Give agents tools for secure, real-time access to fetch, search, write, and sync across every system, with replication, entity mapping, and auth built-in.
About this 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.
CRM
Sales Analytics
Customer Data
Version Information
Package version
0.19.126
Connector version
0.1.16
SDK commit
7956e2eb1a5082bef0be1b8ededead13e1089437
Support Open Source
Check us out on Github and join the Airbyte community
Installation & Usage
1
Install Package
Using uv or pip
bash
uv pip install airbyte-agent-linear
2
Import
Initialize and use
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>"
)
)3
Tool
Add tools to your agent
python
@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 {})Supported Entities & Actions
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| Entity | Actions |\n|--------|---------|\n| Issues | [List](./REFERENCE.md#issues-list), [Get](./REFERENCE.md#issues-get), [Create](./REFERENCE.md#issues-create), [Update](./REFERENCE.md#issues-update), [Search](./REFERENCE.md#issues-search) |\n| Projects | [List](./REFERENCE.md#projects-list), [Get](./REFERENCE.md#projects-get), [Search](./REFERENCE.md#projects-search) |\n| Teams | [List](./REFERENCE.md#teams-list), [Get](./REFERENCE.md#teams-get), [Search](./REFERENCE.md#teams-search) |\n| Users | [List](./REFERENCE.md#users-list), [Get](./REFERENCE.md#users-get), [Search](./REFERENCE.md#users-search) |\n| Comments | [List](./REFERENCE.md#comments-list), [Get](./REFERENCE.md#comments-get), [Create](./REFERENCE.md#comments-create), [Update](./REFERENCE.md#comments-update), [Search](./REFERENCE.md#comments-search) |
Example Prompts
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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
Why Airbyte for AI Agents?
Built for production AI workloads with enterprise-grade reliability
Agent-Native Design
Structured, LLM-friendly schemas optimized for AI agent consumption with natural language query support.
Secure Authentication
Built-in OAuth 2.0 handling with automatic token refresh. No hard-coded credentials.
Production Ready
Battle-tested connectors with comprehensive error handling, logging, and retry logic.
Open Source
Fully open source under the MIT license. Contribute, customize, and extend freely.
Works with your favorite frameworks
🦜
LangChain
🦙
LlamaIndex
🤖
CrewAI
⚡
AutoGen
🧠
OpenAI Agents SDK
🔮
Claude Agents SDK
Frequently Asked Questions
Didn't find your answer? Please don't hesitate to reach out.
The Linear connector supports API key authentication. You provide a Personal API key from your Linear account settings (Settings > API > Personal API keys) directly as the auth_config when initializing the connector in open source mode. In hosted mode, credentials are stored securely in Airbyte Cloud using Airbyte client credentials instead.
Can I use this connector with any AI agent framework?
The connector is compatible with any Python-based AI agent framework including LangChain, LlamaIndex, CrewAI, Pydantic AI, and custom implementations.
Does this connector support write operations?
Yes, the Linear connector supports several write operations. It can create and update issues, create and update comments, assign or reassign issues to team members, and add issues to projects. However, delete operations are not currently supported — you cannot delete issues, projects, or comments.
How is this different from the Airbyte data connector?
Agent connectors are specifically designed for AI agents and LLM applications. They provide natural language interfaces, optimized response formats, and seamless integration with agent frameworks, unlike traditional ETL-focused connectors.
Will there be a platform for agent connectors?
The hosted version with secure credential storage through Airbyte Cloud is already available. See the hosted usage section in the documentation for setup instructions.