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The ask tool lets you query everything your team has saved to Hyper using plain English. It uses vector search with reranking to find the most relevant memory documents, then synthesizes a coherent answer from what it finds. Whether you want to know what a teammate is working on, why a particular technology was chosen, or what decisions came out of last week’s planning session, ask surfaces the answer without you knowing exactly where to look.

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Parameters

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Returns

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answer
string
A synthesized answer drawn from the team’s memory documents. The answer reflects the current state of knowledge in Hyper — it’s generated at query time from retrieved context, not stored verbatim.

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How It Works

When you call ask, Hyper runs a vector search across your workspace’s memory documents to find the most semantically relevant chunks. Those chunks are reranked for relevance, then passed to the synthesis layer, which composes a coherent answer. The whole pipeline runs in a single call — you get a ready-to-read answer, not a list of raw documents.

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Example Calls

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Technology decision

ask("why did we choose PostgreSQL over MySQL?")

Answer: The team chose PostgreSQL for row-level security (RLS), which is
essential for multi-tenant data isolation. MySQL's RLS support was
considered immature at the time of the decision (Q3 2024).

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Teammate status

ask("what is Marcus working on right now?")

Answer: Marcus is currently focused on the AML (anti-money laundering)
integration for the payments module. He's targeting a code-complete date
of May 9th and is blocked on API credentials from the compliance team.

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Open tasks

ask("what are the open tasks for the auth sprint?")

Answer: The following items are still open in the auth sprint:
- Row-level security implementation (Alex, in progress)
- OAuth token rotation endpoint (unassigned)
- Session invalidation on password reset (Maya, in review)

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Recent events

ask("what happened this week?")

Answer: This week the team merged the new onboarding flow, deferred the
billing integration to Q3, and started the RLS implementation. Maya's
PR for onboarding is in review. The team also resolved the staging
environment issue that was blocking QA.

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When to Use ask vs. ask-with-history

ask returns the current state of the answer — what Hyper knows right now. It’s fast and the right tool for the vast majority of questions. Use ask-with-history instead when you explicitly need to understand how something changed over time — for example, “how has our pricing evolved?” or “what did the team decide about X before we reversed course?” If the question is “what is X right now?”, ask is faster and sufficient.
The UserPromptSubmit lifecycle hook calls ask automatically with your message text before the model responds. This means your AI assistant already has relevant team context pulled in before it formulates a reply — you don’t need to call ask manually in most workflows.

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Tips for Better Answers

  • Name people specifically — “what is Alex working on?” returns more targeted results than “what is the team working on?”
  • Reference project names — “what’s the status of the RLS project?” is better than “what’s the status of the database work?”
  • Ask for reasoning — “why did we decide to use edge caching?” surfaces rationale that remember captured alongside outcomes.
  • Use time references — “what happened last week?” or “what did we decide in Q1?” constrains retrieval temporally.