Search and Retrieval APIs
Use Exa with LangGraph
Exa connects to LangGraph through a official adapter package path (langchain-exa ExaSearchResults and ExaFindSimilarResults used with a LangGraph agent). This page records what that connection looks like and links the evidence behind it.
Pairing facts
Why this pairing
A search API for AI: natural-language queries over an index built for agents, with content returned in model-ready shapes.
Best for: Agents that need relevant evidence quickly, sized for the context window Research steps that benefit from deeper search modes with synthesis Pipelines that want structured JSON answers with sources attached
Not ideal for: Self-hosted stacks with no external API dependency Workloads that mostly fetch known URLs rather than discover sources
Configuration
tools = [ExaSearchResults(), ExaFindSimilarResults()]Source: https://docs.langchain.com/oss/python/integrations/tools/exa_search. Examples use placeholders only.
Evidence
The LangChain Exa search page states the Exa tools can be used with a LangGraph agent to dynamically search and find similar content.
https://docs.langchain.com/oss/python/integrations/tools/exa_search
Compatibility here means the cited page documents the pairing. It does not mean AgentsUse benchmarked the combination or that every version works unchanged. Pin versions, run the tool on a small job first, and review the first outputs before widening access.
Watch for
- Search does not support pagination; deep modes add seconds of latency per call.
- Requesting both highlights and full text bills two content views of the same page.