Linear

The Linear agent connector is a Python package that equips AI agents to interact with Linear through strongly typed, well-documented tools. It's ready to use directly in your Python app, in an agent framework, or exposed through an MCP.

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
List all users in my Linear workspace
Who is assigned to the most recently updated issue?
Create a new issue titled 'Fix login bug'
Update the priority of a recent issue to urgent
Change the title of a recent issue to 'Updated feature request'
Add a comment to a recent issue saying 'This is ready for review'
Update my most recent comment to say 'Revised feedback after testing'
Create a high priority issue about API performance
Assign a recent issue to a teammate
Unassign the current assignee from a recent issue
Reassign a recent issue from one teammate to another
Create a new issue in the 'Backend Improvements' project
Add a recent issue to a specific project
Move an issue to a different project
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

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Delete an outdated project from our workspace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

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.

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. If your Airbyte client can access multiple organizations, also set organization_id.

This example assumes you've already authenticated your connector with Airbyte. See Authentication to learn more about authenticating. If you need a step-by-step guide, see the hosted execution tutorial.

from airbyte_agent_linear import LinearConnector, AirbyteAuthConfig

connector = LinearConnector(
    auth_config=AirbyteAuthConfig(
        customer_name="<your_customer_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        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

Entities and actions

This connector supports the following entities and actions. For more details, see this connector's full reference documentation.

Entity	Actions
Issues	List, Get, Create, Update, Context Store Search
Projects	List, Get, Context Store Search
Teams	List, Get, Context Store Search
Workflow States	List, Context Store Search
Users	List, Get, Context Store Search
Comments	List, Get, Create, Update, Context Store Search

Authentication

For all authentication options, see the connector's authentication documentation.

Linear API docs

See the official Linear API reference.

Version information

Package version: 0.19.134
Connector version: 0.1.19
Generated with Connector SDK commit SHA: c5258debd165ab701252c1436d4acfb475bf92b9
Changelog: View changelog

Release files for airbyte-agent-linear 0.19.134

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for airbyte-agent-linear 0.19.134
File	Size	Uploaded
airbyte_agent_linear-0.19.134.tar.gz	173.2 kB	Apr 9, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for airbyte-agent-linear 0.19.134
File	Interpreter	ABI	Platform	Reset
airbyte_agent_linear-0.19.134-py3-none-any.whl 196.5 kB Apr 9, 2026	Python 3	none	any	Details

Total release size: 369.6 kB

Release files / airbyte_agent_linear-0.19.134.tar.gz

Download URL	airbyte_agent_linear-0.19.134.tar.gz
Size	173.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`8203e1594487113c51876e18eb340661c3f903cb2ff5a113c46bcc5e64c3ce8a`
BLAKE2b-256 checksum How to use checksums	`9002f16f1eef8eb2ecd0b7bcdeadd678d67dad82fd13b8894d965997d99ae54b`
Upload date	Apr 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.13.12`

Release files / airbyte_agent_linear-0.19.134-py3-none-any.whl

Download URL	airbyte_agent_linear-0.19.134-py3-none-any.whl
Size	196.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`cf34932f544024a7c9bd9faffe65310fdd7839899aba6a767c92f9ea69280809`
BLAKE2b-256 checksum How to use checksums	`91ec37042a6d286ceaf163a7157ba063efeca1e3a934649e32e918cb9f90c0ca`
Upload date	Apr 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.2.0 CPython/3.13.12`