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Documentation Index

Fetch the complete documentation index at: https://docs.hyperfx.ai/llms.txt

Use this file to discover all available pages before exploring further.

Hyper Database is the structured-data layer for your agents. Where Knowledge Bases cover unstructured docs, Hyper Database handles tables — rows of records that agents can read, write, query, and update. Two flavors:
  • Managed tables — Hyper auto-creates these as agents do their work (e.g. a “leads” table that fills up when your prospecting agent runs, a “campaigns” table that builds up over time)
  • User-defined tables — you create the schema yourself; agents read and write against it
Both are queryable from any agent in your workspace.

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When to use this

Use Hyper Database when:
  • The data is structured (rows and columns, not docs)
  • Multiple agents and tasks need to read and write to the same source of truth
  • You want historical state an agent can update over time, not just one-shot context
  • You’re building a custom workflow where the agent’s output feeds the next run’s input
Examples:
  • A leads table populated by a prospecting agent and dequeued by an outreach agent
  • A campaigns table tracking spend, performance, and status across ad platforms
  • A research-tracker table storing every competitor scan run and its findings
  • A content calendar where a writing agent picks up ideas, drafts, and marks them shipped

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Managed tables

Some agent templates and skills automatically create and maintain managed tables for you. For example, the Apollo prospect skill writes lead records to a managed leads table; the platform-usage-analysis skill writes findings to its own table. You don’t need to set these up — they appear in Hyper Database as agents use them. You can read, query, and even export them like any other table.

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Custom tables

To define your own:
1

Open Hyper Database

From the sidebar, click More → Hyper Database → Create table.
2

Define the schema

Add columns and pick a type for each (text, number, date, boolean, JSON). Hyper handles the storage layer.
3

Seed it (optional)

Import a CSV or paste rows to start with existing data, or leave it empty for agents to populate.
4

Attach to an agent

Open an agent’s Resources tab and add the table. The agent can now query it, append rows, update existing rows, and run aggregations.

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Queries from chat

Agents handle SQL-like queries themselves. You ask in plain language: 
From the leads table, find all contacts in the SaaS vertical that we
haven't emailed in the last 30 days and rank them by company headcount.
Save the result as a new view.
The agent figures out the query, runs it, and returns results — and can chain it into the next action (draft outreach, push to a sequence, etc.).

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Powering tasks

Database tables are most powerful inside tasks. For example:
  • A daily prospecting task pulls 50 new leads into the leads table
  • An hourly outreach task picks the top 10 unworked leads, drafts personalized emails, and updates the row status
  • A weekly reporting task aggregates the table and posts a summary to Slack
The table is the shared state across all of those runs.

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Going further

  • Build a task that reads or writes to your database on a schedule
  • Visualize tables with Interfaces — turn them into shared dashboards
  • For unstructured docs, use Knowledge Bases instead