AWS Strands Agents SDK integrates Exa for AI-native web search in agent workflows

The new integration between Exa and AWS's Strands Agents SDK brings AI-native web search capabilities directly into agent workflows. Strands is an open-source, model-driven framework where developers define a system prompt, a model, and a list of tools, and the model autonomously decides which tools to call and in what order. Exa is a search engine built specifically for LLMs, understanding semantic meaning rather than keywords—so a query like 'startups building climate solutions' returns relevant startups even if the exact phrase is missing. The integration exposes two tools: exa_search, which performs semantic search with support for categories (news, research papers, repositories), domain filters, date ranges, and text content; and exa_get_contents, which retrieves full-page text from URLs with caching and optional live crawling for fresh content. Both return clean, structured data free of HTML, ads, or SEO noise, ready for direct consumption in an LLM's context window.

In practice, agents built with Strands can use these tools in a multi-step loop: the model might first call exa_search to find relevant articles, then call exa_get_contents on selected URLs to extract full details, then reason over the combined context to produce a final answer. The agent loop accumulates conversation history across iterations, enabling complex tasks like deep research, competitive intelligence gathering, or fact-checking. The SDK already ships with over 40 pre-built tools and supports Model Context Protocol (MCP), so adding Exa tools follows the same simple pattern: include them in the tools list. This eliminates the need for developers to build custom crawlers, parsers, and ranking logic to adapt general-purpose search results for agent use. As highlighted in the co-authored blog post (by Ishan Goswami and Nitya Sridhar from Exa), this integration specifically addresses the gap where most search APIs return HTML-heavy snippets optimized for humans, not structured data for agents.