Xinference + Roo-Cline: AI Coding Enhancement Solution

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“ The internal R&D network is the exclusive territory for confidential product development, strictly isolated from the external network and office intranet, creating a solid security barrier to protect core product secrets and prevent data leaks. However, with the wave of the AI era surging in, how to smoothly implement AI Coding within the R&D intranet and improve developer efficiency has become a pressing challenge for R&D managers. This article will introduce a practical solution, which technical managers including CTOs might find useful. At the same time, I welcome diverse opinions, feel free to message me for discussion and exchange.”

Currently, in the AI Coding field, Cline is one of the most useful AI-assisted programming tools, consistently ranking high in the Open Router model usage.

A few days ago, I published two articles about Cline-assisted programming, detailing some specific configuration methods. If you haven’t read them, you can check them out below 👇.

Cline Themes

◆ Deepseek V3 + Cline for AI programming, this plugin is really great

◆ Roo Cline: A forked enhanced version of Cline

As mentioned above 👆🏻, in my AI full-stack work, I chose a branch version of Cline — Roo Cline. Therefore, in this article, Cline specifically refers to this Roo-Cline plugin. I assume you have read the previous two articles or have a basic understanding of Cline’s configuration. Next, I will briefly introduce the configuration method for Xinference.

Introduction to Xinference

Xorbits Inference (Xinference) is an open-source platform designed to simplify the execution and integration of various AI models. With Xinference, you can run inference using any open-source LLM, embedding models, and multimodal models in cloud or local environments, creating powerful AI applications.

Installation

Xinference can be installed on Linux, Windows, and MacOS via pip. If you need to use Xinference for model inference, you can specify different engines based on the models.

If you want to infer all supported models, you can install all necessary dependencies with the following command:

pip install "xinference[all]"

You can also select specific engines as backend

pip install "xinference[transformers,vllm,sglang]"

You can also use Docker images

docker pull registry.cn-hangzhou.aliyuncs.com/xprobe_xinference/xinference:<tag>

Starting Local Service

xinference-local --host 0.0.0.0 --port 9997

Then you can access the Xinference web console from http://127.0.0.1:9997.

Xinference has pre-configured support for mainstream models, such as the well-known llama series, qwen series, phi series, deepseek series, etc., and includes various embeddings, rerank, image, audio, video models. You can also register custom models, making it flexible and easy to use.

Note: For more information on the Xinference project, see the end of the article.

Integration of Roo-Cline and Xinference

Having understood the basic usage of Xinference, let’s see how Roo-Cline integrates with Xinference.

After installing the Roo-Cline plugin in VSCode, open the Cline page in the sidebar, click the gear icon in the upper right corner to configure the model. Select OpenAI Compatible for API Provider, and enter the Xinference address you just started, http://127.0.0.1:9997/v1, for Base URL. If you haven’t set a special API Key, you can fill in any value; otherwise, enter your configured API Key as the call credential. After filling in all configurations, click the Done button in the upper right corner.

After the configuration is complete, you can start Chat Coding with Cline.

Advanced Generation Techniques

Now that the local environment is set up, let’s assist the CTO in completing the first step of the work (for demonstration only). In fact, as an AI productivity tool, the Cline project can not only help R&D positions improve organizational efficiency but also aid management roles like CTOs in generating solutions.

For AI Coding in the R&D intranet, every CTO knows that completing the integration configuration of Roo-Cline + Xinference is just the first step. As a core role in R&D, CTOs need to strengthen the security barrier while promoting organizational efficiency, ensuring compliance audits and necessary management work, making sure the R&D intranet is both efficient and secure, which undoubtedly tests the comprehensive management capabilities of CTOs. In the AI era, the improvement of personal effectiveness for CTOs can also be highlighted with the help of such tools. Therefore, tasks that previously required collaboration across multiple roles (including internal control, security, architect, NLP R&D engineer, etc.) can now be accomplished with the aid of this tool, achieving an AI Coding security management solution.

Let’s Get Roo-Cline Moving 👇🏻

Intranet R&D Security Architecture

This is a preliminary AI-generated effect from the example above:

Note: The above generation used the privatized Deepseek V3 inference.

The small example we see is merely the initial generated result, and its shortcomings will need to be iterated and optimized further, precisely adapting to the actual business environment. Nevertheless, with the assistance of AI, the CTO’s capabilities are continuously expanding. The above solution, with slight modifications, can continue to be aided by AI for subsequent architectural constructions. All of this can be achieved within a completely closed R&D intranet, thereby enhancing the overall team efficiency.

Conclusion

This article briefly outlines the method of implementing AI Coding in the R&D intranet using Roo-Cline + Xinference. During the technology selection phase, we adopted the inference layer of the Xinference architecture to realize the inference functionality of intranet models. Xinference integrates multiple inference engines to meet the needs of different infrastructure environments, including Transformers, vLLM, SGLang, llamacpp, MLX, etc.

The latest official release has also achieved multi-instance KV Cache sharing among vLLM, further enhancing inference performance. Additionally, Xinference provides a simple and easy-to-use UI for managing the inference of intranet models, making this project highly suitable for localized AI project inference engines.

Moreover, during the implementation process, when facing the requirements of full-stack domestic innovation, Xinference also offers commercial solutions that achieve comprehensive adaptation to domestic innovations, providing strong support for large-scale R&D work in the intranet.

As a side note, this solution is not only applicable to private intranet R&D scenarios but is also attractive to individual developers who yearn for token freedom.

Related Resources

Xinference Documentation

https://inference.readthedocs.io/en/latest/

Xinference Open Source Repository

https://github.com/xorbitsai/inference

Cline Open Source Repository

https://github.com/cline/cline

Roo-Cline Open Source Repository:

https://github.com/RooVetGit/Roo-Cline

Cline MCP Servers Documentation:

https://github.com/nickbaumann98/cline_docs/blob/main/mcp/README.md

https://github.com/nickbaumann98/cline_docs/blob/main/mcp/mcp-server-from-scratch.md

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I am Si Lingqi 🐝, an internet practitioner passionate about AI. Here, I share my observations, thoughts, and insights. I hope to inspire those who love AI, technology, and life through my exploration.

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