> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://you.com/docs/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://you.com/docs/_mcp/server.

# Biomedical Research Brief

Biomedical research questions belong on the **Research** endpoint, not plain `search` — the API is built to crawl peer-reviewed literature, trial registries, and regulatory filings and synthesize them with inline citations.

This template fans out **four parallel `Research` calls** per target — mechanism of action, clinical evidence, competitive landscape, and regulatory & safety — and assembles them into a single brief you can drop straight into a pipeline review or IC memo.

***

## What You'll Build

A command-line tool that takes a target (a drug, mechanism, or disease) and runs four `Research` calls concurrently, one per signal area. Each section comes back as cited Markdown — the script stitches them into one brief. Step `research_effort` up to `deep` or `exhaustive` for diligence-grade work.

***

## Try It Live

Run a real Research API request right here — no setup. Open the **Try It** panel below, paste your API key, edit the `input` to your target, and send it against the live endpoint.

### Request

POST [https://api.you.com/v1/research](https://api.you.com/v1/research)

```python Basic example
import os
from youdotcom import You
from youdotcom.models import ResearchEffort

you = You(api_key_auth=os.environ["YDC_API_KEY"])

res = you.research(
    input="Which global cities improved air quality the most over the past 10 years, and what measurable actions contributed?",
    research_effort=ResearchEffort.LITE,
)

print(res.output.content)

for i, source in enumerate(res.output.sources, 1):
    print(f"[{i}] {source.title or 'Untitled'}: {source.url}")

```

```typescript Basic example
import { You } from "@youdotcom-oss/sdk";
import type { ResearchRequest } from "@youdotcom-oss/sdk/models/operations";
import { ResearchEffort } from "@youdotcom-oss/sdk/models/operations";

const you = new You({ apiKeyAuth: process.env.YDC_API_KEY });

const request: ResearchRequest = {
  input: "Which global cities improved air quality the most over the past 10 years, and what measurable actions contributed?",
  researchEffort: ResearchEffort.Lite,
};

const result = await you.research(request);

console.log(result.output.content);

result.output.sources.forEach((s, i) => {
  console.log(`[${i + 1}] ${s.title ?? s.url}: ${s.url}`);
});

```

```javascript Basic example
import { You } from "@youdotcom-oss/sdk";
import { ResearchEffort } from "@youdotcom-oss/sdk/models/operations";

const you = new You({ apiKeyAuth: process.env.YDC_API_KEY });

const result = await you.research({
  input: "Which global cities improved air quality the most over the past 10 years, and what measurable actions contributed?",
  researchEffort: ResearchEffort.Lite,
});

console.log(result.output.content);

result.output.sources.forEach((s, i) => {
  console.log(`[${i + 1}] ${s.title ?? s.url}: ${s.url}`);
});

```

```curl Basic example
curl -X POST https://api.you.com/v1/research \
     -H "X-API-Key: <apiKey>" \
     -H "Content-Type: application/json" \
     -d '{
  "input": "Which global cities improved air quality the most over the past 10 years, and what measurable actions contributed?",
  "research_effort": "lite"
}'
```

```go Basic example
package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.you.com/v1/research"

	payload := strings.NewReader("{\n  \"input\": \"Which global cities improved air quality the most over the past 10 years, and what measurable actions contributed?\",\n  \"research_effort\": \"lite\"\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("X-API-Key", "<apiKey>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(res)
	fmt.Println(string(body))

}
```

```java Basic example
import com.mashape.unirest.http.HttpResponse;
import com.mashape.unirest.http.Unirest;

HttpResponse<String> response = Unirest.post("https://api.you.com/v1/research")
  .header("X-API-Key", "<apiKey>")
  .header("Content-Type", "application/json")
  .body("{\n  \"input\": \"Which global cities improved air quality the most over the past 10 years, and what measurable actions contributed?\",\n  \"research_effort\": \"lite\"\n}")
  .asString();
```

```csharp Basic example
using RestSharp;

var client = new RestClient("https://api.you.com/v1/research");
var request = new RestRequest(Method.POST);
request.AddHeader("X-API-Key", "<apiKey>");
request.AddHeader("Content-Type", "application/json");
request.AddParameter("application/json", "{\n  \"input\": \"Which global cities improved air quality the most over the past 10 years, and what measurable actions contributed?\",\n  \"research_effort\": \"lite\"\n}", ParameterType.RequestBody);
IRestResponse response = client.Execute(request);
```

```swift Basic example
import Foundation

let headers = [
  "X-API-Key": "<apiKey>",
  "Content-Type": "application/json"
]
let parameters = [
  "input": "Which global cities improved air quality the most over the past 10 years, and what measurable actions contributed?",
  "research_effort": "lite"
] as [String : Any]

let postData = JSONSerialization.data(withJSONObject: parameters, options: [])

let request = NSMutableURLRequest(url: NSURL(string: "https://api.you.com/v1/research")! as URL,
                                        cachePolicy: .useProtocolCachePolicy,
                                    timeoutInterval: 10.0)
request.httpMethod = "POST"
request.allHTTPHeaderFields = headers
request.httpBody = postData as Data

let session = URLSession.shared
let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in
  if (error != nil) {
    print(error as Any)
  } else {
    let httpResponse = response as? HTTPURLResponse
    print(httpResponse)
  }
})

dataTask.resume()
```

### Response (200)

```json
{
  "output": {
    "content": "Over the past decade, some global cities have shown improvements in air quality due to specific actions. Beijing, for example, made significant strides in improving its air quality through coordinated control measures with surrounding areas, collaborative planning, unified standards, joint emergency responses, and information sharing [[1]]. These efforts, including a five-year action plan for air pollution prevention and control, have helped to substantially improve air quality in the Jing-Jin-Ji region [[2]].\n\nParis has also seen improvements, with a 50% reduction in Nitrogen dioxide pollution and a 55% decrease in particulate matter citywide since 2005. This was achieved through its climate strategy, which included adding more bike lanes and increasing cycling networks [[3]]. Wellington, New Zealand, improved air quality in one of its busiest districts by increasing the percentage of electric buses from 5% to over 50% between 2022 and 2023, leading to a 50% reduction in black carbon and a 29% drop in nitrogen dioxide levels [[3]]. Mexico City, once known as the world's most polluted city in the early 1990s, has vastly improved its air quality, with the daily concentration of SO2 declining significantly by 2018 [[2]].\n\nWhile many cities globally have experienced persistently high or even rising levels of air pollution, especially concerning PM2.5 concentrations, NO2 exposures have shown an encouraging trend, with 211 more cities meeting the WHO guideline in 2019 compared to 2010 [[4]]. Local policies have been instrumental in these improvements [[4]].",
    "content_type": "text",
    "sources": [
      {
        "url": "https://sustainablemobility.iclei.org/air-pollution-beijing/",
        "title": "Clearing the skies: how Beijing tackled air pollution & what lies ...",
        "snippets": [
          "However, Beijing has made remarkable strides in recent years to improve its air quality, setting an example for other cities grappling with similar challenges. The root causes Comparing the past 20 years of its development to the 20 before, Beijing's GDP, population, and vehicles sharply increased by 1078%, 74%, and 335% respectively (UNEP, 2019).",
          "The city actively coordinated air pollution control measures with surrounding areas, such as the Beijing-Tianjin-Hebei region. Collaborative planning, unified standards, joint emergency responses, and information sharing significantly improved the air quality in this broader region.",
          "While Beijing has made significant strides, challenges remain. The average PM2.5 level is still six times higher than the World Health Organization's (WHO) guideline, and the 2021-22 improvement may be partially attributed to measures taken for the Winter Olympics.",
          "As China emerged as the world's largest automobile producer and consumer, it grappled with the detrimental impacts of increased oil consumption. Furthermore, the high level of coal consumption, especially during the winter heating season, contributed to the city's deteriorating air quality, reaching an average of 101.56 micrograms of PM2.5 particles per cubic meter in 2013 (Statista, 2023)."
        ]
      },
      {
        "url": "https://blogs.worldbank.org/en/voices/tackling-poor-air-quality-lessons-three-cities",
        "title": "Tackling poor air quality: Lessons from three cities",
        "snippets": [
          "The latest World Bank report, Clearing the Air: A Tale of Three Cities, chose Beijing, New Delhi and Mexico City to assess how current and past efforts improved air quality. In the early 1990s, Mexico City was known as the world's most polluted city and while there are still challenges, air quality has vastly improved. Daily concentration of SO2 – a contributor to PM2.5 concentrations – declined from 300 µg/m3 in the 1990s to less than 100 µg/m3 in 2018.",
          "In China, the ministries of Environmental Protection (now the Ministry of Ecology and Environment), Industry and Information Technology, Finance, Housing and Rural Development, along with the National Development and Reform Commission and National Energy Administration, worked together to issue a five-year action plan for air pollution prevention and control for the entire Jing-Jin-Ji region that surrounds Beijing and includes the municipality of Beijing, municipality of Tianjin, the province of Hebei, and small parts of Henan, Shanxi, inner Mongolia, and Shandong. What's encouraging about this new work is that it shows that with the right policies, incentives and information, air quality can be improved substantially, particularly as countries work to grow back cleaner after the pandemic.",
          "Failure to provide such incentives in India in the late 1990s resulted in the government developing plans but not implementing them. This led to India's Supreme Court stepping in to force the government to implement policy measures. A recent government of India program to provide performance-based grants to cities to reward improvements in air quality is a step in the right direction.",
          "The cost associated with health impacts of outdoor PM2.5 air pollution is estimated to be US$5.7 trillion, equivalent to 4.8 percent of global GDP, according to World Bank research. The COVID-19 pandemic further highlights why addressing air pollution is so important, with early research pointing to links between air pollution, illness and death due to the virus. On the flip side, the economic lockdowns caused by the pandemic, while devastating for communities, did result in some noticeable improvements in air quality but these improvements were inconsistent, particularly when it came to PM2.5."
        ]
      },
      {
        "url": "https://www.weforum.org/stories/2025/06/urban-mobility-improving-cities-air-quality/",
        "title": "Boosting clean air strategies in cities around the world | World ...",
        "snippets": [
          "Comprehensive cycling networks improve air quality while also transforming urban mobility. Paris has added more bike lanes to its cityscape in recent years. Between 2022 and 2023 alone, bike path usage doubled during rush hour and cyclists now outnumber cars on many of the city's streets. The results of Paris' growing cycling network are promising. Alongside other elements of Paris's climate strategy, cycling has contributed to a 50% reduction in Nitrogen dioxide pollution and 55% decrease in particulate matter citywide since 2005.",
          "In 2025, the alliance and members of the Global New Mobility Coalition will launch a new workstream on Transport and Urbanism that aims to speed up cross-sector collaboration on implementing proven mobility options to improve air quality and drive sustainable growth.",
          "Air pollution has been estimated to cause 4.2 million premature deaths worldwide per year, according to the World Health Organization, and nearly half of urban airborne contamination comes from city transport. While vehicles are essential to the vitality of cities, without the right policies in place, transport will continue to be a major contributor to harmful air pollution.",
          "In Wellington, New Zealand, the percentage of electric buses travelling across the city's heavily trafficked Golden Mile corridor rose from 5% to over 50% between 2022 and 2023. This shift led to a 50% reduction in black carbon and a 29% drop in nitrogen dioxide levels throughout the district. This has significantly improved air quality in one of the busiest parts of Wellington, as well as lowering noise pollution."
        ]
      },
      {
        "url": "https://www.stateofglobalair.org/resources/health-in-cities",
        "title": "Air Pollution and Health in Cities | State of Global Air",
        "snippets": [
          "Globally, NO2 exposures are heading in an encouraging direction as 211 more cities met the WHO guideline of 10 µg/m3 in 2019 compared to 2010. However, NO2 pollution is worsening in some other regions. Percentage of cities by population-weighted annual average pollutant concentration in 2010 and 2019. However, interventions targeting pollution at the local scale have successfully improved air quality in some cities.",
          "Local policies have improved air quality in some cities, while pollution has worsened in others. Overall, many cities have seen persistently high — and even rising — levels of air pollution over the past decade. PM2.5 exposures remained stagnant in many cities from 2010 to 2019.",
          "Cities are often hotspots for poor air quality. As rapid urbanization increases the number of people breathing dangerously polluted air, city-level data can help inform targeted efforts to curb urban air pollution and improve public health.",
          "Explore air quality and health data for your city using our new interactive app here. Most cities have polluted air, but the type of pollution varies from place to place. Local policies have improved air quality in some cities, while pollution has worsened in others."
        ]
      }
    ]
  }
}
```

***

## Prerequisites

Sign up at you.com/platform. The free tier includes 100 requests/day.

```bash
pip install youdotcom        # Python ≥ 3.10
npm install @youdotcom-oss/sdk   # Node ≥ 20
```

***

## Walkthrough

```python title="biomedical_research.py"
"""Biomedical Research Brief — fan out the Research API across four signal areas."""

import os
import sys
from concurrent.futures import ThreadPoolExecutor

from youdotcom import You, models

target = sys.argv[1] if len(sys.argv) > 1 else "GLP-1 receptor agonists for obesity"

you = You(api_key_auth=os.environ["YDC_API_KEY"])

PROMPTS = {
    "Mechanism of action": (
        f"Explain the mechanism of action for {target}. Cover the molecular target, "
        "downstream pathways, and any known biomarkers used to gate patient selection."
    ),
    "Clinical evidence": (
        f"Summarize the strongest clinical evidence for {target} from the last 24 months: "
        "phase, endpoints, effect sizes, and notable adverse events."
    ),
    "Competitive landscape": (
        f"Map the competitive landscape for {target}. Who else is targeting this mechanism, "
        "what stage are they at, and what differentiates each program?"
    ),
    "Regulatory & safety": (
        f"Summarize recent regulatory milestones, FDA/EMA correspondence, and safety signals "
        f"for {target} — black-box warnings, REMS, post-market surveillance findings."
    ),
}

def run(section_prompt):
    section, prompt = section_prompt
    return section, you.research(input=prompt, research_effort=models.ResearchEffort.STANDARD)

with ThreadPoolExecutor(max_workers=4) as pool:
    results = list(pool.map(run, PROMPTS.items()))

for section, response in results:
    print(f"## {section}\n")
    print(response.output.content)
    print()
    for i, source in enumerate(response.output.sources[:5], 1):
        print(f"  [{i}] {source.title} — {source.url}")
    print()
```

```bash
export YDC_API_KEY="your-api-key-here"
python biomedical_research.py "GLP-1 receptor agonists for obesity"
```

```typescript title="biomedical-research.ts"
import { You } from "@youdotcom-oss/sdk";

const target = process.argv[2] ?? "GLP-1 receptor agonists for obesity";
const you = new You({ apiKeyAuth: process.env.YDC_API_KEY! });

const prompts = {
  "Mechanism of action":
    `Explain the mechanism of action for ${target}. Cover the molecular target, ` +
    `downstream pathways, and any known biomarkers used to gate patient selection.`,
  "Clinical evidence":
    `Summarize the strongest clinical evidence for ${target} from the last 24 months: ` +
    `phase, endpoints, effect sizes, and notable adverse events.`,
  "Competitive landscape":
    `Map the competitive landscape for ${target}. Who else is targeting this mechanism, ` +
    `what stage are they at, and what differentiates each program?`,
  "Regulatory & safety":
    `Summarize recent regulatory milestones, FDA/EMA correspondence, and safety signals ` +
    `for ${target} — black-box warnings, REMS, post-market surveillance findings.`,
};

const sections = await Promise.all(
  Object.entries(prompts).map(async ([section, input]) => ({
    section,
    response: await you.research({ input, researchEffort: "standard" }),
  })),
);

for (const { section, response } of sections) {
  console.log(`## ${section}\n`);
  console.log(response.output.content);
  response.output.sources.slice(0, 5).forEach((s, i) => {
    console.log(`  [${i + 1}] ${s.title} — ${s.url}`);
  });
  console.log();
}
```

### Effort Levels

| **Level**    | **Speed** | **When to use**                                           |
| ------------ | --------- | --------------------------------------------------------- |
| `lite`       | \~5s      | Quick triage — is a target worth investigating?           |
| `standard`   | \~10–15s  | Default for daily pipeline reviews and competitor scans   |
| `deep`       | \~20–30s  | Diligence-grade synthesis across more sources per section |
| `exhaustive` | \~30–60s  | Investment- or BD-grade — widest source net               |

### Example Output

```markdown
## Mechanism of action

GLP-1 receptor agonists (semaglutide, tirzepatide) bind the GLP-1 receptor on
pancreatic β-cells and hypothalamic neurons, potentiating glucose-dependent
insulin release and slowing gastric emptying [1]. Tirzepatide additionally
agonizes the GIP receptor, contributing ~15-20% of incremental weight loss
versus pure GLP-1 agonism in head-to-head SURMOUNT-2 data [2].

  [1] Drucker DJ. Mechanisms of action of GLP-1 — https://pubmed.ncbi.nlm.nih.gov/...
  [2] Frias JP et al. SURMOUNT-2 — https://www.nejm.org/doi/full/10.1056/...

(Mechanism / Evidence / Competitive landscape / Regulatory all return as above.
Full output is ~3,500 words across the four sections.)
```

***

## Next Steps

The single-call Research walkthrough this template builds on.

Multi-pass retrieval for broad, exploratory questions.

All parameters and response fields.

***

## Resources

* [Research API Reference](/docs/api-reference/research/v1-research)
* [Python SDK](https://pypi.org/project/youdotcom/) · [TypeScript SDK](https://www.npmjs.com/package/@youdotcom-oss/sdk)
* [GitHub: ydc-research-sample](https://github.com/youdotcom-oss/ydc-research-sample)