If you're searching for "how to connect Linear to OpenClaw", the real question is usually not just whether the connection is possible. It's how to make Linear usable inside an OpenClaw workflow with the right model, the right context, and the right level of control.
That's the practical framing.
OpenClaw gives you the orchestration layer: connectors, skills, tools, prompts, approvals, and the ability to run workflows where your team already works. Linear provides the domain context. The integration becomes valuable when those two pieces are connected cleanly.
What “Connect Linear to OpenClaw” Actually Means
In practice, connecting Linear to OpenClaw usually involves four layers:
- Authentication so OpenClaw can securely access Linear
- Tooling or proxy endpoints that expose the right Linear actions and data
- Skills/instructions that tell OpenClaw how to reason over Linear context
- Model selection so the assistant uses the right LLM for the job
That last piece matters more than most people expect.
Which Models Can You Use?
OpenClaw is model-flexible, so a Linear integration does not need to be tied to a single provider. Depending on your setup, teams commonly want to use:
- OpenAI models like GPT-4o, GPT-4.1, and o3 for broad reasoning and tool use
- Anthropic models like Claude 3.5 Sonnet, Claude Sonnet 4/4.5, and Claude Opus for strong writing, analysis, and long-context work
- Google models like Gemini 1.5 Pro or newer Gemini models for multimodal and large-context workflows
- Other model backends if your OpenClaw environment exposes them
The practical point: you can connect Linear to OpenClaw once, then run different workflows with different models depending on the job.
For example:
- Use Claude for nuanced summarisation or drafting
- Use OpenAI for structured extraction, tool-heavy workflows, or general-purpose copiloting
- Use Gemini when multimodal or very large context windows matter
A Good Integration Pattern for Linear
A strong Linear + OpenClaw setup usually looks like this:
- OpenClaw receives a request in chat or from an automation
- It calls the right Linear endpoint or proxy
- The selected model reasons over the returned context
- OpenClaw returns an answer, draft, classification, or action
- High-risk actions stay behind approvals or structured guardrails
That is what makes the setup operational rather than just experimental.
Step-by-Step: Connect Linear to OpenClaw
Step 1: Get Your Linear API Key
Go to Linear → Settings → API and create a Personal API Key. This key authenticates all requests to Linear's GraphQL API at https://api.linear.app/graphql. No OAuth flow required for personal or service account usage.
Step 2: Learn the GraphQL Schema
Linear's API is GraphQL-only. Use the Linear API explorer to understand the schema before building your proxy. Key objects: Issue, Cycle, Project, Team, User. Queries are flexible — you can request exactly the fields you need.
Step 3: Build the Proxy and Skill File
Your proxy will accept simple HTTP requests from OpenClaw and translate them into GraphQL queries. Write ~/.openclaw/skills/linear.md with your team identifiers and the types of queries available. Linear's consistent naming makes skill file writing relatively straightforward.
Model-Specific Workflow Ideas
Linear + OpenAI
Use this when you want a strong general-purpose setup for extraction, classification, action planning, and tool-driven workflows around Linear.
Linear + Claude
Use this when you want better writing quality, clearer summaries, stronger nuance, and reliable long-context reasoning over Linear data.
Linear + Gemini
Use this when the workflow benefits from large context windows, multimodal inputs, or Google-native ecosystem alignment.
Common Mistakes
Most teams do not fail because the model is bad. They fail because:
- the Linear connection is too thin
- the model lacks the right live context
- prompts are vague
- no structured outputs are enforced
- permissions and approvals are skipped
- one model is forced to do every job, even when another would be a better fit
The best setup is usually one integration layer, multiple model options, and clear guardrails.
Challenges and Caveats
GraphQL Adds a Layer of Complexity
If your proxy developer isn't familiar with GraphQL, there's a learning curve. REST is more familiar territory — GraphQL query construction, pagination with cursors, and error handling work differently.
No Webhooks Without a Public Endpoint
Linear supports webhooks, but your EC2 instance needs a publicly accessible HTTPS endpoint to receive them. If you want OpenClaw to proactively notify your Slack channel when an issue is updated, you'll need to set up SSL and a public endpoint on your server.
Want Linear Connected to OpenClaw Without Building the Whole Stack Yourself?
Cody has Linear integration built in. Query issues, cycles, and team workload from Slack — no GraphQL proxy required.
Related OpenClaw Guides
Looking for a more workflow-first angle? See: Linear AI Automation and Linear AI Assistant.