

Why LLMs are the future of automated code review

When it comes toautomated code review, the benefits are clear:

• Speed:LLMs can analyze multiple lines of code in less than a second.
• Scalability:No review limit; they can handle repositories of any size.
• Consistency:Same evaluation standard across the team.
• 24/7 security breach detection:Proactive detection of security issues based on patterns.

These advantages have been amplified by two tools just released in 2026.

The latest LLM tools of 2026: plugins and harness

Google AI: the Elephant-Goldfish Model plugin for specification-driven development

Google AI has introduced a plugin calledElephant-Goldfish Modelthat revolutionizes the development workflow:

• Specification writing:The agent writes a detailed specification before any code is written.
• Five zero-context review stages:After code is written, the plugin automatically performs five rounds of review without any additional context, ensuring a thorough check.

The plugin is integrated directly into VS Code, GitHub, and JetBrains, makingautomated code reviewa native feature.

0sec Labs: the open-source "0" harness

0sec Labs has released0, an AI-based security harness that works via both web interface and CLI and supports MCP (Model Control Protocol). Its main features include:

• Code review, penetration testing, and reverse engineering:One tool for multiple phases of the development cycle.
• Local execution:All data remains within the user's environment.
• Integration with the latest LLMs:Supports custom and vendor models for greater flexibility.

Because0is open-source, teams of any size can adapt it to their review needs.

How to get started: a practical workflow with LLMs

You don't need to be an AI expert to get started. Here's a step-by-step workflow you can implement this week.

1. Install the Google AI Elephant-Goldfish plugin

Open your IDE and install the plugin via the marketplace. Once installed, create a new project and let the agent write a specification outline.

2. Start using the 0sec Labs 0 harness

Download0from the GitHub repository and run it locally:git clone https://github.com/0sec-labs/0.git
cd 0
python -m pip install -r requirements.txt
python harness.py --model gpt-4 --mode review

3. Write a prompt for the LLM

Use a well-structured prompt to guide the LLM in reviewing. Here's an example for a security-focused review:Prompt:
"Analyze the following code for potential vulnerabilities, security best practice violations, and performance issues. Provide a risk score for each issue and suggest corrective code."
Code:
def read_file(path):
# opens the file without error handling
return open(path).read()"The LLM returns a structured JSON with risk, description, and solution.

4. Combine LLM and human review

Use LLMs for automatic detection, but keep final approval with a human reviewer. This hybrid approach reduces false positives and ensures compliance with internal policies.

Best practices for effective automated code review

• Define review goals:Security, performance, style, or compliance? Different LLMs excel in different domains.
• Use multiple LLMs:For example, use one model for bug detection and another for style review.
• Document prompts:Keep review prompts in a repository to ensure reproducibility.
• Monitor false positives/negatives:Collect statistics to refine prompt inputs or change models.
• Leverage TPU chips for large-scale inference:Google's new TPU 8i Max and 8tx chips offer a larger context window, ideal for reviewing entire migration files.

Future outlook: wider context thanks to new TPU chips

With the introduction of TPU 8i Max (two 8i chips per package) and the reserved 8tx, teams can now run LLMs with context windows up to 128k tokens. This means a single LLM can analyze an entire microservice, identifying design issues that previously evaded automated reviewers.

Key takeaways

LLMs have transformedautomated code reviewfrom an occasional check into a continuous, workflow-integrated function. With tools like the Google AI Elephant-Goldfish Model plugin and the open-source 0 harness, you can achieve unprecedented specification-driven reviews, security, and performance, all while staying in control of your data. Start this week: install a plugin, configure a harness, and experiment with a review prompt. The faster you start iterating, the sooner you'll discover the real value of LLM-powered code review.

Immediate actions

1. Install the Google AI Elephant-Goldfish plugin in your preferred IDE.
2. Clone the 0 harness and run it with an open-source model.
3. Write a basic review prompt for a small Python script and test the feedback.
4. Record the review results and refine the prompt to reduce false positives.