DeepSeek V5 Model Identifiers Found in Official Harness Code Repository

According to a report by STAR Market Daily on November 3rd, DeepSeek intends to use Huawei Ascend chips to train its next-generation model, V5. This plan, reported by several industry sources, suggests an expansion of the role of Ascend chips.

Previously, Ascend chips were primarily used for performing inference operations of DeepSeek models. However, their application in training V5 will be another step in integrating Chinese AI hardware into DeepSeek's infrastructure. This follows previous efforts by the company to adapt its software for domestic processors.

The DeepSeek Harness team has made dsh-superagent publicly available—a research prototype that accepts development requests via GitHub and, optionally, via Feishu. The agent performs work in separate sessions and returns results, such as pull requests, back to the original conversation.

This prototype is configured around a single GitHub repository and functions as a package for DeepSeek Harness. It transforms authorized requests into working tasks, allocates a dedicated working directory and agent session for each task, and provides a web dashboard to display issues requiring human attention.

The project explores the capabilities of coding agents to complete long-running tasks with reduced need for human coordination. The implementation, which originated from the team's daily workflow, has been published and is available under the MIT license.

DeepSeek has released a pre-release version of DeepSeek Harness, its open-source agent software, version v0.2.2-alpha.2, which was posted on GitHub on October 28. The main innovation is an experimental plugin called subagent_oar, which enables the main agent to assign an autonomous task to install Codex CLI or Claude Code on the same machine.

Previously, Harness supported Claude Code and Codex; the v0.1.0-rc.8 update in August allowed users to install them as profile packages within their subagent system. However, the new tool functions differently. It provides the main agent with a single entry point for distributing work: when calling subagent_oar, the agent selects either codex or claude, then sends the task description, the full prompt, and an optional working directory. The tool immediately returns a task ID, and upon completion, a notification of execution brings back the final answer or an error message to the main session.

External tools operate independently. Codex CLI and Claude Code maintain their own configurations, models, tools, and login process, so users must install and authorize each one, and ensure that the Harness host can find them. Provider parameters can pass the model, reasoning level, launch arguments, environment variables, and timeout, depending on each runtime's support.

The documentation sets clear limitations: the main conversation is not redirected, so the request must explicitly state which files are important and what should be returned. Tasks are one-off, without the possibility of subsequent questions, session resumption, or real-time progress streaming. Only the final answer or failure is returned, not the internal reasoning of the external agent or its tool history. Official examples allow for a writable workspace and disable permission requests, and canceling a task does not undo file changes already made by the external agent.

Other add-ons help users track delegated work. An experimental change request feature can display the status and differences of a related merge request for a session running in its own Git working tree. The developer statistics plugin details the usage of context, cache, as well as the time spent on models and tools for each step and turn. Output from small background tasks now comes through the completion notification, and the subagent waiting guide reduces the need for repeated polling.

The release also allows users to update third-party plugin packages, with the option to roll back in case of a hot update failure, and adds interface text in Japanese, Spanish, French, German, Russian, and Brazilian Portuguese. Earlier community materials, including multi-agent setup tests from Bilibili creators, were created by users themselves and are not DeepSeek products.