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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.

Strengths

  • Unified API: Access to 36+ proprietary data sources in a single query.
  • Agent-Ready: Returns structured, LLM-ready data rather than just links.
  • Multimodal: Supports multimodal retrieval for deep-content extraction.
  • Alternative to Tavily/Exa: Provides a broader data scope beyond standard web search.

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.

Licensing and cost

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

Sources / References

Contribution Metadata

  • Last reviewed: 2026-02-27
  • Confidence: high