How to Use the Exa AI MCP in LangChain

Build complex reasoning chains using Exa AI search tools directly within your LangChain agents.

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Connect Exa AI MCP to LangChain

Create your Vinkius account to connect Exa AI to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Exa AI with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Exa AI tools for multi-step reasoning

Feed the output of a `semantic_search` directly into a subsequent analysis step. Your agent decides the sequence based on real-time data, not static logic. This creates a pipeline where `keyword_search` results inform your next action. You get full visibility into every step through your standard tracing tools.

Native integration for the Exa AI MCP Server

Initialize the server and pass the tool definitions into your agent constructor. The adapter handles the schema mapping so your chain stays clean. Everything happens in your code. You define the logic, and the tools bridge your agent to the web using `search_with_contents` or `extract_page_content`.

Build agents that query the web dynamically

Connect your agent to the live web using `perform_live_crawl` when your pipeline hits a knowledge gap. It fetches fresh data without manual intervention. Use `advanced_custom_search` to narrow the focus of your chain. Your agent pulls exactly what it needs to complete the next link in the workflow.

Setup guide

Set up Exa AI MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Exa AI tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "exa-ai-alternative-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Exa AI transactions"
    })
    print(result["messages"][-1].content)

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Real-time monitoring

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place for every integration

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Common questions about Exa AI MCP in LangChain

You instantiate the MCP client and pass the tools to your agent. From there, you simply prompt the agent to use the semantic_search tool.

Yes. You can aggregate tools from multiple servers into a single agent. This allows your LangChain pipeline to query web data alongside your private database.

It is stateless by default. Use the client session if you need to maintain context across multiple turns in your conversation.

It doesn't. You provide your key via your local environment variables. The server simply uses it to authenticate your requests to the search backend.

Your search queries and retrieved page text remain within your local execution environment. Only the necessary requests to perform the search leave your system.

Use it with your favorite AI tools

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