OpenMed开源医疗AI

Building with an agent?

Start with the consumer agent-usage guide, or load the curated llms.txt documentation index. For callable local interfaces, use the MCP server, the typed tool registry, or the command-line interface. Ready-made cross-tool procedures live in the repository skills catalog.

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Agent Skills — build with OpenMed from your coding agent

The skills/ catalog ships portable Agent Skills for on-device de-identification, clinical NER, FHIR export, evaluation, and the healthcare workflows around them. The same SKILL.md folders work in Claude Code, OpenAI Codex, OpenCode, and compatible agents; the installer uses each client's skills directory plus the cross-client ~/.agents/skills convention.

git clone https://github.com/maziyarpanahi/openmed && cd openmed
./install-skills.sh          # installs for Claude Code, Codex, OpenCode, and ~/.agents/skills

Then ask your agent for the pipeline using synthetic placeholders:

Build a local OpenMed pipeline that de-identifies a synthetic discharge note and extracts medication entities.

It loads the right skills and produces an on-device pipeline (openmed.deidentify(...) → openmed.analyze_text(...)). After the one-time model download, run that pipeline locally on real notes; do not paste real PHI into a cloud-hosted agent prompt. See the skills catalog and 30-second example.

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or with Docker

docker build -t openmed:local . docker run --rm -p 8080:8080 -e OPENMED_PROFILE=prod openmed:local

Example output:

Abbreviated example response:

**Model lifecycle and service controls:** free memory on demand with
`GET /models/loaded`, `POST /models/unload`, and a `keep_alive` idle window;
v1.8 also includes API-key/JWT auth, no-PHI request logging, tracing, gRPC,
async jobs, webhooks, warm pools, dynamic batching, request coalescing, rate
and concurrency limits, `/livez`, `/readyz`, and opt-in metrics:

Example response:

See the full REST service guide.

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30-second example

```python from openmed import analyze_text

result = analyze_text( "Patient started on imatinib for chronic myeloid leukemia.", model_name="disease_detection_superclinical", )

for entity in result.entities: print(f"{entity.label:<12} {entity.text:<28} {entity.confidence:.2f}")

Quick start

```bash

REST API

A Docker-friendly FastAPI service with request validation, shared pipeline preload, and unified error envelopes.

```bash pip install --upgrade "openmed[hf,service]" uvicorn openmed.service.app:app --host 0.0.0.0 --port 8080