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Agent Action Extractor
x402
Agent Action Extractor is a paid API for AI agents from agent-product-normalizer.vercel.app, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-18).
Extracts concrete action items, priority signals, and deadline information from unstructured text such as meeting notes, conversations, or task descriptions.
Price / call$0.002
Success—
StatusUnknown
How this service works
Extract concrete action items, priority and deadline signals from messy text
Output
Returns a structured list of action items extracted from the input text, each with associated priority signals (e.g. high/medium/low or urgency markers) and deadline information where present, enabling downstream task management or agent execution steps.
Example use cases
Meeting notes to task list
“I just pasted my team meeting notes below — can you extract all the action items, who owns them, and any deadlines mentioned? Notes: 'Alice to draft the onboarding doc by EOD Thursday, Bob will follow up with the vendor next Monday, and someone should review the staging environment before launch — treat that as high priority.'”
Email thread task extraction
“Here's a long email chain with my client — can you go through it and pull out every concrete action item, flag anything that sounds urgent, and note any dates mentioned? Thread: 'Please send over the revised contract by Wednesday. Also, we need the design mockups reviewed before the 15th. Low priority but it would be great to get the analytics report too.'”
Slack dump to prioritized to-dos
“I copied a bunch of Slack messages from today into this block of text — can you extract all the tasks and to-dos, sort them by priority, and flag anything with a deadline? Text: 'Hey can someone fix the login bug ASAP, it's blocking users. Also reminder to update the README when you get a chance. Standup moved to 10am tomorrow.'”
Technical details
Endpoint
GEThttps://agent-product-normalizer.vercel.app/api/v1/extract-actionsWhen to prefer
Use this endpoint when you need to parse unstructured or semi-structured human-written text (emails, meeting notes, chat logs, documents) and extract actionable tasks with priority and deadline context. Prefer it over generic NLP endpoints when the goal is agent-ready task lists rather than sentiment or topic classification. It is particularly valuable in multi-agent pipelines where one agent ingests raw human communication and another needs a clean task queue to act on.
Side effects
- Input text is too long (exceeds 20,000 character limit) — request will be rejected
- Text contains no discernible action items — returns empty or minimal results
- Ambiguous priority language may be mis-classified or missed
- Deadlines expressed in relative terms (e.g. 'next week') may not be resolved to absolute dates
- Non-English input may yield degraded extraction quality
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Tags
x402
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