Metadata-Version: 2.4
Name: airbyte-agent-linear
Version: 0.19.62
Summary: Airbyte Linear Connector for AI platforms
Project-URL: Homepage, https://github.com/airbytehq/airbyte-agent-connectors
Project-URL: Documentation, https://docs.airbyte.com/ai-agents/
Project-URL: Repository, https://github.com/airbytehq/airbyte-agent-connectors
Project-URL: Issues, https://github.com/airbytehq/airbyte-agent-connectors/issues
Author-email: Airbyte <contact@airbyte.io>
License: Elastic-2.0
Keywords: agent,ai,airbyte,api,connector,data-integration,linear,llm,mcp
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Typing :: Typed
Requires-Python: >=3.13
Requires-Dist: httpx>=0.24.0
Requires-Dist: jinja2>=3.0.0
Requires-Dist: jsonpath-ng>=1.6.1
Requires-Dist: jsonref>=1.1.0
Requires-Dist: opentelemetry-api>=1.37.0
Requires-Dist: opentelemetry-sdk>=1.37.0
Requires-Dist: pydantic>=2.0.0
Requires-Dist: python-dotenv>=1.0.0
Requires-Dist: pyyaml>=6.0
Requires-Dist: segment-analytics-python>=2.2.0
Description-Content-Type: text/markdown

# Linear agent connector

Linear is a modern issue tracking and project management tool built for software
development teams. This connector provides access to issues, projects, and teams
for sprint planning, backlog management, and development workflow analysis.


## Example questions

The Linear connector is optimized to handle prompts like these.

- Show me the open issues assigned to my team this week
- List out all projects I'm currently involved in
- Analyze the workload distribution across my development team
- What are the top priority issues in our current sprint?
- Identify the most active projects in our organization right now
- Summarize the recent issues for \{team_member\} in the last two weeks
- Compare the issue complexity across different teams
- Which projects have the most unresolved issues?
- Give me an overview of my team's current project backlog
- Create a new issue titled 'Fix login bug' for the Engineering team
- Update issue ABC-123 to set priority to urgent
- Change the title of issue XYZ-456 to 'Updated feature request'
- Add a comment to issue DEF-789 saying 'This is ready for review'
- Update my comment on issue to say 'Revised feedback after testing'
- Create a high priority issue for the backend team about the API performance
- List all users in my Linear workspace
- Assign John to issue ABC-123
- Unassign the current assignee from issue XYZ-456
- Who is assigned to issue DEF-789?
- Reassign issue ABC-123 from John to Jane

## Unsupported questions

The Linear connector isn't currently able to handle prompts like these.

- Delete an outdated project from our workspace
- Schedule a sprint planning meeting
- Delete this issue
- Remove a comment from an issue

## Installation

```bash
uv pip install airbyte-agent-linear
```

## Usage

Connectors can run in open source or hosted mode.

### Open source

In open source mode, you provide API credentials directly to the connector.

```python
from airbyte_agent_linear import LinearConnector
from airbyte_agent_linear.models import LinearAuthConfig

connector = LinearConnector(
    auth_config=LinearAuthConfig(
        api_key="<Your Linear API key from Settings > API > Personal API keys>"
    )
)

@agent.tool_plain # assumes you're using Pydantic AI
@LinearConnector.tool_utils
async def linear_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})
```

### Hosted

In hosted mode, API credentials are stored securely in Airbyte Cloud. You provide your Airbyte credentials instead. 

This example assumes you've already authenticated your connector with Airbyte. See [Authentication](AUTH.md) to learn more about authenticating. If you need a step-by-step guide, see the [hosted execution tutorial](https://docs.airbyte.com/ai-agents/quickstarts/tutorial-hosted).

```python
from airbyte_agent_linear import LinearConnector

connector = LinearConnector(
    external_user_id="<your_external_user_id>",
    airbyte_client_id="<your-client-id>",
    airbyte_client_secret="<your-client-secret>"
)

@agent.tool_plain # assumes you're using Pydantic AI
@LinearConnector.tool_utils
async def linear_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})
```


## Full documentation

This connector supports the following entities and actions.

| Entity | Actions |
|--------|---------|
| Issues | [List](./REFERENCE.md#issues-list), [Get](./REFERENCE.md#issues-get), [Create](./REFERENCE.md#issues-create), [Update](./REFERENCE.md#issues-update) |
| Projects | [List](./REFERENCE.md#projects-list), [Get](./REFERENCE.md#projects-get) |
| Teams | [List](./REFERENCE.md#teams-list), [Get](./REFERENCE.md#teams-get) |
| Users | [List](./REFERENCE.md#users-list), [Get](./REFERENCE.md#users-get) |
| Comments | [List](./REFERENCE.md#comments-list), [Get](./REFERENCE.md#comments-get), [Create](./REFERENCE.md#comments-create), [Update](./REFERENCE.md#comments-update) |


For all authentication options, see the connector's [authentication documentation](AUTH.md).

For detailed documentation on available actions and parameters, see this connector's [full reference documentation](./REFERENCE.md).

For the service's official API docs, see the [Linear API reference](https://linear.app/developers/graphql).

## Version information

- **Package version:** 0.19.62
- **Connector version:** 0.1.7
- **Generated with Connector SDK commit SHA:** 4bded58d3cabff3ac257c30c425ccab118f6ed87