Developer Tools

Advanced computational tools for developers, data scientists, and bio-informaticians

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Boost life science research innovation with user-friendly developer tools

Save time, reduce lab costs and prevent human error

Conducting research today requires the collection and analysis of huge amounts of data, fast. Labguru's best-in-class developer tools facilitate the agility and rapidity needed for cutting-edge life science research and development. Rest APIs, GraphQL, Workflow Editor, and Dashboards enable you to incorporate your data, from various sources, into one holistic lab management system, permitting you to gain insights and see the big picture.

RESTful API for life science


An Application Program Interface (API) is a set of routines and tools for connecting with databases without the user interface. Labguru's RESTful API - has an extensive list of endpoints divided into Labguru modules that allow communicating with your account in a programmatic way. 

  • Import and export data in and out of Labguru- create new entities or update existing ones, upload files, export data and pages, generate reports, and more.

  • Connect to other databases and applications - using Labguru’s  API will enable you to connect external services or applications to streamline data from or to Labguru ELN. For Example, Connect the Labguru Shopping list with a third-party ERP application.

  • Analysis- Bioinformaticians and data scientists can use Labguru’s API to extract experimental data collected and documented by the lab’s scientists, analyze it and push the analysis results back to Labguru.


GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. With GraphQL, you have the ability to request precisely what you need, making it easier to evolve APIs over time and providing powerful developer tools.Labguru is the first to integrate GraphQL, providing you with the ability to design the structure of query data. This innovative capability drastically improves performance and provides scientists with the kind of flexibility required for cutting edge research. 

Integrating GraphQL provides Labguru users with three major advantages:


Streamline data processing, and derive data-based insights faster

By defining exactly how you would like your data structured, you can prevent return of excess data and save the time normally wasted downloading unnecessary results. 



Easily Access Complex Data Structures

All tables in Labguru databases are linked to each other via associations. Leverage GraphQL to create a primary query and a second query simultaneously, seamlessly generating a complex returned data structure. For example peptide information along with the properties of the plasmid used to construct it. 

Design customized user interfaces

The GraphQL query builder feature allows you to design customized user interfaces, tailoring their digital workspaces to their needs for increased operational efficiency.


R and Python Labguru packages 

The Labguru R and Python packages are API wrappers that allow using Labguru terminology to interact with your Labguru Database. The packages encapsulate multiple API calls so that complicated API calls or other functions can be easily used. You can download the packages from our GitHub repository.

R & python
Labguru Dashboards


Labguru Dashboards is an add-on solution to Labguru ELN, enabling you to create and present visualized reports based on pre-designed SQL queries. Labguru Dashboards can be presented within Labguru and separately on a wall screen.

Learn more about Dashboards- here.

The Workflow Editor 

The Workflow Editor is an interoperable solution that allows users to customize and automate actions and events that happen in their lab. The Workflow Editor is a Labguru add-on, resides on a stand-alone server, and connects to Labguru via API. 

To learn more about the Workflow editor- click here.

Labguru Workflow Editor

Devoting too much time to mundane, repetitive data management tasks?

Labguru Developer Tools help dismantle data silos, and save time previously spent on manually transferring information from one system to another.

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