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
Related tools / concepts¶
Sources / References¶
Contribution Metadata¶
- Last reviewed: 2026-02-27
- Confidence: high