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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 prompts​

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
  • Create a new project called 'Q3 Platform Migration'
  • Update the description of the 'Backend Improvements' project
  • Change the target date of a project to next month
  • Mark a project as started
  • Set a project lead for the 'API Redesign' 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 prompts​

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  • Delete an outdated project from our workspace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

Entities and actions​

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

EntityActions
IssuesList, Get, Create, Update, Context Store Search, Context Store SQL Query, Semantic Search
ProjectsList, Get, Create, Update, Context Store Search, Context Store SQL Query
TeamsList, Get, Context Store Search, Context Store SQL Query
Workflow StatesList, Context Store Search, Context Store SQL Query
UsersList, Get, Context Store Search, Context Store SQL Query
CommentsList, Get, Create, Update, Context Store Search, Context Store SQL Query, Semantic Search

Linear API docs​

See the official Linear API reference.

Interfaces​

Use the Linear connector through the Airbyte Agent CLI, the Python SDK, or the API.

CLI​

Install the CLI:

curl -fsSL https://airbyte.ai/install.sh | bash

Authenticate with Airbyte:

airbyte-agent login

Create the connector. The CLI opens the hosted setup flow:

airbyte-agent connectors create --json '{
  "workspace": "<your_workspace_name>",
  "name": "linear"
}'

Describe the connector to see its supported entities and actions:

airbyte-agent connectors describe --json '{
  "workspace": "<your_workspace_name>",
  "name": "linear"
}'

Execute an action:

airbyte-agent connectors execute --json '{
  "workspace": "<your_workspace_name>",
  "name": "linear",
  "entity": "issues",
  "action": "list"
}'

Python SDK​

Installation​

uv pip install airbyte-agent-sdk

Usage​

Connectors can run in hosted or open source mode.

Hosted​

In hosted mode, API credentials are stored securely in Airbyte Agents. 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.

The connect() factory returns a fully typed LinearConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.linear import LinearConnector

connector = connect("linear", workspace_name="<your_workspace_name>")

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use LinearConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.linear import LinearConnector

connector = connect("linear", workspace_name="<your_workspace_name>")

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@LinearConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="linear_inspect",
    docs_tool="linear_read_docs",
)
async def linear_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@LinearConnector.agent_tool(framework="pydantic_ai")
async def linear_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@LinearConnector.agent_tool(framework="pydantic_ai")
async def linear_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.linear import LinearConnector

connector = connect("linear", workspace_name="<your_workspace_name>")

@LinearConnector.agent_tool(
    inspect_tool="linear_inspect",
    docs_tool="linear_read_docs",
)
async def linear_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@LinearConnector.agent_tool()
async def linear_inspect():
    return await connector.inspect_connector()

@LinearConnector.agent_tool()
async def linear_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (linear_inspect, linear_read_docs, linear_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated LinearConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or LinearConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.linear import LinearConnector

connector = connect("linear", workspace_name="<your_workspace_name>")

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@LinearConnector.tool_utils
async def linear_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment):

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.linear import LinearConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = LinearConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Open source​

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

The recommended pattern is build_connector_tools, which gives the agent three tools bound to this connector: inspect_connector, read_skill_docs, and execute. The agent can inspect the connector, read only the skill-doc section it needs, and then execute:

inspect_connector() -> read_skill_docs() -> read_skill_docs(section="...") -> execute(entity, action, params)

Pass section IDs verbatim as the outline lists them, prefix included (actions.<entity>.<action>, not <entity>.<action>); anything else returns an error the agent has to recover from.

The builder names its tools inspect_connector, read_skill_docs, and execute, so the tool sets for more than one connector collide when registered on the same agent. Renaming the callables at registration avoids the collision, but the generated execute guidance still names inspect_connector and read_skill_docs, pointing the model at the wrong tools. Use the agent_tool pattern below instead: it weaves your own names into that guidance.

Pydantic AI
from airbyte_agent_sdk import build_connector_tools
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.linear import LinearConnector
from airbyte_agent_sdk.connectors.linear.models import LinearLinearApiKeyAuthenticationAuthConfig

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

tools = build_connector_tools(connector, framework="pydantic_ai")
agent = Agent("openai:gpt-4o", tools=tools.as_list())
Custom tool bodies​

When you need custom tool bodies — or a framework without native support — use LinearConnector.agent_tool. Register execute, inspect, and docs together so the agent can fetch connector guidance progressively. Pass the framework explicitly when it has a supported failure strategy:

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.linear import LinearConnector
from airbyte_agent_sdk.connectors.linear.models import LinearLinearApiKeyAuthenticationAuthConfig

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

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@LinearConnector.agent_tool(
    framework="pydantic_ai",
    inspect_tool="linear_inspect",
    docs_tool="linear_read_docs",
)
async def linear_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@agent.tool_plain
@LinearConnector.agent_tool(framework="pydantic_ai")
async def linear_inspect():
    return await connector.inspect_connector()

@agent.tool_plain
@LinearConnector.agent_tool(framework="pydantic_ai")
async def linear_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

Use the same three-function pattern with framework="langchain", "openai_agents", or "mcp" and that framework's registration decorator. Each value translates connector failures into the framework's own signal:

framework=Tool failures surface as
"pydantic_ai"pydantic_ai.ModelRetry
"langchain"langchain_core.tools.ToolException (set handle_tool_error=True to feed it back to the model)
"openai_agents"the failure message returned to the model as the tool result
"mcp"fastmcp.exceptions.ToolError
"none" (default)airbyte_agent_sdk.AirbyteToolError

On a framework the SDK does not support natively — or in a raw LLM dispatch loop — omit framework= and handle AirbyteToolError yourself:

No framework
from airbyte_agent_sdk import AirbyteToolError
from airbyte_agent_sdk.connectors.linear import LinearConnector
from airbyte_agent_sdk.connectors.linear.models import LinearLinearApiKeyAuthenticationAuthConfig

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

@LinearConnector.agent_tool(
    inspect_tool="linear_inspect",
    docs_tool="linear_read_docs",
)
async def linear_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

@LinearConnector.agent_tool()
async def linear_inspect():
    return await connector.inspect_connector()

@LinearConnector.agent_tool()
async def linear_read_docs(section: str | None = None):
    return await connector.read_skill_docs(section)

# Advertise all three to the model, using each function's docstring as its description.
handlers = {
    fn.__name__: fn
    for fn in (linear_inspect, linear_read_docs, linear_execute)
}

# `tool_name` and `tool_args` come from the model's tool call in your dispatch loop.
try:
    tool_result = await handlers[tool_name](**tool_args)
except AirbyteToolError as err:
    tool_result = str(err)  # hand the message back to the model as an errored tool result

Each function's docstring carries the guidance the model needs, so pass it through as the tool description wherever you register it.

Legacy alternatives​

These examples are kept for existing integrations. The deprecated LinearConnector.tool_utils pattern loads the connector's full generated catalog into one broad execute tool description instead of letting the agent read skill docs on demand. For new code, use build_connector_tools or LinearConnector.agent_tool above.

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.linear import LinearConnector
from airbyte_agent_sdk.connectors.linear.models import LinearLinearApiKeyAuthenticationAuthConfig

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

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@LinearConnector.tool_utils
async def linear_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

Authentication​

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

IP allow list​

If your organization restricts access to specific IPs, add the Airbyte Agents IP addresses to your allow list.

Version information​

Connector version: 0.1.19

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