Calypso Context

Multimodal RAG your users can check.

Load PDFs, images, web pages, and data into buckets, and get cited answers through an OpenAI-compatible API, MCP, n8n, or a web widget. Hosted, so there is no vector database to run.

Calypso Context is the knowledge layer behind Calypso WhatsApp. Developers use it directly.

Live demo

Pick a corpus, ask, and read the citations.

Prompts

Quick start

Point the OpenAI SDK at Calypso. That is the integration.

Same client, same Responses call. The model is your agent; the answer comes back with the sources it drew on.

  1. 01Create a project API key in Project → API Keys.
  2. 02Put your sources in a bucket and wait for ready.
  3. 03Call calypso-agent, or a named agent, from any OpenAI SDK.
CALYPSO_API_KEY
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["CALYPSO_API_KEY"],
    base_url="https://api.calypso.so/v1",
)

response = client.responses.create(
    model="calypso-agent",
    input="What does the refund policy say about opened items?",
)

print(response.output_text)
# Sources arrive as file_search_call.results[] with source_index,
# label, page_number, and access_url.

What it reads

PDFs, images, pages, and data. Read, not just stored.

A source is accepted if Calypso can actually read it, and rejected at upload if it cannot. Every source lands in a bucket and reports one ready bit when it is searchable.

Up to 25 MB per file, 1 to 100 files per batch. The full list of accepted types, and how each gets in.

The shape

One source layer. Named agents. Answers everywhere.

01 · Buckets

Scoped, durable source memory.

A bucket is a named collection of sources. Ingestion writes into buckets, agents search buckets, and nothing crosses between them unless you bind it. Create one on first use, or provision it by slug.

Buckets in the docs

The doors

Four ways in. One answer.

Proof

Ask a real corpus yourself.

Controls

Scoped, traceable, yours.

Plans

Included in every plan.

There is no separate Context SKU. Pick the capacity: knowledge storage, answers per month, and agents.

Context pricing
  • Starter

    $19/ month

    100 MB of knowledge · 1,000 answers a month

  • Pro

    $49/ month

    1 GB of knowledge · 5,000 answers a month

  • Scale

    $199/ month

    10 GB of knowledge · 25,000 answers a month

Questions and answers

Before you load your first bucket.

New to the idea? Read what multimodal RAG is.

Start today

Load a bucket. Call the agent. Ship.

Start the 14-day trial on Pro limits, create a project API key, and get a cited answer in your first hour.