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APIS FOR AI AGENTS

Search + content extraction in one platform. Web, research, and financial data and clean page conten, in a single call.

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AN AGENT IS ONLY AS GOOD AS ITS CONTEXT

Most “search” tools return web links. Agents need sources and content. Blog posts about SEC filings, not the filings. Articles about clinical trials, not the trial data. Pages full of navigation and pop-ups, not the text your model can use.

Discover how
Search API

What are the 5 year historical stock prices for Nvidia and AMD?

Stock PricesClearRun

Found 2 results

Price of AMD for 2021-01-28 to 2026-01-28

97%
structured•6,887 chars

Use cases:

How users use Valyu

Research pipelines

Extract full articles from search results for deeper analysis

Data enrichment

Pull structured fields from company pages, docs, product listings

Content monitoring

Track changes over time with consistent extraction

Search

Search, built for AI agents

The highest accuracy search API. Web, research, and financial data in a single call.

Web + Proprietary

Search the open web alongside licensed datasets: arXiv + PubMed, full-text SEC filings, live market data from 70+ exchanges, clinical trials, patents, and economic indicators. One API. Every source your agent needs.

Natural Language Queries

Agents don’t want to parse ticker symbols or look up NCT IDs. They want to ask for what they need. “Phase 3 melanoma immunotherapy trials” works — no database identifiers required.

AI-Ready Outputs

Results come with content, relevance scores, and source metadata. Feed them directly into your agent’s context window. No extraction. No cleaning. No post-processing.

Try Search in Playground
Search API

What are the 5 year historical stock prices for Nvidia and AMD?

Price of AMD for 2021-01-30 to 2026-01-30

97%valyu/valyu-stocksstructured6890 charsNASDAQ$ USD
Stock Chart+198.4%
202120222023202420252026
Content

Extract content from any URL

Turn web pages into clean, structured data — Markdown, JSON, screenshots, or AI summaries.

Clean Markdown

Get page content as structured markdown. Headlines, paragraphs, lists, tables — preserved.

Structured Extraction

Define a JSON schema. Extract specific fields from any page.

AI Summaries

Summarize the page with control over length and focus.

Screenshot capture

Need the visual? Get a screenshot of any URL alongside the extracted content.

Try Contents in Playground
SEC Filing10-KAnnual Report

Tesla, Inc.

Annual Report pursuant to Section 13 or 15(d) of the Securities Exchange Act of 1934

CIK: 0001318605FY 2024NASDAQ: TSLAVerified

Key Financials

Revenue

$96.77B

Net Income

$7.93B

Total Assets

$106.62B

Employees

140,473

Contents

Part I — Business

15

Part I — Risk Factors

27

Part II — Financial Statements

39

Part II — MD&A

51

Part III — Directors & Officers

63

FORMER CONFORMED NAME: TESLA MOTORS INC DATE OF NAME CHANGE: 20050222 </SEC-HEADER> <DOCUMENT> <TYPE>10-K <SEQUENCE>1 <FILENAME>tsla-20231231.htm <DESCRIPTION>10-K <TEXT> <XBRL> <?xml version="1.0" ?><!--XBRL Document Created with the Workiva Platform--><!--Copyright 2024 Workiva--><!--r:4361b8b5-d51a-4c9f-b09b-b6721cc29d7f,g:865b3b8d-1460-4446-b611-684494e0f490,d:605b1b87e9dd4fc1a8af0ddafef749a2--><html xmlns:link="http://www.xbrl.org/2003/linkbase" xmlns="http://www.w3.org/1999/xhtml" xmlns:tsla="http://www.tesla.com/20231231" xmlns:ixt="http://www.xbrl.org/inlineXBRL/transformation/2020-02-12" xmlns:ecd="http://xbrl.sec.gov/ecd/2023" xmlns:xbrldi="http://xbrl.org/2006/xbrldi" xmlns:country="http://xbrl.sec.gov/country/2023" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:us-gaap="http://fasb.org/us-gaap/2023" xmlns:exch="http://xbrl.sec.gov/exch/2023" xmlns:iso4217="http://www.xbrl.org/2003/iso4217" xmlns:xbrli="http://www.xbrl.org/2003/instance" xmlns:ix="http://www.xbrl.org/2013/inlineXBRL" xmlns:dei="http://xbrl.sec.gov/dei/2023" xmlns:ixt-sec="http://www.sec.gov/inlineXBRL/transformation/2015-08-31" xmlns:srt="http://fasb.org/srt/2023" xmlns:xlink="http://www.w3.org/1999/xlink" xml:lang="en-US"><head><meta http-equiv="Content-Type" content="text/html"/> <title>tsla-20231231</title></head><body><div style="display:none"><ix:header><ix:hidden><ix:nonNumeric contextRef="c-1" name="dei:AmendmentFlag" format="ixt:fixed-false" id="f-32">false</ix:nonNumeric><ix:nonNumeric contextRef="c-1" name="dei:EntityCentralIndexKey" id="f-33">0001318605</ix:nonNumeric><ix:nonNumeric contextRef="c-1" name="dei:DocumentFiscalYearFocus" id="f-34">2023</ix:nonNumeric><ix:nonNumeric contextRef="c-1" name="dei:DocumentFiscalPeriodFocus" id="f-35">FY</ix:nonNumeric><ix:nonNumeric contextRef="c-38" name="us-gaap:AccountingStandardsUpdateExtensibleList" id="f-250">http://fasb.org/us-gaap/2023#AccountingStandardsUpdate202006Member</ix:nonNumeric><ix:nonNumeric contextRef="c-1" name="us-gaap:OperatingLeaseIncomeComprehensiveIncomeExtensibleList" id="f-543">http://fasb.org/us-gaap/2023#RevenueFromContractWithCustomerExcludingAssessedTax</ix:nonNumeric><ix:nonNumeric contextRef="c-29" name="us-gaap:OperatingLeaseIncomeComprehensiveIncomeExtensibleList" id="f-544">http://fasb.org/us-gaap/2023#RevenueFromContractWithCustomerExcludingAssessedTax</ix:nonNumeric><ix:nonNumeric contextRef="c-30" name="us-gaap:OperatingLeaseIncomeComprehensiveIncomeExtensibleList" id="f-545">http://fasb.org/us-gaap/2023#RevenueFromContractWithCustomerExcludingAssessedTax</ix:nonNumeric><ix:nonNumeric contextRef="c-128" name="us-gaap:FiniteLivedIntangibleAssetUsefulLife" id="f-667">P7Y</ix:nonNumeric><ix:nonNumeric contextRef="c-129" name="us-gaap:FiniteLivedIntangibleAssetUsefulLife" id="f-674">P3Y</ix:nonNumeric><ix:nonFraction unitRef="number" contextRef="c-300" decimals="6" name="us-gaap:DebtInstrumentConvertibleConversionRatio1" scale="0" id="f-1142">0.048414</ix:nonFraction><ix:nonNumeric contextRef="c-6" name="us-gaap:OperatingLeaseLiabilityCurrentStatementOfFinancialPositionExtensibleList" id="f-1151">http://www.tesla.com/20231231#AccruedAndOtherCurrentLiabilities</ix:nonNumeric><ix:nonNumeric contextRef="c-5" name="us-gaap:OperatingLeaseLiabilityCurrentStatementOfFinancialPositionExtensibleList" id="f-1152">http://www.tesla.com/20231231#AccruedAndOtherCurrentLiabilities</ix:nonNumeric> <ix:nonNumeric contextRef="c-5" name="us-gaap:OperatingLeaseLiabilityNoncurrentStatementOfFinancialPositionExtensibleList" id="f-1155">http://fasb.org/us-gaap/2023#OtherLiabilitiesNoncurrent</ix:nonNumeric><ix:nonNumeric contextRef="c-6" name="us-gaap:OperatingLeaseLiabilityNoncurrentStatementOfFinancialPositionExtensibleList" id="f-1156">http://fasb.org/us-gaap/2023#OtherLiabilitiesNoncurrent</ix:nonNumeric><ix:nonNumeric contextRef="c-6" name="us-gaap:FinanceLeaseRightOfUseAssetStatementOfFinancialPositionExtensibleList" id="f-1165">http://fasb.org/us-gaap/2023#PropertyPlantAndEquipmentNet http://www.tesla.com/20231231#LeasedAssetsNet</ix:nonNumeric><ix:nonNumeric contextRef="c-5" name="us-gaap:FinanceLeaseRightOfUseAssetStatementOfFinancialPositionExtensibleList" id="f-1166">http://fasb.org/us-gaap/2023#PropertyPlantAndEquipmentNet http://www.tesla.com/20231231#LeasedAssetsNet</ix:nonNumeric> <ix:nonNumeric contextRef="c-6" name="us-gaap:FinanceLeaseLiabilityCurrentStatementOfFinancialPositionExtensibleList" id="f-1169">http://www.tesla.com/20231231#LongTermDebtAndFinanceLeasesCurrent</ix:nonNumeric><ix:nonNumeric contextRef="c-5" name="us-gaap:FinanceLeaseLiabilityCurrentStatementOfFinancialPositionExtensibleList" id="f-1170">http://www.tesla.com/20231231#LongTermDebtAndFinanceLeasesCurrent</ix:nonNumeric><ix:nonNumeric contextRef="c-5" name="us-gaap:FinanceLeaseLiabilityNoncurrentStatementOfFinancialPositionExtensibleList" id="f-1173">http://www.tesla.com/20231231#LongTermDebtAndFinanceLeasesNoncurrent</ix:nonNumeric><ix:nonNumeric contextRef="c-6" name="us-gaap:FinanceLeaseLiabilityNoncurrentStatementOfFinancialPositionExtensibleList" id="f-1174">http://www.tesla.com/20231231#LongTermDebtAndFinanceLeasesNoncurrent</ix:nonNumeric> </ix:hidden><ix:references xml:lang="en-US"><link:schemaRef xlink:type="simple" xlink:href="tsla-20231231.xsd"></link:schemaRef></ix:references><ix:resources><xbrli:context id="c-1"><xbrli:entity><xbrli:identifier scheme="http://www.sec.gov/CIK">0001318605</xbrli:identifier></xbrli:entity><xbrli:period><xbrli:startDate>2023-01-01</xbrli:startDate><xbrli:endDate>2023-12-31</xbrli:endDate></xbrli:period></xbrli:context><xbrli:context id="c-2"><xbrli:entity><xbrli:identifier scheme="http://www.sec.gov/CIK">0001318605</xbrli:identifier></xbrli:entity><xbrli:period><xbrli:instant>2023-06-30</xbrli:instant></xbrli:period></xbrli:context><xbrli:unit id="usd"><xbrli:measure>iso4217:USD</xbrli:measure></xbrli:unit>
Raw XBRL
Extracted

Three lines to power your agent

No pipelines to wire up. No parsing. No cleanup. Three lines is all it takes to give your agent real context.

from valyu import Valyu
 
valyu = Valyu(api_key="*********************************")
 
response = valyu.search(
    "CRISPR gene therapy clinical trials Phase 3"
)
 
# Access results
for result in response.results:
    print(f"Title: {result.title}")
    print(f"URL: {result.url}")
    print(f"Content: {result.content}")

Why choose Valyu

Intelligent Routing

Valyu analyzes your query and routes to the right sources automatically. Ask about “Tesla earnings” — it hits financial databases. Ask about “mRNA vaccine mechanisms” — it routes to academic literature. Faster. More relevant. Lower cost.

Control & Guardrails

Restrict results by domains, datasets, or URLs. Filter by date range. Set relevance thresholds. Control spend with max_price.

Predictable, transparent pricing

Pay per retrieval. No subscriptions. No token counting. Search API: $0.003 per result (web) | $0.01+ per result (proprietary) Contents API: $0.001 per successful extraction

SoTA across several benchmarks

DRACO is Perplexity's open expert-rubric benchmark of 100 long-form deep research tasks across 10 professional knowledge-work domains, including finance, medicine, academic research, law, and technology. Each output is graded by a per-criterion judge against a domain-expert rubric. We ran every commercially available deep research API end-to-end against the same 100 questions.

Every search and research API was tested on its highest publicly-available compute tier. Parallel (Ultra8x), You(.)com Research (exhaustive), Tavily (pro), Exa (deep-reasoning), and Perplexity Deep Research (Opus 4.6). Valyu was run on Heavy mode; we offer a higher Max tier but did not use it here, since Max is more expensive per task than the field.

Valyu DeepResearch achieved 72.7% accuracy on the DRACO long-form research benchmark, outperforming Perplexity Deep Research (70.5%), Claude Opus 4.6 (59.8%), Gemini Deep Research (59.0%), and OpenAI Deep Research o3 (52.1%), while operating at less than half the cost of the next-best system.

Read more →
DRACO
Accuracy (%)
VALYU DEEPRESEARCH
72.7% / $2,500CPM
PERPLEXITY
CLAUDE 4.6
GEMINI
YOU.COM
OPENAI O3
PARALLEL
CLAUDE 4.5
OPENAI O4-MINI
TAVILY
EXA
Cost (CPM)
INTEGRATIONS

Works with your stack

View all integrations
npm install valyu-js
import { Valyu } from 'valyu-js';
 
const valyu = new Valyu("**************************");
 
const response = await valyu.search(
    "your query here"
);
 
// Access results
response.results.forEach(result => {
    console.log("Title:", result.title);
    console.log("URL:", result.url);
    console.log("Content:", result.content);
});
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