DeepModeling and DP Technology Launch Uni-Lab-OS: An Open-Source AI-Native Operating System for Intelligent Laboratories

Apr 17, 2025 18:18 CST Updated 18:18

DP Technology

Simulation R&D Platform Developer

In the深夜 laboratory, mechanical arms and AGV carts perform high-precision collaborative operations. AI dynamically adjusts experimental protocols based on real-time data, while automatically generated and analyzed experimental data silently refines algorithmic models—this is no longer a scene from a sci-fi movie. The era in which laboratories evolve from a "collection of tools" to "embodied intelligent agents" has already arrived.This operating system, Uni-Lab-OS Intelligent Laboratory Operating System, which打通 "Read, Calculate, Execute"闭环, is helping an increasing number of laboratories upgrade into smart R&D rehearsal fields.。

At the 2025 Zhongguancun Forum Annual Meeting - AI for Science Youth Forum, Zhang Linfeng, founder and CSO of DP Technology and director of the Beijing Academy of Artificial Intelligence, presented the first demonstration of Uni-Lab-OS at the forum., in anticipation of solving the pain points of inefficiency in traditional laboratory manual operations, isolated equipment, and scattered data. This innovation pushes towards "AI scientists" capable of autonomously designing experiments, saving researchers more time and energy. This significant innovative achievement connects all AI for Science infrastructures, linking data, knowledge, models, and instruments, facilitating the transformation of traditional laboratories into automated and intelligent ones, greatly enhancing research efficiency.

In April 2025, the Uni-Lab project officially open-sourced its Equipment Abstraction Layer module, fulfilling the open-source commitment made on March 29 at the Zhongguancun Forum.

This module is the core foundation of the Uni-Lab laboratory operating system, supporting device driver encapsulation, heterogeneous protocol unification, and experimental operations.EditorAnd other key functions, the source code and interface documentation are now open to manufacturers and developers.

Project Address:

https://github.com/dptech-corp/Uni-Lab-OS

Document Center:

https://readthedocs.dp.tech/Uni-Lab/v0.8.0/

To address the pain points of difficulty in balancing flexibility, stability, and scalability in laboratory scenarios, Uni-Lab-OS provides the following core features for connected devices:

Multi-node self-organizing network, supporting dynamic discovery of devices between laboratory workstations and cross-device scheduling;
Support flexible experimental process editing at multiple levels within and between workstations;
Experimental sample lifecycle tracking, full-process data synchronization, AI-Ready;
Cross-platform adaptation: supports deployment on devices such as Windows, Linux, and Mac;

Unified Abstraction,

Make Devices "Plug and Play"

Whether you need to integrate a single pump, single-axis slide, robotic arm, or an entire automated workstation, Uni-Lab's equipment abstraction layer can efficiently interface with various systems, featuring the following characteristics:

Multi-Protocol Native Support：Built-in Modbus, serial communication, PLC interface, HTTP/RPCSupports multiple communication protocols to meet the access requirements of heterogeneous devices;
Compatible with mainstream development language SDKs：Supports low-code rapid integration of existing SDKs (such as Python, C#, C++ drivers), significantly reducing development and debugging costs.

Flexible Integration

Compatible with Different Access Depths

For device integrators and workstation manufacturers, Uni-Lab supports three typical access methods:

1. Jog Level Control(such as AGV, robotic arms, pumps): capable of executing basic motion commands and collecting feedback;

2. Script-level Scheduling: For solidified experimental processes, invoke start/stop/monitor; furthermore, support configuring new experimental processes and converting them into scripts for execution;

3. Full Drive Access: All sub-devices are scheduled by Uni-Lab and can be combined into a custom protocol;

The community has currently adapted to various types of workstation structures, supporting the orchestration of task modules such as liquid preparation, sample addition, reaction, pipetting, and cleaning. All experimental actions can be implemented as standardized workflow nodes.

For developers and the community,

Co-build Experimental Equipment and Scenario Process Ecosystem

Individual developers/device suppliers can quickly write or access device drivers and register experimental actions based on templates;
The community-maintained protocol library already covers common scenarios such as organic synthesis and pipetting workstations.

If you need further support (e.g., custom integration, driver examples, experimental workflow templates), feel free to contact us or participate in the open-source community co-building! We believe that a unified interface and open-ecosystem smart lab infrastructure will continuously empower the future of scientific intelligence.

AssociationContact Us:

https://dptechnology.feishu.cn/share/base/form/shrcnHVTZ4pI11SWYRudP5PnQ7g

Following the path of open source and openness, with firm and rapid steps, DP Technology will further showcase more advancements of Uni-Lab-OS and practical collaborations with many partners at the Developer Conference on May 7. Stay tuned!

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About DP Technology

DP Technology is globallyAIFor Science Leaders, AI for Science means using AI to learn a series of scientific principles and knowledge, and further solve key problems in scientific research and industrial R&D fields.

Relying on deep cultivation in the field of interdisciplinary research, DP Technology has built the "Deep Potential·Universe" AI for Science large model system, further addressing key issues in scientific research and industrial development, and bringing research methods across numerous disciplines from the "trial-and-error experimentation / computer" era into the "Pre-trained ModelThe "era" has formed the "innovation-implementation" chain and open ecosystem of AI for Science, built micro-scale industrial infrastructure based on AI for Science, empowered "thousands of industries," and created a new generation of R&D systems for the most fundamental biomedical, energy, materials, and information science and engineering research in human economic development.

DP Technology is a national high-tech enterprise in China.Specialized, Refined, Unique, and Innovative"Little Giant" enterprises have established R&D centers in cities such as Beijing, Shanghai, and Shenzhen.The scientific research and technology team is led by an academician of the Chinese Academy of Sciences, bringing together over a hundred outstanding young scientists and engineers from various fields such as mathematics, physics, chemistry, biology, materials, and computer science. Among the company's members, more than 35% hold doctoral or postdoctoral degrees.Core members have won the "Gordon Bell Prize," the highest award in the field of global high-performance computing in 2020. Their related work was selected as one of China's top ten scientific and technological advances in 2020 and one of the ten major technological breakthroughs in the global AI field.