Exa vs Linkup (2026): Semantic Search vs Real-Time Web for Agents

How they actually differ

Exa and Linkup are both search APIs aimed at AI agents and RAG systems, but they prioritize different things and you can feel it on the first ten queries you run.

Exa is an AI-native search engine built on embedding-based retrieval rather than classic keyword matching. The pitch is semantic understanding: "find me content like this" works better than "find me content matching these keywords." Strong for research-style queries where the user does not know the exact terminology, for "similar to" queries, and for content that lives on the long tail of the web. The trade-off is that for direct factual lookups (specific company news, exact dates, named entities) keyword-style search often returns more accurate top results than semantic matching.

Linkup is a real-time web search API with a focus on fresh, structured results designed to be fed straight into an LLM prompt. The strength is recency and clean output. Linkup's response format tends to play well with RAG pipelines that want short, structured snippets rather than full pages. If your agent is doing live research and you care about freshness more than semantic discovery, Linkup tends to feel snappier in production.

Where the trade-off bites: Exa is the right pick when your queries are open-ended research, semantic similarity, or long-tail discovery. Linkup is the right pick when your agent does live web search as part of an answer pipeline and needs fresh, well-formed snippets. Many production RAG systems end up using both behind a router that picks based on query intent.

Where Respan fits. Both APIs are HTTP endpoints that fit naturally into an agent's tool list. With Respan tracing, calls to either show up in the trace tree alongside the LLM steps, so you can see exactly which search returned which chunks for each user query. See RAG observability for the broader telemetry pattern and LLM workflows and tracing for the trace shape.