Qodo 透過 Codex 將上下文融入 AI 程式碼審查

Qodo builds context into AI code review with Codex

Qodo brings organizational context and codebase standards into Codex, helping developers catch P0 issues across repositories.

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Behind the Build: OpenAI and Qodo.

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Image 1: Qodo — Video poster

00:00 01:25

Company size: Startup

Region: North America

Industry: Technology

Products: API, Codex

Finding P0 bugs across repositories with Codex

Qodo⁠(opens in a new window) builds AI tools for code review and software quality, giving coding agents the organizational knowledge they need to understand the code they change. Co-founder and CEO Itamar Friedman calls it “the coding agent’s best friend.”

Qodo uses OpenAI models including GPT‑6 Astra to gather engineering knowledge, review code, and monitor software quality. Its Codex plugin brings that context and review into developers’ coding sessions.

“We love OpenAI models. We want the frontier, the best reasoning models, but also those that are really fast.”

—Itamar Friedman, Co-founder and CEO, Qodo

Finding P0 bugs across repositories with Codex

Itamar describes customer environments that can span 10,000 or more repositories, where a local code change can break a downstream service, even if it looks correct on its own. Qodo built a plugin in the Codex marketplace to address this.

“Codex is really good at taking a local task and completing it in an efficient way,” Itamar says. Qodo’s plugin gives Codex access to codebase knowledge, organizational rules, and independent review through Qodo’s Agentic Toolbox.

With context, Codex can account for dependencies across repositories, including those hosted by different Git providers or services connected through Kafka. “Qodo will make that connection and will help Codex look at the software as a whole and not miss those P0 bugs,” Itamar says.

Automating review while keeping human judgment

Before Codex starts writing code, it can use Qodo’s plugin to retrieve the relevant codebase rules and pull request history and patterns. After Codex makes the changes, Qodo reviews them and returns its findings directly to Codex in the same session, before the developer opens a pull request.

Nnenna Ndukwe, Qodo’s AI Developer Relations Lead, sees developers using this workflow to build features and tackle technical debt with GPT‑6 models in Codex. They are automating the handoff between Codex’s coding work and Qodo’s independent review. “That’s what I’ve been seeing developers do, which is really exciting because GPT‑6 is extremely capable,” Nnenna says.

Itamar describes coding and review agents as “yin and yang”: one creates code, the other challenges it. Each role needs its own setup and context and may involve multiple agents. His vision is for agents to discuss findings and “surface only what they don’t accept” for human review. Developers would become planners and reviewers, able to inspect unresolved issues alongside the agents’ discussion, context, and prior developer decisions.

“That’s how you create trust,” Itamar says. “I think it makes the whole process overall more efficient,” Nnenna adds. “We get to leverage the best parts of our expertise.”

Putting Codex to work every day

Internally at Qodo, Nnenna has been using GPT‑6 Astra’s Extra High and Ultra effort settings in her own automated development workflows as well as knowledge work.

“What does Codex not do? I love everything that Codex does. I love the idea of combining AI and automation.”

—Nnenna Ndukwe, AI Developer Relations Lead, Qodo

Nnenna schedules Codex to report on what she accomplished during the week and where she could focus or improve next. She also uses it to research conversations about agentic engineering on social platforms and suggest ideas for developer content. When it comes to her own code changes, she schedules Codex to analyze her work and suggest agent skills to create or refine.

Choosing which coding rules apply with the Decisions API

Itamar sees an opportunity to use OpenAI’s recently launched Decisions API to choose which rules and agent skills apply at each stage of development, rather than rely on language models for every decision.

He cites an example where the Decisions API could select which of 200 rules need checking before a language model reasons through the relevant ones, helping to make selection faster and cheaper while improving the quality of the review. “The Decisions API is an immense opportunity for us to bring more trust to the process,” Itamar says.

Building software factories customers can own

Qodo’s approach combines “the best aspects of artificial intelligence and the best aspects of traditional software engineering and scripting,” Nnenna says. Company knowledge guides coding agents, independent review checks their work, and human expertise resolves the questions that remain.

This creates the foundation of what Itamar describes as a software factory: a system customers could assemble and own, bringing coding and review agents together with an understanding of how software changes in production.

With OpenAI, Qodo is building a future where coding agents put more of a company’s experience to work and developers can focus their judgment where it matters most.

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