Devin

Devin is a state-of-the-art autonomous AI agent developed by Cognition specifically for software engineering. Unlike traditional autocomplete tools, Devin is capable of long-term reasoning and planning, allowing it to handle thousands of decisions to complete entire engineering projects. It operates within a secure, sandboxed environment that includes a shell, code editor, and a fully functional web browser. On the SWE-bench benchmark, which evaluates AI agents on their ability to resolve real-world GitHub issues, Devin correctly resolved 13.86% of issues unassisted, far exceeding the previous state-of-the-art performance of 1.96%. It is built to be a tireless, highly skilled teammate that can learn new technologies on the fly and debug code autonomously.

Devin can take an end-to-end software project from a simple natural language prompt, creating a plan, writing the necessary code, and deploying the final application without constant human intervention.

Integrated Developer Tooling

Devin comes equipped with its own developer sandbox, including a shell for running commands, a code editor for complex logic, and a browser to research documentation or test web interfaces.

Real-Time Human Collaboration

Engineers can observe Devin’s progress in real-time, provide feedback through a chat interface, and jump in to assist when necessary, making it a collaborative partner rather than just a generator.

Continuous Learning and Debugging

Devin can learn how to use unfamiliar technologies simply by reading their documentation. It excels at identifying the root cause of errors and autonomously writing and testing fixes in large codebases.

Advanced Reasoning and Planning

Powered by proprietary reasoning models, Devin can break down complex engineering problems into manageable steps, maintaining context and state over long-running tasks that span multiple files and dependencies.

A software team can task Devin with building a new feature, such as a user dashboard or an API integration. Devin handles everything from database schema design to frontend implementation.

Automated Bug Resolution and Maintenance

Maintainers of large open-source projects can deploy Devin to scan their GitHub issues, reproduce reported bugs, and submit pull requests with the necessary fixes.

AI Model Training and Infrastructure

Machine learning engineers can use Devin to automate the setup of training pipelines, fine-tune models based on research papers, and manage the deployment of inference servers.

Legacy Code Migration

Organizations looking to modernize their stack can utilize Devin to refactor legacy code, such as converting a Python 2 project to Python 3 or migrating a monolithic application to a microservices architecture.