Introduction

Autumn turns a single request into finished, sourced data. Give it a goal and it plans, searches the web, scrapes pages, cross-checks facts, and returns clean output rows with the sources behind every value. One API call does the work of a whole research and enrichment pipeline, and it keeps running in the cloud while you do something else. Every task gets a durable task_id, its own sandbox and agent, task-local files, streamed events, and output rows you can fetch any time. Start a task, walk away, and come back to results. When you need changes, continue the same task instead of starting over. Autumn is one HTTP API. Start with the Quickstart, read the Task API for every route, or install the Skill to give another agent the same task surface.

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Start

• In your code: create an API key in Settings → Developer and follow the Quickstart.
• In ChatGPT, Claude.ai or another MCP client: add https://api.autumn.ai/mcp as a custom connector. See Hosted MCP.
• In a coding agent: paste the prompt from Connect your agent.

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The model

Autumn exposes one public primitive: a task. You hand it a goal; it plans, works the web, and writes output rows you can fetch any time. One planning flag, clarify, decides whether it may stop to ask a question first. The task model covers this properly: start modes, turns, and why continuing a task beats starting a new one.

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When to use Autumn

Reach for Autumn when the work is too big or too slow for a single chat turn and you want it done end to end:

• finding or enriching companies, people, jobs, filings, pages, or contacts
• collecting rows with sources and evidence
• scraping or checking multiple public sources
• continuing a previous task without starting over
• producing output files or task-local artifacts

Avoid Autumn for quick questions your agent can answer directly.

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Next