How Exa built a Web Research Multi-Agent System with LangGraph and LangSmith

Exa has launched a groundbreaking deep research agent that autonomously explores the web to deliver structured information quickly, leveraging a sophisticated multi-agent system built on LangGraph. The company evolved from a basic search API to this advanced agentic search system, reflecting an industry trend toward more complex, long-running applications. Exa's architecture features dynamic task generation and intentional context engineering, allowing tasks to use specialized tools and reasoning while maintaining structured JSON output for API consumption. This approach, inspired by Anthropic's Deep Research system, optimizes token usage by reasoning first on search snippets rather than full content, enhancing efficiency and preserving research quality. By utilizing LangGraph for coordination and LangSmith for observability, particularly in tracking token usage, Exa ensures cost-effective performance at scale, providing a model for building robust, production-ready agentic systems that prioritize structured, reliable outputs for diverse applications.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.