# Building an intelligent Linear app assistant URL: https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant > Build a Linear assistant that answers questions about projects and teams and writes well-scoped issues from chat, using Linear's MCP server and Dynamiq. Learn Build a Linear assistant that answers questions about projects and teams and writes well-scoped issues from chat, using Linear's MCP server and Dynamiq. Oleksii Babych · Machine learning engineer · December 5, 2024 · 4 min read · Updated September 29, 2026 In short This tutorial builds a project management assistant that reads your Linear workspace and creates well-described issues from a chat message. It connects a Dynamiq agent to Linear's hosted MCP server, adds session memory so follow-up requests make sense, and streams answers into a small Streamlit chat. You also limit which Linear actions the agent may take. An intelligent Linear assistant turns a chat message like "create two tasks for next sprint" into well-scoped Linear issues, and answers questions about your projects and teams. The simplest way to build one is to connect an agent to Linear's hosted MCP server, which exposes Linear's own tools for finding, creating and updating issues, projects and comments. This tutorial does that with the open-source Dynamiq SDK, adds memory and streaming, and puts the agent behind a small chat UI. ## What can the assistant do? Once connected, the assistant can: - **Answer questions** about projects, teams and issues, such as "what's still open in the payments project?" - **Create issues** with a clear title, context and a checklist of deliverables, from a one-line request. - **Follow up** on earlier messages in the same conversation, such as "add a third task for QA". - **Ask before guessing** when a request could belong to more than one team or project. ## How does the agent connect to Linear? The [Model Context Protocol](https://www.getdynamiq.ai/glossary/model-context-protocol) (MCP) is an open standard for giving AI agents tools from other applications. Linear runs a hosted MCP server, so the agent gets Linear's actions without custom integration code. According to [Linear's MCP documentation](https://linear.app/docs/mcp), the server lives at `https://mcp.linear.app/mcp` and accepts an API key or OAuth token in an `Authorization: Bearer` header, as well as an interactive sign-in flow. This tutorial uses an API key; create one in Linear's settings with only the permissions the assistant needs. ## Step 1: Install and configure ``` pip install dynamiq streamlit export OPENAI_API_KEY="..." export LINEAR_API_KEY="..." ``` ## Step 2: Connect to Linear's MCP server `MCPServer` discovers the tools a server offers and hands them to the agent. Print their names once, then decide which ones the assistant should have. ``` import os from dynamiq.nodes.tools.mcp import MCPServer, MCPStreamableHTTP linear = MCPServer( name="linear", connection=MCPStreamableHTTP( url="https://mcp.linear.app/mcp", headers={"Authorization": f"Bearer {os.environ['LINEAR_API_KEY']}"}, ), ) print([tool.name for tool in linear.get_mcp_tools()]) ``` To limit what the agent can do, pass `include_tools` with the tool names it may use, or `exclude_tools` with the ones it may not, such as anything that deletes or archives. ## Step 3: Create the agent with memory and streaming Session memory lets the agent follow a conversation, and streaming sends the answer to the UI as it is written. ``` from dynamiq import Workflow from dynamiq.connections import OpenAI as OpenAIConnection from dynamiq.flows import Flow from dynamiq.memory import Memory from dynamiq.memory.backends import InMemory from dynamiq.nodes.agents import Agent from dynamiq.nodes.llms import OpenAI from dynamiq.types.streaming import StreamingConfig, StreamingMode agent = Agent( name="PM assistant", llm=OpenAI(connection=OpenAIConnection(), model="gpt-4o", temperature=0.2), role=( "You help a product team manage work in Linear. Look up teams and projects before you " "create anything. Write issues with a clear title, the context, and a checklist of " "deliverables. If the team or project is ambiguous, ask before creating the issue." ), tools=[linear], memory=Memory(backend=InMemory()), streaming=StreamingConfig(enabled=True, mode=StreamingMode.FINAL), max_loops=12, ) workflow = Workflow(flow=Flow(nodes=[agent])) ``` `StreamingMode.FINAL` streams only the final answer. Switch to `StreamingMode.ALL` if you also want to show the agent's intermediate steps, such as each Linear lookup. ## Step 4: Stream answers from the workflow Run the workflow in a background thread and read the stream as it arrives. The streaming handler stops iterating when the workflow finishes. ``` import threading from dynamiq.callbacks.streaming import StreamingIteratorCallbackHandler from dynamiq.runnables import RunnableConfig def ask(message: str, user_id: str, session_id: str): handler = StreamingIteratorCallbackHandler() runner = threading.Thread( target=workflow.run, kwargs={ "input_data": {"input": message, "user_id": user_id, "session_id": session_id}, "config": RunnableConfig(callbacks=[handler]), }, ) runner.start() for event in handler: if event.event != "streaming" or not isinstance(event.data, dict): continue delta = event.data.get("choices", [{}])[0].get("delta", {}).get("content") if isinstance(delta, str): yield delta runner.join() ``` ## Step 5: Add a chat interface Save the code above as `assistant.py`. This Streamlit app keeps one conversation per browser session and writes the streamed answer as it arrives. Start it with `streamlit run app.py`. ``` import uuid import streamlit as st from assistant import ask st.title("Linear assistant") if "session_id" not in st.session_state: st.session_state.session_id = str(uuid.uuid4()) st.session_state.messages = [] for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"]) if prompt := st.chat_input("Ask about a project or describe an issue"): st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) with st.chat_message("assistant"): reply = st.write_stream(ask(prompt, user_id="pm-team", session_id=st.session_state.session_id)) st.session_state.messages.append({"role": "assistant", "content": reply}) ``` Try "Show me our teams and active projects", then "Create an issue in the data team's project for an exploratory analysis of churn, with deliverables". Open Linear to see the issue the agent wrote. ## How do you keep the assistant from doing too much? An assistant that can write to your project tracker needs the same care as a new team member with edit access: - **Scope the credentials.** Use an API key with only the permissions the assistant needs. - **Scope the tools.** Use `include_tools` or `exclude_tools` so the agent cannot delete or archive anything. - **Require approval for writes.** On the Dynamiq platform, an [approval](https://www.getdynamiq.ai/product/guardrails) can pause the step that creates an issue, so a person can edit its fields before it runs. - **Review what happened.** Every run is traced, including each Linear call the agent made. ## Can you do this without writing code? Yes. [AI Coworker](https://www.getdynamiq.ai/product/chat) connects to Linear as one of its app connectors and works in the browser and in Slack, Microsoft Teams and Telegram. A team can ask it for a status summary in a Slack thread or have it draft issues from meeting notes, without building anything. Linear is one of the 2,100+ app integrations available to agents, alongside Jira, Asana and other trackers; see the [integrations page](https://www.getdynamiq.ai/product/integrations). ## FAQ ### Can AI create Linear issues automatically? Yes. An agent connected to Linear's MCP server can create issues with titles, descriptions and deliverables from a plain-language request. Keep an approval step or a narrow set of tools if you want a person to confirm each change. ### What is the Linear MCP server? It is a hosted server from Linear that implements the Model Context Protocol. It gives AI agents tools for finding, creating and updating Linear objects such as issues, projects and comments, authenticated with OAuth or an API key. ### How do you stop the assistant from changing things it shouldn't? Combine a restricted API key, a limited tool list with `include_tools` or `exclude_tools`, clear instructions in the agent's role, and an approval step before any write. Traces show exactly what the agent did afterward. ### Does the assistant remember earlier messages? Within a conversation, yes: session memory is scoped by user and session, so follow-ups like "add one more task" work. For preferences that should persist across conversations, add long-term memory, as shown in our guide to [conversational agents with memory](https://www.getdynamiq.ai/post/building-a-conversational-ai-agent-with-dynamiq). ### Can the same approach work with Jira or other trackers? Yes. Point the agent at another MCP server or give it tools from the platform's app integrations, which include Jira and other project management tools. The agent code stays the same. Oleksii Babych Machine learning engineer Machine learning engineer at Dynamiq, writing tutorials on agents, memory, and multi-agent orchestration. On this page 1. [What can the assistant do?](https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant#what-can-the-assistant-do) 2. [How does the agent connect to Linear?](https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant#how-does-the-agent-connect-to-linear) 3. [Step 1: Install and configure](https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant#step-1-install-and-configure) 4. [Step 2: Connect to Linear's MCP server](https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant#step-2-connect-to-linears-mcp-server) 5. [Step 3: Create the agent with memory and streaming](https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant#step-3-create-the-agent-with-memory-and-streaming) 6. [Step 4: Stream answers from the workflow](https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant#step-4-stream-answers-from-the-workflow) 7. [Step 5: Add a chat interface](https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant#step-5-add-a-chat-interface) 8. [How do you keep the assistant from doing too much?](https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant#how-do-you-keep-the-assistant-from-doing-too-much) 9. [Can you do this without writing code?](https://www.getdynamiq.ai/post/building-an-intelligent-linear-app-assistant#can-you-do-this-without-writing-code) 10. 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