Quickstart - Mirobody

Mirobody in three steps: ① connect data → ② it comes back standardized → ③ use it with AI. Everything is OpenAI-compatible — you’ll need an mb_live_* key and ~10 minutes.

Account management — API keys, usage, and a live Playground — lives in the developer console at platform.mirobody.ai. Sign in with email code, open API Keys, create one (the secret is shown once):

export MIROBODY_API_KEY="mb_live_..."

All /v1 endpoints share one base URL — pick your contracted cluster:

https://mirobody-api.thetahealth.ai/v1

China-region deployments use https://mirobody-api.thetahealth.cn/v1. See Regions for the full host matrix.

① Connect your data

Health data arrives in three shapes — each has exactly one door in:

Data shape	Typical source	How it goes in	Endpoint
Structured readings	Devices / wearables (you integrate them, write daily aggregates), manual entries	Structured records	`POST /v1/data` — pre-aggregate high-frequency samples; episodes (sleep, workouts) carry `time` + `end_time`. See the device-data cookbook.
Files / photos	Lab reports, checkup PDFs, phone photos	File upload	`POST /v1/files` — OCR + parsing included; `POST /v1/extract` when you want the parsed readings back synchronously
Narrative notes	”dizzy and a headache”, dictated notes	Narrative text	`POST /v1/extract` — quantifiable readings are extracted; pure narrative returns `data: []` plus a `note` (a journal endpoint is on the roadmap)

Mirobody hosts, standardizes and serves the data to AI — collecting it (device OAuth, app capture) is your side of the line.Write structured readings with POST /v1/data (retention is required), or upload a lab report — PDF, photo, spreadsheet — with POST /v1/files:

# Structured records
curl https://mirobody-api.thetahealth.ai/v1/data \
  -H "Authorization: Bearer $MIROBODY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
        "user": "alice",
        "retention": "permanent",
        "records": [
          {"indicator": "fasting_glucose", "value": 5.4, "unit": "mmol/L", "time": "2026-06-16T07:30:00Z"},
          {"indicator": "FBG",             "value": 5.1, "unit": "mmol/L", "time": "2026-06-17T07:25:00Z"}
        ]
      }'

# Or a document (OCR'd and parsed automatically)
curl https://mirobody-api.thetahealth.ai/v1/files \
  -H "Authorization: Bearer $MIROBODY_API_KEY" \
  -F "user=alice" -F "file=@lab_report.pdf"

user is the tenant-isolation key — pass each end-user’s stable id and their data never mixes.

② It comes back standardized

Note the two spellings above — fasting_glucose and FBG. Read the data back and both rows carry the same LOINC code, a canonical name, UCUM-parsed values, and a FHIR mirror id:Done experimenting? DELETE /v1/subjects/alice erases everything this quickstart wrote (records, files, conversations) in one call.

curl "https://mirobody-api.thetahealth.ai/v1/data?user=alice&limit=10" \
  -H "Authorization: Bearer $MIROBODY_API_KEY"

{
  "object": "list",
  "data": [
    {
      "indicator": "FBG",
      "value": "5.1 mmol/L",
      "parsed_value": "5.1",
      "parsed_unit": "mmol/L",
      "loinc_code": "1558-6",
      "canonical_name": "Fasting glucose [Mass/volume] in Serum or Plasma",
      "fhir_resource_id": "8f3c9a1e-...",
      "time": "2026-06-17T07:25:00+00",
      "source": "api",
      "comment": ""
    }
  ],
  "has_more": true,
  "subject": "alice"
}

That’s the standardization pipeline — deterministic name→LOINC (no LLM code-guessing) + UCUM + FHIR, on every write. To preview it on a document without storing anything, try POST /v1/extract.

③ Put AI on top

A grounded answer is three lines — the agent reads the standardized series, so “glucose” finds the rows written as “FBG”:

Answers API (3 lines)

from openai import OpenAI
client = OpenAI(api_key="mb_live_...", base_url="https://mirobody-api.thetahealth.ai/v1")
print(client.chat.completions.create(model="mirobody-flash", user="alice",
    messages=[{"role": "user", "content": "How is my fasting glucose trending?"}]
).choices[0].message.content)

And a real agent — with your own tool — is ten. The openai-agents SDK works against the Agent API by changing only the base URL:

openai-agents SDK (10 lines)

from agents import Agent, ModelSettings, Runner, function_tool, set_default_openai_client, set_tracing_disabled
from openai import AsyncOpenAI

set_default_openai_client(AsyncOpenAI(
    base_url="https://mirobody-api.thetahealth.ai/v1", api_key="mb_live_..."))
set_tracing_disabled(True)

@function_tool
def book_appointment(date: str) -> str:
    """Book a clinic appointment for the end user (ISO date)."""
    return f"Booked: {date} 09:30, Dr. Chen"

agent = Agent(name="Health assistant", model="mirobody-flash", tools=[book_appointment],
          model_settings=ModelSettings(extra_body={"user": "alice"}))
print(Runner.run_sync(agent, "Check my recent glucose; book a follow-up if it's trending up.").final_output)

Every agents-SDK agent that touches Mirobody tools must pass the Subject via model_settings=ModelSettings(extra_body={"user": ...}). Without it, the run reads the account-default Subject, not the user you meant.

Mirobody’s built-in tools read alice’s real data; when the model decides to book, it hands off to your function. See Function calling for the protocol underneath.

Prefer clicking over curl?

The console has a Playground — run chats, upload files, and inspect tool steps with no code.

Playground

Try the agent in the browser against your account.

Usage

Requests, tokens, and cost.

Next steps

Choose your API

Answers vs Agent in one minute.

Agent API

Client tools, stored conversations, streaming events.

Standardization

OCR → extraction → LOINC → UCUM → FHIR, explained.

SDK Examples

curl / Python / Node / openai-agents, end to end.

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Playground

Usage

​Next steps

Choose your API

Agent API

Standardization

SDK Examples

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Next steps