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Devin AI - Autonomous AI Software Engineer

Devin AI is an autonomous software engineering agent that independently completes coding tasks, debugs systems, and deploys applications while collaborating with human engineers.

  • Autonomous Coding
  • Real-Time Collaboration
  • End-to-End Development
  • Continuous Learning
  • Tool Integration
💰Core(Pay as you go)/Team($500/month)/Enterprise(Custom pricing)
Devin AI - Autonomous AI Software Engineer
Devin AI - Autonomous AI Software Engineer

Product Overview

Devin AI is an autonomous software engineering agent that independently completes coding tasks, debugs systems, and deploys applications while collaborating with human engineers.


Key Features & Benefits

Autonomous Coding

Devin independently handles complex engineering tasks from planning to deployment, requiring minimal human oversight.

Real-Time Collaboration

Reports progress and accepts feedback during development, enabling seamless human-AI teamwork on coding projects.

End-to-End Development

Builds, tests, and deploys fully functional applications including websites and AI models from natural language prompts.

Continuous Learning

Improves performance over time by learning from mistakes and optimizing solutions through reinforcement learning.

Tool Integration

Works within sandboxed environments with developer tools including shell, code editor, and browser.


Use Cases

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Interactive Game of Life Web App

Devin autonomously builds and deploys a fully interactive Game of Life simulation website end-to-end, adding features incrementally per user feedback.

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Automated LLM Fine-Tuning from GitHub

Given only a link to a research repository on GitHub, Devin sets up fine-tuning workflows and executes them without human intervention.

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Sympy Bug Resolution

Devin reproduces a logarithm-calculation bug in Sympy, sets up the environment, writes and tests a fix, and verifies correctness independently.

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Real-World Upwork Computer Vision Task

Devin completes a paid Upwork project: writing, debugging, and reporting on a computer vision model to specification and deployment.

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Pros & Cons

  • Achieves state-of-the-art performance on the SWE-Bench benchmark, resolving 13.86% of real GitHub issues end-to-end.

    [source]
  • New 2.0 version doubles task throughput compared to initial release, enabling more work per session.

    [source]
  • Pay-as-you-go pricing can be costly, with $2.25 per ACU leading to limited active work hours for large projects.

    [source]
  • Struggles with more complex or large-scale engineering tasks, requiring human intervention for intricate logic.

    [source]

FAQ