Integration Tutorial April 13, 2026

Sync Linear Issues with AI Memory

Linear is where modern engineering teams track work. REM Labs turns your entire Linear history into searchable AI memory -- issues, comments, projects, and cycles indexed semantically so you find context by meaning, not by issue number.

What Gets Synced

The Linear integration pulls structured data from your workspace via the Linear GraphQL API:

Issues -- title, description (markdown), and all comments with author attribution
Properties -- state, priority, assignee, labels, estimate, due date -- all stored as filterable metadata
Projects -- project name and description linked to their child issues via knowledge graph edges
Cycles -- cycle metadata (start/end dates, progress) associated with issues in that cycle
Relations -- "blocks", "is blocked by", "relates to", "duplicate of" links become graph edges

Connect Your Workspace

In the REM Labs Console, go to Integrations and click Connect Linear. Authorize via OAuth and select the teams to sync. You can sync all teams or choose specific ones.

Import via API

curl -X POST https://api.remlabs.ai/v1/memory/sync/linear/import \
  -H "Authorization: Bearer sk-slop-..." \
  -H "Content-Type: application/json" \
  -d '{
  "linear_token": "lin_api_...",
  "teams": ["Engineering", "Platform"],
  "include_comments": true,
  "include_archived": false,
  "sync_mode": "incremental"
}'

Setting include_archived: false skips archived issues. Incremental mode uses Linear's updatedAt timestamp to only process issues modified since the last sync.

Webhook for Real-Time Sync

Configure a Linear webhook so new issues, comments, and state changes are synced to REM Labs in real time. When an engineer closes an issue or adds a comment, the knowledge base updates within seconds.

curl -X POST https://api.remlabs.ai/v1/integrations/linear/webhook \
  -H "Authorization: Bearer sk-slop-..." \
  -H "Content-Type: application/json" \
  -d '{
  "linear_token": "lin_api_...",
  "events": ["Issue", "Comment", "IssueLabel"],
  "teams": ["Engineering"],
  "enabled": true
}'

Search Your Engineering History

curl -X POST https://api.remlabs.ai/v1/integrations/linear/search \
  -H "Authorization: Bearer sk-slop-..." \
  -H "Content-Type: application/json" \
  -d '{
  "query": "performance regression in search indexing",
  "filters": {"team": "Platform", "state": "Done"},
  "limit": 10
}'

The query finds the issue titled "FTS5 rebuild taking 40s on large datasets" and the related issue "Add query result caching layer" -- because semantic search understands the relationship between "performance regression" and "slow rebuild times."

API Endpoints Reference

Endpoint	Method	Description
`/v1/memory/sync/linear/import`	POST	Import issues from Linear teams
`/v1/integrations/linear/webhook`	POST	Configure real-time sync webhook
`/v1/integrations/linear/status`	GET	Check sync status per team
`/v1/integrations/linear/search`	POST	Search within the Linear namespace

GraphQL efficiency: The integration uses Linear's GraphQL API with batched queries to minimize API calls. A workspace with 5,000 issues typically syncs in under 90 seconds. See Linear integration docs.

Give your AI your full engineering history

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