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1

Get your free Valyu API key

Head to the Valyu Platform and sign in (or create an account). Then, copy one of your API keys from your dashboard.

Get your free API key

You get over 1000 free query retrievals. No credit card required.
Join the Valyu community on Discord for hands-on setup help and release previews.
2

Install Valyu

Install the Valyu SDK in your language of choice.
pip install valyu
3

Start searching with Valyu

Run your first Valyu DeepSearch in 4 lines of code. Simply replace the API key in this snippet with your own:
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search("What is quantum computing?")

for result in response.results:
    print(f"Title: {result.title}")
    print(f"Content preview: {result.content[:200]}...")
    print(f"URL: {result.url}")
Improve or guardrail your search results with more advanced parameters:
from valyu import Valyu

valyu = Valyu(api_key="your-valyu-api-key")
response = valyu.search(
    "Implementation details of agentic search-enhanced large reasoning models",
    search_type="proprietary",
    max_num_results=10,
    max_price=30,
    relevance_threshold=0.5,
    category="agentic retrieval-augmented generation",
    included_sources=["valyu/valyu-arxiv"],
    is_tool_call=True
)

for result in response.results:
    print(f" Title: {result.title}")
    print(f" URL: {result.url}")
    print(f" Content Preview: {result.content[:300]}...")
To learn more about advanced features, see the Search Guide and the DeepSearch API reference.

​
Content Extraction

Turn any web page into clean markdown (or structured data) using the Contents API:
from valyu import Valyu

valyu = Valyu() # Uses VALYU_API_KEY from env

data = valyu.contents(
    urls=[
        "https://techcrunch.com/category/artificial-intelligence/",
    ],
    response_length="medium",
    extract_effort="auto",
)
print(data["results"][0]["content"][:500]) # preview first 500 chars
For structured extraction with JSON Schemas or AI summaries, see the Content Extraction Guide and the Contents API reference.

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AI-Powered Answers

Get intelligent responses that combine real-time search with AI processing using the Answer API:
from valyu import Valyu

valyu = Valyu() # Uses VALYU_API_KEY from env

data = valyu.answer(
    query="latest developments in quantum computing",
)
print(data["contents"]) # AI-generated response
For structured responses with JSON schemas or custom AI instructions, see the Answer API Guide and the Answer API reference.

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Use Cases

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Academic Research

Access millions of research papers and academic content with full-text search: 
response = valyu.search(
    "Extending context window of large language models via positional interpolation",
    search_type="proprietary",
    max_num_results=5,
    max_price=30,
    included_sources=["valyu/valyu-arxiv", "valyu/valyu-pubmed"]
)

# Print academic paper details with key fields

for result in response.results:
    print(f" Title: {result.title}")
    print(f" Authors: {', '.join(result.authors) if hasattr(result, 'authors') else 'N/A'}")
    print(f" Publication Date: {getattr(result, 'publication_date', 'N/A')}")
    print(f" DOI: {getattr(result, 'doi', 'N/A')}")
    print(f" Citation: {getattr(result, 'citation', 'N/A')}")
    print(f" Citation Count: {getattr(result, 'citation_count', 'N/A')}")
    print(f" Content Preview: {result.content[:200]}...")
Response includes rich academic content: 
{
  "success": true,
  "error": "",
  "tx_id": "tx_55e65c6f-3607-4ebe-892b-e964b9c72a8d",
  "query": "Extending context window of large language models via positional interpolation",
  "results": [
    {
      "id": "55e65c6f-3607-4ebe-892b-e964b9c72a8d:2306.15595:1",
      "title": "Extending Context Window of Large Language Models via Positional Interpolation",
      "url": "https://arxiv.org/abs/2306.15595?utm_source=valyu.network&utm_medium=referral",
      "content": "#### 2.3 PROPOSED APPROACH: POSITION INTERPOLATION (PI)\n\n#### 2.1 BACKGROUND: ROTARY POSITION EMBEDDING (ROPE)\n\nTransformer models require explicit positional information to be injected, typically in the form of positional encodings, to represent the order of inputs. We consider Rotary Position Embedding (RoPE) (Su et al., 2021), which is the position encoding used in the LLaMA model...\n\n$$\\mathbf{f}(\\mathbf{x},m)=[(x_{0}+\\mathrm{i}x_{1})e^{im\\theta_{0}},(x_{2}+\\mathrm{i}x_{3})e^{im\\theta_{1}},\\ldots,(x_{d-2}+\\mathrm{i}x_{d-1})e^{im\\theta_{d/2-1}}]^{\\top}$$",
      "source": "valyu/valyu-arxiv",
      "length": 593,
      "image_url": {
        "_page_4_Figure_2.jpeg": "https://prod-s3-vyplatform-processeddata.s3.amazonaws.com/valyu/Arxiv-new/eb030308-a71b-5227-8ddf-9dbeeb1c6e12/_page_4_Figure_2.jpeg"
      },
      "publication_date": "2023-01-01",
      "doi": "https://doi.org/10.48550/arxiv.2306.15595",
      "citation": "Shouyuan Chen et al. (2023). Extending Context Window of Large Language Models via Positional Interpolation.",
      "citation_count": 25,
      "authors": [
        "Shouyuan Chen",
        "S.H. Wong",
        "Liangjian Chen",
        "Yuandong Tian"
      ],
      "price": 0.0005,
      "data_type": "unstructured",
      "source_type": "paper",
      "relevance_score": 0.8071867796187081
    }
  ],
  "results_by_source": {
    "proprietary": 1,
    "web": 0
  }
}

​
Financial Market Data

Want financial data? Just ask in plain English - we’ll figure it out: 
response = valyu.search(
    "Pfizer stock price since COVID-19 outbreak",
    search_type="proprietary",
    max_num_results=1,
    max_price=30 # Maximum price for a thousand retreivals (CPM)
)

for result in response.results:
    print(f" Title: {result.title}")
    print(f" Content Preview: {str(result.content)[:300]}...") # For structured financial data, show sample data points
    if result.data_type == "structured" and isinstance(result.content, list):
        print(f" Sample Data Points: {len(result.content)} entries")
        for item in range(0, 3):
            print(f" {result.content[item]}")
Response looks like this: 
{
    "success": true,
    "error": "",
    "tx_id": "tx_6a2568cf-a3f4-4860-9b6e-96b35e398b7f",
    "query": "Price of TSLA today?",
    "results": [
      {
        "title": "Price of PFE every 1mo between 2020-01-01 00:00 and 2025-05-26 00:00",
        "url": "https://platform.valyu.network/data-sources/valyu/valyu-stocks-US/characteristics",
        "content": [
          {
              "datetime": "2025-05-01 04:00:00",
              "open": 24,
              "high": 24,
              "low": 22,
              "close": 23,
              "volume": 78776482
          },
          {
            ...
          }
        ],
        "source": "valyu/valyu-stocks-US",
        "price": 0.006,
        "length": 8175,
        "image_url": null,
        "data_type": "structured",
        "source_type": "data",
        "relevance_score": 0.6277149030411012
      }
    ],
    "results_by_source": {
        "proprietary": 1,
        "web": 0
    }
}
The search service can interpret noisy queries such as ”$$$$$ of larry and Sergey brins companie. on fr week commencing 5th hune on the 21st century” (which maps to “Google stock price for Friday June 5th, 2021”).

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Next Steps

Python SDK

Detailed Python SDK documentation

TypeScript SDK

Detailed TypeScript SDK documentation

API Reference

Full API specification

Tips and Tricks

How to get the best results from the Valyu API

Prompting Guide

Learn how to craft effective prompts for optimal search results

MCP Integration

Power your MCP agents with enriched context
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