Teamwork Graph is Atlassian’s unified data layer that connects teamwork data from across Atlassian apps, like Jira and Confluence, as well as external tools. It enables organizations to centralize their data, break down silos, and streamline workflows. For developers, Teamwork Graph offers opportunities to contribute to and leverage this unified data model, enabling richer integrations and smarter, more contextual experiences for users.
This transformation is made possible by the foundation of Atlassian’s platform: Teamwork Graph. Teamwork Graph is a powerful data layer that connects people, work, and knowledge across all Atlassian experiences. For the first time, apps will be able to both contribute to and leverage this unified data model, enabling richer integrations and smarter, more contextual experiences for users.
At its core, Teamwork Graph is a common data model that represents the building blocks of teamwork: work items, documents, messages, users, groups, projects, and more. Each item—such as a Jira work item, Confluence page, or Google Drive file—is represented in Teamwork Graph as an object.
Every object belongs to a specific object type, which defines its category and the set of
properties it has. For example, Jira, Asana, and GitHub all have different objects that represent
work. Jira has Work items, Asana has Tasks, and GitHub has Issues. In Teamwork Graph, these
are grouped under the object type Work item.
This standardization allows data from different tools to be integrated, queried, and related in a consistent way.
Teamwork Graph connectors are available through Forge's Early Access Program (EAP).
EAPs are offered to selected users for testing and feedback purposes. We are currently working with a select group of EAP participants to get their apps production-ready and available for publishing on Marketplace.
If you are interested in joining this EAP, you can express interest through this form.
Teamwork Graph connectors bring data from external apps and tools into Teamwork Graph. Atlassian provides 100 out-of-the-box Teamwork Graph connectors that allow customers to connect popular tools to Teamwork Graph. With the Teamwork Graph connector module (EAP), you can now also build custom Teamwork Graph connectors to connect data from your app or other external tools to Teamwork Graph.
Teamwork Graph connectors work by:
Once in Teamwork Graph, this data can be used across Atlassian experiences like Rovo Search, Chat, Agents, and Atlassian Analytics. Note that currently, some experiences require additional configuration or code changes to enable data visibility.
Teamwork Graph doesn’t just store data—it maps relationships between objects. For example, it can link a document to a Jira work item, or a message to a project. These relationships are what enable powerful, context-aware features like:
Today, developers can add data to Teamwork Graph via connectors. Soon, you’ll also be able to access data from the Graph in your apps. This will allow you to:
This two-sided model—adding data to the Graph and accessing data in the Graph—makes Teamwork Graph a true platform for extensibility.
Integrating external data into Teamwork Graph gives customers a more complete and accurate picture of how their organizations work. By bringing in information and context from outside Atlassian, customers can streamline workflows, improve collaboration, and gain richer insights across teams.
This enables a range of benefits for customers, including:
While Teamwork Graph operates behind the scenes as part of the platform, users feel its impact in many ways, such as through:
| Feature | Description | Example |
|---|---|---|
| Unified search | Search for content across Atlassian and third-party tools from a single interface. | Find a Google Drive file while working in Jira or Confluence. |
| Integrated workflows | Automate and streamline processes that span multiple tools. | Automatically create Jira work items from CRM events or sync tasks between Jira and another tool. |
| Personalized recommendations | Get intelligent suggestions based on unified data and user context. | Suggest relevant documents or collaborators for a project. |
| Analytics and insights | Visualize and analyze data from multiple sources in Atlassian Analytics. | Generate cross-tool dashboards and reports. |
These capabilities reduce context switching, improve efficiency, and create a more cohesive user experience. Teamwork Graph extensibility is key to this, as it allows you to build applications that become integral to delivering these richer, more connected customer experiences.
Connecting to Teamwork Graph provides you with a unique opportunity to build smarter, more integrated applications within the Atlassian ecosystem. Some examples of use cases include:
Unify data across tools: Build connectors that ingest data from third-party SaaS tools (like Google Drive, Slack, Salesforce, Workday, GitHub, etc.) or internal systems into Teamwork Graph.
Enhance existing apps: Add a Teamwork Graph connector to your existing app so that its data becomes discoverable and actionable across Atlassian experiences, such as Chat and Search.
Power analytics, reporting, and AI: Ingest data into Teamwork Graph to enable advanced analytics, reporting, and AI-driven insights.
To get started extending Teamwork Graph, check out the following resources:
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