DeepSeek Harness v0.2.2-alpha.2 allows the main agent to delegate tasks to local Codex and Claude Code tools
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Pandaily
pandaily.com

DeepSeek Harness v0.2.2-alpha.2 allows the main agent to delegate tasks to local Codex and Claude Code tools

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.

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DeepSeek releases DeepInfer code to accelerate and optimize AI model execution
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olhardigital.com.br

DeepSeek releases DeepInfer code to accelerate and optimize AI model execution

Last Tuesday (20), DeepSeek made the code for DeepInfer public, a software designed to operate its artificial intelligence (AI) models. This tool was created with the goal of improving processing efficiency and reducing the time required to generate responses.

With the release of the code, developers and corporations have the opportunity to analyze the program's operation and adapt it to various projects, provided they meet the technical requirements and established terms of use. According to DeepSeek itself, this action aims to decrease energy and computational resource consumption, making AI model operation more efficient. However, practical results will depend on usage conditions and available infrastructure.

Currently, DeepInfer is specifically compatible with the DeepSeek-V4.1-Flash model. Although opening the code allows other developers to investigate and use the technology, it is important to note that not the entire company execution system has been made public, as some infrastructure components remain closed.

During his participation in the Olhar Digital News program, physicist Roberto 'Pena' Spinelli, a Machine Learning specialist, commented on this news. The researcher clarified the distinction between providing an AI model and releasing the software responsible for executing it. To explain this difference, he used the analogy of a car.

Pena detailed that simply sharing the model is not equivalent to providing all the necessary tools to operate it effectively. While the model carries the parameters that guide response generation, the inference software groups the mechanisms that enable its functioning. The opening of this code can assist developers who intend to implement their own solutions.

The specialist compared the situation by saying: 'Before, they only shared the engine blueprint, which is the artificial intelligence itself. What they are doing now is delivering the functioning of the entire car. All the software and the mechanism of how they execute the artificial intelligence have been released to the public.'

This comparison helps to understand the difference between the two elements, but there is a caveat: the availability of DeepInfer does not imply that all elements of DeepSeek's infrastructure are accessible. Nevertheless, access to the code allows programmers and researchers to study the execution methodologies employed by the company and evaluate how to apply them in other works.

The initiative may also benefit DeepSeek's commercial strategy. By sharing part of its technology, the company opens avenues for other developers to use it, contribute improvements, and create compatible applications. This adoption can expand the reach of its technology in a highly competitive market, where several companies compete to offer more accessible and efficient models.

Pena added: 'DeepSeek wants to create an ecosystem so that more people and companies start using its products.' He continued: 'By showing how to assemble the complete structure, it encourages the public to abandon more expensive paid platforms. It is a consistent way to assert itself in an extremely contested sector.'

The release of DeepInfer underscores that competition in the field of AI is not limited only to the ability of models to generate responses, but also to the efficiency required to execute them. By sharing part of its technology, DeepSeek increases the options for software use and strengthens its position in the sector. The impact of this measure will depend, among other factors, on the performance achieved in various environments and the adoption by developers and companies.

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