InfoVetted MCP Server for LangChainGive LangChain instant access to 12 tools to Cancel Active Vetting, Check Api Connectivity, Create Contact Group, and more
LangChain is the leading Python framework for composable LLM applications. Connect InfoVetted 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 InfoVetted app connector for LangChain is a standout in the Human Resources 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
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({
"infovetted": {
"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 InfoVetted, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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 InfoVetted MCP Server
Connect your InfoVetted account to any AI agent and manage background checks through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with InfoVetted 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
- Vetting Requests — List all vetting requests, create new background checks, check status, and cancel active vettings
- Screening Contacts — Manage contacts for screening with full profile data, create new screening contacts, and inspect individual records
- Package Management — Browse available vetting packages and their included checks
- Result Tracking — Monitor check results with pass/fail status and compliance details
- Activity History — View submission and completion timelines
The InfoVetted 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 InfoVetted tools available for LangChain
When LangChain connects to InfoVetted through Vinkius, your AI agent gets direct access to every tool listed below — spanning background-screening, identity-verification, employment-checks, 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.
Cancel a background check
Verify InfoVetted API status
g., "Engineering Team"). Create a new organization group
Initiate a background check
Add a new individual for screening
Get details for a specific individual
Check status of a vetting process
List active webhooks
List organizational contact groups
List individuals being screened
). List available background check types
List all background check requests
Connect InfoVetted to LangChain via MCP
Follow these steps to wire InfoVetted into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the InfoVetted MCP Server
LangChain provides unique advantages when paired with InfoVetted through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine InfoVetted MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across InfoVetted queries for multi-turn workflows
InfoVetted + LangChain Use Cases
Practical scenarios where LangChain combined with the InfoVetted MCP Server delivers measurable value.
RAG with live data: combine InfoVetted tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query InfoVetted, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain InfoVetted tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every InfoVetted tool call, measure latency, and optimize your agent's performance
Example Prompts for InfoVetted in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with InfoVetted immediately.
"Show all active vetting requests and create a new background check for a candidate."
"Check the status of Maria Silva's background check and list all screening contacts."
"Show completed vetting results and cancel the pending check for candidate #3."
Troubleshooting InfoVetted MCP Server with LangChain
Common issues when connecting InfoVetted to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersInfoVetted + LangChain FAQ
Common questions about integrating InfoVetted MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.