Research As Code

Make research a dependency.

Declare the data your agents need. DeepSieve researches it, keeps it current on your schedule, and serves a stable, sourced dataset whenever they ask.

Start integrating — free Read the docs

Declared schema

entities · fields · schedule

Maintained dataset

sourced · scored · re-checked

Your agent or app

reads · reacts to changes

re-checked on your schedule

Your agent should not research the same question twice.

Model memory ages. Live search varies from call to call. A maintained dataset gives every caller the same current facts, with the evidence attached.

Model memory

Answers immediately, from a world frozen at a training cutoff it cannot report.

Strength: Instant
Limit: Stale and opaque

Search on demand

Finds today's pages, but rebuilds the answer each time, and two calls can disagree.

Strength: Current at request time
Limit: Variable, slow, paid on every call

Maintained research

Researched once into the shape you declared, kept current on schedule, read like a table.

Strength: Structured and reusable
Limit: Needs asynchronous research runs

Declare once. Read continuously.

1Define
Declare a blueprint: the entities and fields, in plain language or code.
2Build + maintain
Each run researches, scores and sources it; monitoring re-checks it on your schedule.
3Read + react
Agents and apps query it; your system can respond when values change.

See what the dependency returns.

What your coding agent declares: the AI providers it depends on, and a lifecycle in days per column.

json

{
  "key": "ai_providers",
  "singular": "AI provider",
  "plural": "AI providers",
  "columns": [
    { "name": "name",               "type": "text", "label": "Provider",                "lifecycle_days": 365 },
    { "name": "latest_deprecation", "type": "text", "label": "Latest deprecated model", "lifecycle_days": 7 },
    { "name": "retires",            "type": "date", "label": "Retires",                 "lifecycle_days": 7 },
    { "name": "replacement",        "type": "text", "label": "Replacement",             "lifecycle_days": 7 },
    { "name": "replacement_pricing","type": "text", "label": "Replacement pricing",     "lifecycle_days": 7 },
    { "name": "breaking_change",    "type": "text", "label": "Other breaking change",   "lifecycle_days": 7 }
  ]
}

Illustrative example in the API’s real response shape. Field names are the contract; the values are a sample.

Plug it into the tools your agents already use.

MCP
Agent-native setup and reads
Connect MCP
CLI + Skills
Coding-agent workflows
Copy setup prompt
REST + Webhooks
Applications and production systems
Open API docs

Paste into your coding agent

Copy this into your coding assistant

Set up DeepSieve by following the instructions here:
https://deepsieve.ai/setup.md

Build the whole loop against dry_run first: it’s free and unlimited, returns real response shapes, and bills and saves nothing.

Start integrating — free Read the docs

Production details

Stop making agents relearn the web.

Give them maintained research they can query like any other dependency.

Autonomous systems are only as trustworthy as the facts they can inspect and revisit.

Start integrating — free