Videos
With all the new open-source alternatives to Devin, I was looking for a comprehensive comparison of the top alternatives. I couldn't really find one, so I decided to compile one myself and thought I'd share my findings with the community.
Based on popularity and performance, I've identified SWE-agent and OpenDevin as the most promising open-source alternatives of the moment (feel free to add others I should check out in the comments).
Here's what I was able to gather about the pros and cons of each:
SWE-agent (8.7K ⭐ on GitHub https://github.com/princeton-nlp/SWE-agent):
➕ Pros:
High performance: Performs almost as well as Devin on SWE-bench, a key benchmark for evaluating developer skill, consisting of real github issues. It accurately corrects 12% of submitted bugs, which corresponds to the state of the art.
Speed and accuracy: It achieves an impressive average analysis and repair time of just 93 seconds.
Innovative: SWE-agent comes with new innovations, namely Agent-Computer Interface (ACI). ACI is a design paradigm that optimizes interactions between AI programmers and code repositories. By simplifying commands and feedback formats, ACI facilitates communication, allowing SWE-Agent to perform tasks ranging from syntax checks to test execution with remarkable efficiency.
❌ Cons:
Specialized functionality: Primarily focused on fixing bugs and issues in real GitHub repositories, limiting its versatility.
Limited output: The software does not actually produce cleartext fixed code, only “patch files” showing which lines of codes are added (+) or deleted (-).
Early stage: As a relatively new project, it's still rough around the edges.
Installation hassles: Users have reported a rather cumbersome setup process.
2. OpenDevin (20.8K ⭐ on GitHub: https://github.com/OpenDevin/OpenDevin):
➕ Pros:
User-friendly: Offers a familiar UX similar to Devin's.
Broader functionality: Offers a broader set of functionalities beyond bug fixing, catering to various aspects of software development.
Easy setup and integration: To get started, you need Python, Git, npm, and an OpenAI API key. OpenDevin is designed for seamless integration with popular development tools, serving as a comprehensive platform for both front-end and back-end tasks.
Customization: High level of level of customization
❌ Cons:
Limited performance data: There's no available data on its actual performance compared to industry benchmarks.
Workspace considerations: Runs bash commands within a Docker sandbox, potentially impacting workspace directories.
API limitations: Users have reported to have rather quickly reached the limit of OpenAI's free API plan.
PS: I wanted to explore Devika as well, but resources were surprisingly scarce.
By no means do I claim exhaustiveness, so I would be very interested to hear about your experiences!
With all the new open-source alternatives to Devin, I was looking for a comprehensive comparison of the top alternatives. I couldn't really find one, so I decided to compile one myself and thought I'd share my findings with the community.
Based on popularity and performance, I've identified SWE-agent and OpenDevin as the most promising open-source alternatives of the moment (feel free to add others I should check out in the comments).
Here's what I was able to gather about the pros and cons of each:
-
SWE-agent (8.7K ⭐ on GitHub https://github.com/princeton-nlp/SWE-agent):
➕ Pros:
-
High performance: Performs almost as well as Devin on SWE-bench, a key benchmark for evaluating developer skill, consisting of real github issues. It accurately corrects 12% of submitted bugs, which corresponds to the state of the art.
-
Speed and accuracy: It achieves an impressive average analysis and repair time of just 93 seconds.
-
Innovative: SWE-agent comes with new innovations, namely Agent-Computer Interface (ACI). ACI is a design paradigm that optimizes interactions between AI programmers and code repositories. By simplifying commands and feedback formats, ACI facilitates communication, allowing SWE-Agent to perform tasks ranging from syntax checks to test execution with remarkable efficiency.
❌ Cons:
-
Specialized functionality: Primarily focused on fixing bugs and issues in real GitHub repositories, limiting its versatility.
-
Limited output: The software does not actually produce cleartext fixed code, only “patch files” showing which lines of codes are added (+) or deleted (-).
-
Early stage: As a relatively new project, it's still rough around the edges.
-
Installation hassles: Users have reported a rather cumbersome setup process.
2. OpenDevin (20.8K ⭐ on GitHub: https://github.com/OpenDevin/OpenDevin):
➕ Pros:
-
User-friendly: Offers a familiar UX similar to Devin's.
-
Broader functionality: Offers a broader set of functionalities beyond bug fixing, catering to various aspects of software development.
-
Easy setup and integration: To get started, you need Python, Git, npm, and an OpenAI API key. OpenDevin is designed for seamless integration with popular development tools, serving as a comprehensive platform for both front-end and back-end tasks.
-
Customization: High level of level of customization
❌ Cons:
-
Limited performance data: There's no available data on its actual performance compared to industry benchmarks.
-
Workspace considerations: Runs bash commands within a Docker sandbox, potentially impacting workspace directories.
-
API limitations: Users have reported to have rather quickly reached the limit of OpenAI's free API plan.
PS: I wanted to explore Devika as well, but resources were surprisingly scarce.
By no means do I claim exhaustiveness, so I would be very interested to hear about your experiences!