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Mirobody API turns messy health data into standardized, AI-ready records — and gives you two OpenAI-compatible surfaces to put agents to work on them. It’s the managed layer over the open-source Mirobody engine: we host storage, models, and keys; you point an OpenAI SDK at the base URL with an mb_live_* key.

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The loop: upload → standardize → use with AI

1

Send the right data shape

Structured readings go to POST /v1/data. POST /v1/files stores lab PDFs, photos, and spreadsheets and extracts their text. Use POST /v1/standardize to turn a report into standardized readings.
2

Every structured reading is standardized

Names resolve to LOINC deterministically (no LLM code-guessing), values normalize to UCUM units, every reading gets a FHIR mirror. "血糖(空腹)", "FBG" and "Glucose, fasting" become one series. See Standardization.
3

Put AI on top

Ask the Answers API for a grounded, evidence-cited answer — or build a full agent on the Agent API with your own tools, stored conversations, and the openai-agents SDK working out of the box.

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Four shapes of data, one door each

Health data arrives in four shapes — each has exactly one door in: Mirobody stores source data, standardizes structured readings, and makes both available to AI. You decide how data is collected and prepared before it reaches the API.

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Two API surfaces, one engine

Agent API — recommended

POST /v1/responses (OpenAI Responses-compatible). Your function tools, previous_response_id / session_id state, response.* streaming. The openai-agents SDK works by changing only the base URL.

Answers API

POST /v1/chat/completions. A closed grounded completion — one question, one evidence-backed answer. Drop-in for any OpenAI SDK, and ideal as a tool inside your own agent.
Not sure? Choose your API in one minute.

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Why Mirobody

  • Grounded, not plausible — answers can include health_records, citations, and the server tool steps used to gather evidence.
  • Standardized structured data — LOINC + UCUM + FHIR on records written through /v1/data or stored by /v1/standardize; your analytics and the agent read the same series.
  • OpenAI-compatible twice over — Chat Completions and Responses protocols; existing SDKs and agent frameworks just work.
  • Multi-tenant by design — one key, an isolated Subject per end-user; per-Subject right-to-be-forgotten deletes.
  • Open at the core — the same engine is open source; self-host it whenever you want.

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Two ways to run Mirobody

  • Hosted API (this platform) — an mb_live_* key against our managed clusters; storage, models, and keys are run for you. Start with the Quickstart.
  • Open source — git clone the Mirobody engine, build it with ./build.sh, and run it yourself.

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Start here

Quickstart

Key → data → standardized records → your first agent, in minutes.

Choose your API

Answers vs Agent — the one-minute decision.

API Overview

Auth, multi-tenancy, retention, errors, every endpoint.

Regions

Which cluster to use and what’s live today.