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Valyu

What it is

Valyu is an AI-native search API that provides agents with access to both the open web and licensed, high-signal proprietary data sources.

What problem it solves

It allows agents to search beyond just the current web, providing structured, high-accuracy results from datasets like PubMed, SEC filings, clinical trials, patents, arXiv, and financial data through a single, natural-language-enabled API.

Where it fits in the stack

AI Assistants & Knowledge / Understand (Aggregators). It acts as a high-signal search engine that feeds real-time context and deep research data to LLMs and agents.

Typical use cases

  • Deep Research: Running complex queries that require cross-referencing web search with research papers (arXiv) or patents.
  • Financial Analysis: Extracting real-time market data or historical SEC filings.
  • Medical/Scientific Agents: Searching PubMed or clinical trials for verified medical information.
  • RAG Enrichment: Feeding high-fidelity, citation-backed data into retrieval-augmented generation pipelines.

Technical Capabilities

  • Search API: Core semantic search across 36+ proprietary sources and the open web.
  • Answer API: Returns AI-synthesized answers grounded in search results with inline citations.
  • Deep Research API: Performs multi-step, autonomous research plans and returns structured reports.
  • Content API: High-quality Markdown extraction and structured data parsing from URLs.

Strengths

  • Unified API: Access to licensed repositories (PubMed, SEC, Wiley) in a single request.
  • Agent-Ready: Returns structured, LLM-ready data rather than just a list of links.
  • High Recall: Accesses "dark data" not indexable by standard search bots.
  • Citations: Native support for source attribution in the Answer and Deep Research endpoints.

Limitations

  • Paid Service: Requires an API key and usage-based pricing.
  • Latency: Searching proprietary databases can sometimes be slower than simple web-index searches.
  • Closed-Source: The search engine itself is a proprietary service.

When to use it

  • When an agent needs high-accuracy, verified data from scientific, financial, or legal sources.
  • For building specialized agents (e.g., a "Scientific Research Agent") that require more than just web results.

When not to use it

  • For general, low-stakes web search where free or cheaper alternatives suffice.
  • If you require a fully open-source, self-hosted search index.

Implementation: Cross-Source Answer API

The following example demonstrates using the Answer API to synthesize findings across scientific literature and regulatory filings.

from valyu import Valyu

# Initialize the client
client = Valyu(api_key="your-api-key")

# Perform a grounded answer query across specific proprietary sources
response = client.answer(
    query="Analyze the impact of GLP-1 agonists on healthcare provider stock volatility in 2024",
    included_sources=["valyu/valyu-pubmed", "valyu/valyu-sec-filings"],
    summary_instructions="Provide a structured analysis with citations from both medical and financial sources.",
    response_length="large"
)

print(f"Answer: {response.answer}")
for citation in response.citations:
    print(f"[{citation.id}] {citation.title} ({citation.url})")

Implementation: Deep Research Pattern

For long-horizon tasks, use the Deep Research API to generate comprehensive reports.

# Deep Research for a specific market landscape
report = client.deep_research(
    query="Future of solid-state battery manufacturing: key players, patent landscape, and supply chain risks",
    output_format="markdown",
    max_steps=10
)

# Save the generated research report
with open("solid_state_research.md", "w") as f:
    f.write(report.content)

Licensing and cost

  • Open Source: No
  • Cost: Paid (Usage-based pricing with free tier)
  • Self-hostable: No

Sources / References

Contribution Metadata

  • Last reviewed: 2026-05-16
  • Confidence: high