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Lusha MCP Server for LangChainGive LangChain instant access to 12 tools to Bulk Enrich Companies, Bulk Enrich Persons, Enrich Company Info, and more

Built by Vinkius GDPR 12 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Lusha through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Ask AI about this App Connector for LangChain

The Lusha app connector for LangChain is a standout in the Industry Titans category — giving your AI agent 12 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "lusha-alternative": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Lusha, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())
Lusha
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Lusha MCP Server

Connect your Lusha account to any AI agent and take full control of your sales prospecting and data enrichment through natural conversation. Lusha provides a premier B2B database, and this integration allows you to retrieve high-fidelity contact details (email, phone), enrich company metadata, and search for new prospects directly from your chat interface.

LangChain's ecosystem of 500+ components combines seamlessly with Lusha through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

  • Contact & Person Enrichment — Lookup detailed contact metadata programmatically using name, company, or LinkedIn URLs to ensure your CRM is always synchronized.
  • Company & Firmographic Intelligence — Access and monitor company data including industry, revenue, and headcount directly from the AI interface to qualify accounts in real-time.
  • Prospecting & Search Control — Search for new contacts and companies matching your Ideal Customer Profile (ICP) via natural language to drive better sales efficiency.
  • Usage & Credit Oversight — Access granular details for your credit consumption and remaining balance using simple AI commands to maintain a clear overview of your resources.
  • Operational Monitoring — Track system responses and manage data ingestion to ensure your sales workflows are always optimized.

The Lusha MCP Server exposes 12 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 12 Lusha tools available for LangChain

When LangChain connects to Lusha through Vinkius, your AI agent gets direct access to every tool listed below — spanning b2b-intelligence, data-enrichment, prospecting, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

bulk_enrich_companies

Enrich multiple companies

bulk_enrich_persons

Enrich multiple contacts

enrich_company_info

Get firmographics

enrich_person_info

Get contact details

get_account_info

Check connection

get_credit_balance

Check account balance

get_person_by_email

Enrich by email

get_person_by_linkedin

Enrich by LinkedIn

get_usage_stats

Check API usage

prospect_new_companies

Search for businesses

prospect_new_leads

Search for contacts

test_lusha_auth

Verify API key

Connect Lusha to LangChain via MCP

Follow these steps to wire Lusha into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token
03

Run the agent

Save the code and run python agent.py
04

Explore tools

The agent discovers 12 tools from Lusha via MCP

Why Use LangChain with the Lusha MCP Server

LangChain provides unique advantages when paired with Lusha through the Model Context Protocol.

01

The largest ecosystem of integrations, chains, and agents. combine Lusha MCP tools with 500+ LangChain components

02

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

03

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

04

Memory and conversation persistence let agents maintain context across Lusha queries for multi-turn workflows

Lusha + LangChain Use Cases

Practical scenarios where LangChain combined with the Lusha MCP Server delivers measurable value.

01

RAG with live data: combine Lusha tool results with vector store retrievals for answers grounded in both real-time and historical data

02

Autonomous research agents: LangChain agents query Lusha, synthesize findings, and generate comprehensive research reports

03

Multi-tool orchestration: chain Lusha tools with web scrapers, databases, and calculators in a single agent run

04

Production monitoring: use LangSmith to trace every Lusha tool call, measure latency, and optimize your agent's performance

Example Prompts for Lusha in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Lusha immediately.

01

"Enrich this contact: John Miller at Acme Corp."

02

"Search for companies in New York with 500-1000 employees in the SaaS industry."

03

"Check my Lusha credit balance."

Troubleshooting Lusha MCP Server with LangChain

Common issues when connecting Lusha to LangChain through the Vinkius, and how to resolve them.

01

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Lusha + LangChain FAQ

Common questions about integrating Lusha MCP Server with LangChain.

01

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.
02

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.
03

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.