Exa has shipped a Search API positioned as a search engine built specifically for AI agents. It is not just another search box but a tool that promises to make agents smarter through token-efficient search, deep research and structured results. In a world where AI agents increasingly need current information from the web, a specialised API like this becomes critical.
What happened
Exa introduced its Search API, built to give AI agents real-time search, crawling and research capability. The API offers several modes, including Search, Contents and Deep, letting agents pull both quick answers and deep results with structured output. The headline feature is token efficiency: Exa's specialised model is trained to extract the most relevant passages from the web, cutting the tokens passed to models by up to 90%. That materially lowers costs and speeds agents up.
The API also delivers high precision across verticals including companies, people and code, beating competitors on benchmarks such as FRAMES, Tip-of-Tongue and Seal0. Exa Instant returns results in under 180 ms. On top of that, Exa Connect provides access to leading data providers, and an enrichment feature extracts structured data on more than 70 million companies. Cognition, the company behind Devin, already uses Exa for its agents, noting that traditional search solutions did not cut it.
Why it matters
A search API purpose-built for AI agents is a meaningful step in the evolution of autonomous systems. Traditional search engines are not always well suited to programmatic use: they tend to return excess information that needs extra processing and burns a lot of tokens. Exa solves that by giving agents exactly the information they need in the most convenient and economical format. Agents end up more accurate, faster and cheaper to run.
Getting structured data and deep research through a single API greatly simplifies building complex AI agents that need current, verified information. The Exa Connect integration also opens access to a wide range of data, extending what agents can do in areas from financial analysis to market research. For developers that means less time spent parsing and filtering data and more time on agent logic.
What it means in practice
- Wire Exa into RAG systems: if you build agents that use Retrieval-Augmented Generation (RAG), take a look at the Exa Search API. Its token efficiency and precision can noticeably improve answer quality and cut operating costs.
- Use the structured data: for tasks that hinge on specific facts (company information, financial data), lean on Exa's enriched structured data extraction. It makes information easier for agents to process.
- Optimise for speed: if your agents need fast access to information, Exa Instant returns results in under 180 ms, which is critical for interactive applications.
- Broaden your data sources: explore Exa Connect for additional data sources that may help your agents, especially if they operate in specialised domains.
- Test and compare: benchmark Exa against other search solutions on your specific tasks to confirm the accuracy and token-efficiency gains. Feedback from Cognition (the makers of Devin) already points to substantial improvements.