Best AI Agent Frameworks for Automated Code Reviews
There is no single framework that fits every engineering team. Some options are dedicated code-review systems, while others are general agent frameworks that can be adapted into custom review workflows.
GitHub Copilot for AI Code Reviews
For teams already using GitHub, Copilot is one of the most straightforward ways to introduce AI into pull-request reviews.
GitHub currently supports Copilot code review across GitHub.com and several development environments, and automatic reviews can be configured for repositories and pull requests.
What makes this interesting for CI/CD is that the review can become part of the existing pull-request workflow rather than creating an entirely separate review system.
Teams can also customize the review effort and provide instructions that tell Copilot what matters in a particular repository. GitHub specifically recommends custom instructions for making automated reviews more relevant to project-specific requirements.
This makes Copilot particularly practical when your repositories, pull requests and CI/CD infrastructure already live on GitHub.
Qodo for Agentic Code Review Automation
Qodo takes a more specialized approach to AI-assisted code review.
Its documentation describes configurable review agents, automated PR analysis and CI capabilities. Qodo can also run its CLI in CI mode, allowing agents to operate without interactive input inside automated pipelines.
One particularly useful feature is customization.
Teams can define repository-specific instructions, compliance requirements and review behavior. Qodo supports configuration through files such as .pr_agent.toml, allowing review rules to be stored alongside the project.
That matters because every software project has different standards.
A financial application may care heavily about security and audit requirements. A frontend project may emphasize accessibility and performance. An internal API may have strict conventions around authentication and error handling.
An AI review agent becomes much more useful when it understands those differences.
Qodo also supports automated feedback around failed CI checks, allowing the system to analyze failed stages, tests and error logs and provide feedback through the pull request workflow.
LangGraph and Custom AI Review Agents for CI/CD
Sometimes an off-the-shelf code-review tool is not enough.
A larger engineering organization may want to build its own review workflow where different agents perform different jobs.
This is where agent orchestration frameworks such as LangGraph become relevant.
Instead of creating one giant “review this pull request” prompt, you can design a workflow with specialized stages:
- A planner agent determines what changed.
- A security agent looks for security problems.
- A testing agent examines test coverage.
- A quality agent checks maintainability.
- A reviewer agent combines the findings.
- A final step decides whether the result should be reported to the developer.
This approach is more complicated, but it provides significantly more control.
It also follows a broader pattern emerging in agentic software engineering: using multiple specialized agents rather than expecting one model to perform every task perfectly.
LangChain’s Open SWE project, for example, describes a multi-agent architecture containing dedicated planning and reviewing stages before a pull request is opened.
For organizations building internal developer platforms, this type of architecture can be more interesting than simply installing another PR bot.