How to Use the Linear MCP in LangChain

Q: How do I filter Linear issues for specific LangChain agents?

You pass the team ID to listissues or searchissues. This keeps your agent focused only on the relevant backlog for that specific project.

Q: Can I comment on Linear issues using LangChain?

Absolutely. Use the createcomment tool to post updates or feedback directly to an issue from your agent.

Q: Is it possible to track sprint progress in LangChain using Linear?

You can use the listcycles tool to pull current sprint data. This lets your agents report on completion percentages and active cycle timelines.

Chain your Linear issues directly into LangChain workflows for automated project tracking and smarter engineering pipelines.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Linear MCP to LangChain

Create your Vinkius account to connect Linear to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Linear with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate issue flows in LangChain

Connect your agents to the Linear API to bridge the gap between planning and execution. Use `create_issue` to generate tasks from incoming data, and rely on `update_issue` to keep progress moving without manual input. Your chains gain direct access to project state. By chaining `list_issues` with custom logic, you can trigger downstream tasks whenever a status changes.

Context-aware issue management

Stop hunting for task details across different tabs. Your agents can pull specific data using `get_issue` or perform broad lookups with `search_issues` to summarize current team velocity. Feed these results into your prompt templates to ensure your AI always works with the latest info. Every tool call provides real-time data from your board.

Direct team and cycle inspection

Understand your current sprint health by calling `list_cycles` to see progress percentages. This MCP Server gives your LangChain agents the ability to map out team dependencies before starting a task. Use `list_teams` to identify the right context for your queries. It turns your project structure into a queryable graph for your agents.

Setup guide

Set up Linear MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Linear tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "linear-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Linear transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Linear. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Linear now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Linear MCP in LangChain

Yes, you can. By integrating this MCP Server, your agents gain access to `update_issue`, allowing them to modify fields like priority or status based on your chain's logic.

You pass the team ID to `list_issues` or `search_issues`. This keeps your agent focused only on the relevant backlog for that specific project.

The server operates on a zero-trust model. It only interacts with your Linear workspace using the provided endpoint token, and your data is never stored by the Vinkius infrastructure.

Absolutely. Use the `create_comment` tool to post updates or feedback directly to an issue from your agent.

You can use the `list_cycles` tool to pull current sprint data. This lets your agents report on completion percentages and active cycle timelines.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python