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Batch Web Research With Parallel AI
For a researcher who needs N structured briefs in parallel. Creates a Parallel AI task group, launches a long-running research run per topic, monitors progress, and collects the structured outputs.
Research at scale is painful
Here's what doing this by hand looks like today — and why it doesn't scale.
Running 10 research passes sequentially takes the whole afternoon
Every brief comes back in a different shape, so synthesis is a second job
You'd batch them if there was a tool that ran parallel runs and collected structured output
Composio collapses all of this into one prompt — here's what that looks like.
Your agent runs it end-to-end.
- 01Create a Parallel AI task group for the batch
- 02For each topic, start a research run with your structured output schema
- 03Poll the task group until every task hits a terminal state
- 04Fetch each completed task's structured output
- 05Aggregate into a Google Doc or Sheet if an output target is set
- 06Return the aggregated link and per-task status
Ten briefs at once, same structure
Launch a batch, collect structured outputs across every topic — no sequential grind, no inconsistent formatting.
Paste this into Claude, Cursor, or Codex. It'll install the CLI, connect your apps, and run the task — end to end.
Summarize Today's Unread Emails
One prompt turns an inbox of 50+ unread into a 5-bullet digest with the one thing you must answer.
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Ask "what happened in Slack yesterday?" and get a consolidated, channel-by-channel recap with links back to the threads.
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Forward an email to your agent, get a titled Notion task with deadline extracted and properties set.
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