Artificial intelligence has made its way into everything—from writing code to reviewing it. But tools that understand software development like a human developer? That’s where SWE-Agent steps in. Created by researchers at Princeton University, SWE-Agent is a new take on what an AI-powered developer can be. It doesn’t just autocomplete code or check for bugs—it reads tasks, searches documentation, runs tests, and submits changes like a real engineer.

SWE-Agent isn't the first AI system to assist with software development, but it stands out in how it works. It handles the full process of fixing GitHub issues end-to-end: understanding the issue, making a plan, writing the code, testing it, and submitting a pull request—all without needing human guidance. And it does all of this using only open-source tools and models.

How It Works from Start to Finish

SWE-Agent behaves more like a teammate than a tool. It isn't just spitting out code from a prompt; it's acting inside a structured process as a software engineer would.

Reading the Issue

The first thing SWE-Agent does is read the GitHub issue and any related files. It's not just scanning keywords—it breaks down the intent, context, and what's needed. It gathers enough background to create a plan, much like how a human developer first understands the problem before touching any code.

Building a Plan

After it understands the issue, SWE-Agent writes a plan for how it will fix the problem. This plan includes what files it expects to modify, what changes it will make, and why. This is key—it doesn’t just rush to code. It reflects on the approach before moving forward.

Writing and Testing the Code

Once the plan is in place, SWE-Agent writes the code. Then, it runs the project's test suite to confirm that the new code works and nothing else breaks. If something fails, it doesn’t stop there—it goes back, figures out why, and fixes it.

Submitting the Fix

After all the tests pass, SWE-Agent creates a pull request with a clean summary of what it did. This includes the context of the issue, its plan, and the actual changes. Reviewers don't have to piece everything together—it's all right there.

What Makes SWE-Agent Different

Plenty of AI coding tools are out there, from Copilot to ChatGPT plugins. But SWE-Agent operates on a different level. It takes full ownership of the task and doesn’t rely on hand-holding. Here’s what sets it apart.

Uses Open-Source LLMs Only

One of the most interesting parts of SWE-Agent is that it's built entirely with open-source models. No black boxes and no locked APIs. It mainly uses DeepSeek-Coder-6.7B, a large language model designed for writing and understanding code. That means anyone can see how it works, change it, or improve it without depending on a single company's product.

Runs Locally

You don’t need a cloud subscription or remote access to run SWE-Agent. It runs on your own machine, using your local files, tests, and tools. This makes it more private and flexible than online AI coding assistants.

Can Handle Real-World Projects

SWE-Agent has been tested on actual repositories that have open GitHub issues. And it doesn't just write a few lines—it makes real fixes across different languages and frameworks. It's shown it can manage full-stack bugs, documentation updates, and even infrastructure changes.

Setting Up SWE-Agent Step-by-Step

If you're interested in trying SWE-Agent on your own system, the setup is straightforward. First, make sure you're using a Linux or Unix-like environment. You'll need Python 3.10 or higher, Git, and at least one GPU if you plan to run the larger language models locally. Then, clone the SWE-Agent repository from GitHub. This will give you access to the agent’s logic, planning scripts, task runners, and model integration code.

Next, set up a virtual environment and install the required Python packages using pip. If you're running models locally, you’ll also need to download the appropriate LLM checkpoints. Once the environment is ready, point the agent to your local copy of the project and specify the GitHub issue you want it to work on. There’s no need to upload anything—the agent works directly with your local files.

When everything’s in place, run the task through the command-line interface, and SWE-Agent will take over from there, analyzing the issue, writing code, running tests, and completing the task.

Does It Get the Job Done?

In testing, SWE-Agent was able to solve 12.3% of real-world GitHub issues end-to-end with no manual edits. That might sound like a modest number, but for full automation on real codebases, it's a serious step forward. In many other cases, it got partway there—writing correct code that just needed some help with tests or formatting.

This level of performance is promising because it’s happening without expensive APIs, closed models, or enterprise-level support. The fact that it works at all, under open conditions, is a huge milestone for anyone interested in AI automation in coding.

Final Thoughts

SWE-Agent is a working example of what an AI developer can look like—not just in theory but in actual codebases. It reads, plans, fixes, tests, and delivers. It does so using open tools on your own machine without relying on external services. While there's still a long road ahead before tools like this are part of every development team, SWE-Agent is a solid first step.

If you're curious about AI in real-world engineering, this is worth a closer look. It's not just a demo. It's code that runs, solves real problems, and leaves the repo a little better than it found it. It’s a glimpse into a future where AI doesn’t just assist developers—it works alongside them.