Audience

Software engineers, researchers, and developers seeking a solution to assist with real-world coding tasks such as bug-fixing, pull-request automation, and multi-file code edits

About DeepSWE

DeepSWE is a fully open source, state-of-the-art coding agent built on top of the Qwen3-32B foundation model and trained exclusively via reinforcement learning (RL), without supervised finetuning or distillation from proprietary models. It is developed using rLLM, Agentica’s open source RL framework for language agents. DeepSWE operates as an agent; it interacts with a simulated development environment (via the R2E-Gym environment) using a suite of tools (file editor, search, shell-execution, submit/finish), enabling it to navigate codebases, edit multiple files, compile/run tests, and iteratively produce patches or complete engineering tasks. DeepSWE exhibits emergent behaviors beyond simple code generation; when presented with bugs or feature requests, the agent reasons about edge cases, seeks existing tests in the repository, proposes patches, writes extra tests for regressions, and dynamically adjusts its “thinking” effort.

Pricing

Starting Price:
Free
Free Version:
Free Version available.

Integrations

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Company Information

Agentica Project
Founded: 2025
United States
agentica-project.com

Videos and Screen Captures

DeepSWE Screenshot 1
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Product Details

Platforms Supported
Windows
Mac
Linux
On-Premises
Training
Documentation
Support
Online

DeepSWE Frequently Asked Questions

Q: What kinds of users and organization types does DeepSWE work with?
Q: What languages does DeepSWE support in their product?
Q: What type of training does DeepSWE provide?
Q: How much does DeepSWE cost?

DeepSWE Product Features