How to Use DeepSeek Free AI Agent to Automate GitHub Coding Tasks

DeepSeek has introduced a free, open-source AI coding agent for GitHub. Discover how it works, its key features, and what developers should know.

The project, open source and still in the prototype phase, proposes a different way of organizing development work and allows us to understand which tasks can be delegated to AI and when a person is still needed to review the result.

Programming assistants can help write code, but coordinating their tasks is also time-consuming. It is necessary to explain to them what they should do, answer questions, and review their results, and much of this work is done in leaps between the chat with the AI, the editor, and the repository.

DeepSeek has published dsh-superagent, a project that explores how to integrate that work into a team’s conversations, and to do so it has a proposal that consists of receiving requests linked to a GitHub repository, turning them into tasks, and returning a solution where the assignment started, usually in the form of a pull request ready to review. The goal is to leverage automation with less disruption to users, but without the agent merging changes on their own—final approval is still in the hands of the team.

The first thing to note is that the repository presents it as a research prototype, not as an official DeepSeek product prepared for production. The code is free to download and is distributed under an MIT license. It works as a package for DeepSeek Harness, the environment that runs agent sessions. Its scope is defined around a single GitHub repository, although it also supports an optional integration with Feishu, a communication platform from which other commands can arrive.

The system starts working when the user requests it or when a rule written by the computer is met. Settings determine who can request the task and in which conversations. In addition, as DeepSeek explains in the documentation, a cited comment or document cannot extend permissions, since lists and rules are enforced by the host program.

It is also able to distinguish between explicit requests and inferred job opportunities. By default it uses an observation mode for the latter, and although it evaluates what it would have accepted, it does not start those tasks. Managers can also review the record before activating an automatic mode.

How DeepSeek new free agent works

When you accept an assignment, you open a task with your own session and directory. That separation organizes the work of each application. The agent can execute commands and prepare changes, while a dashboard shows whether it’s working, waiting for a response, or needs a review. In any case, experts remind us that having an independent board of directors does not in itself equate to security isolation.

The delivery can take the form of a pull request, i.e., a proposal for changes that the team reviews before it is incorporated into the project. The result returns to the original conversation, making the request, development, and review maintain a visible relationship, rather than being spread across disconnected exchanges.

The integration of these changes has its own conditions, and that is that, by default, the agent does not activate the automatic merge using their account, so if no other account is configured for that operation, it must be done by the user. Delivery instructions may have additional requirements, while GitHub branch protections are used to enforce approvals.

To install the agent, you need to prepare a technical environment, using tools such as Node.js, Git, Python, tmux, and GitHub CLI, as well as DeepSeek Harness with a configured model provider, and it is recommended that you dedicate a GitHub account to the agent. The installation wizard collects the necessary decisions and presents a plan before executing the configuration.

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