IBM QRadar MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect IBM QRadar 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 MCP SERVER
Vinkius supports streamable HTTP and SSE.
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({
"ibm-qradar": {
"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 IBM QRadar, 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 IBM QRadar MCP Server
Connect IBM QRadar to any AI agent via MCP.
How to Connect IBM QRadar to LangChain via MCP
Follow these steps to integrate the IBM QRadar MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from IBM QRadar via MCP
Why Use LangChain with the IBM QRadar MCP Server
LangChain provides unique advantages when paired with IBM QRadar through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine IBM QRadar 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 IBM QRadar queries for multi-turn workflows
IBM QRadar + LangChain Use Cases
Practical scenarios where LangChain combined with the IBM QRadar MCP Server delivers measurable value.
RAG with live data: combine IBM QRadar tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query IBM QRadar, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain IBM QRadar tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every IBM QRadar tool call, measure latency, and optimize your agent's performance
IBM QRadar MCP Tools for LangChain (10)
These 10 tools become available when you connect IBM QRadar to LangChain via MCP:
execute_aql
Returns a search ID for async retrieval. Execute an Ariel Query Language (AQL) search
get_aql_results
Get results from a completed AQL search
get_aql_status
Get the status of an AQL search
get_log_sources
List QRadar log sources
get_network_hierarchy
List QRadar network hierarchy
get_offense_details
Get details for a specific QRadar offense
get_offenses
List QRadar offenses
get_reference_sets
). List QRadar reference sets
get_rules
List QRadar correlation rules
update_offense
Update a QRadar offense
Troubleshooting IBM QRadar MCP Server with LangChain
Common issues when connecting IBM QRadar to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersIBM QRadar + LangChain FAQ
Common questions about integrating IBM QRadar 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.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect IBM QRadar with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect IBM QRadar to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.