Valyu is a high-performance search and research infrastructure built specifically for AI agents, developers, and enterprises. Founded at University College London (UCL) by a team of experienced researchers and engineers, Valyu provides a sophisticated alternative to general-purpose search APIs, offering deep, structured access to a vast array of web and proprietary data sources. The platform is designed to handle the complex, high-scale, and accuracy-dependent nature of modern AI knowledge work, enabling developers to build smarter agents that can reliably process and synthesize information across diverse professional domains.

Functionality centers on providing a unified API that supports real-time web search, content extraction, and advanced deep research tasks. Unlike traditional search tools that primarily return links, Valyu delivers clean, AI-ready data—including full-text content, structured fields, and synthesized reports—that can be fed directly into LLM contexts and agent workflows. The platform's proprietary retrieval infrastructure is optimized for classes of data critical to professional environments, such as SEC filings, medical literature, patent databases, financial market data, and economic indicators.

Some of the key features are:

• DeepResearch API: An AI-native research engine that performs multi-step, citation-grounded research across specialized datasets to generate professional deliverables like PDF, DOCX, XLSX, and PPTX reports.
• Proprietary Data Access: Direct access to 50+ specialized data sources, including arXiv, PubMed, SEC filings, clinical trial registries, financial market data, and legal records, with structural access to content sections.
• Agent-Native Outputs: Provides data in formats ready for immediate agent consumption, such as clean markdown, structured JSON, and synthesized summaries, eliminating the need for extensive post-processing.
• Configurable Workflows: Offers human-in-the-loop checkpoints, custom output schemas, and multi-modal support, allowing users to tune verification rigor and research strategy per task.
• Sandboxed Execution: Supports sandboxed code execution for data cleaning, claim recomputation, and dynamic chart generation directly from retrieved primary-source data.
• Enterprise Integration: Features SOC2 compliance, SSO, zero-data retention policies, and support for large-scale production workloads with dedicated engineering assistance.
• Benchmark Performance: Validated via independent benchmarks like DRACO, demonstrating state-of-the-art accuracy in long-form research synthesis at a highly competitive price point.

Operationally, Valyu functions as an abstraction layer for agents. Developers connect their applications via Python or JavaScript SDKs, or through an MCP-compatible server to existing agent frameworks. By submitting queries in natural language, the API performs intelligent routing to the most relevant data providers, retrieves high-fidelity content, and provides structured responses. For deeper tasks, the DeepResearch engine operates asynchronously, planning and executing complex multi-step research before delivering structured findings via webhooks or direct notification systems.

Some common use cases include:

• Investment Due Diligence: Automating the creation of investment memos and financial models by pulling data from SEC filings, earnings transcripts, and market data.
• Biomedical Research: Synthesizing literature reviews from sources like PubMed and ClinicalTrials.gov for drug pipeline tracking and competitive intelligence.
• Patent and R&D Analysis: Performing technology scouting and freedom-to-operate reviews by cross-referencing patent filings with academic papers and global market news.
• Regulatory Monitoring: Continuously tracking updates from legislative bodies and regulatory agencies to map impacts on specific internal assets and portfolios.
• Agent-Based Knowledge Work: Powering autonomous agents that require reliable, citation-backed primary source retrieval to function in professional environments.