Orkes Conductor MCP Server for LangChain 6 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Orkes Conductor 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({
"orkes-conductor": {
"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 Orkes Conductor, 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 Orkes Conductor MCP Server
Connect your Orkes Conductor cluster to any AI agent and get full visibility into your workflow orchestration layer — definitions, running instances, task states, and execution history.
LangChain's ecosystem of 500+ components combines seamlessly with Orkes Conductor through native MCP adapters. Connect 6 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
- Workflow Definitions — List all registered workflow definitions with versions and descriptions, or inspect a specific workflow's graph schema with tasks, operators, and branching logic
- Task Definitions — List all registered task definitions available for orchestration within your workflows
- Running Instances — List actively running workflow instances filtered by workflow name to monitor what's currently executing
- Execution Details — Get deep state details for any workflow execution including input/output mappings, task-by-task trace histories, and exceptions
- Workflow Search — Search across all workflow executions using Elasticsearch queries, filtering by status, correlation ID, or workflow type
The Orkes Conductor MCP Server exposes 6 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.
How to Connect Orkes Conductor to LangChain via MCP
Follow these steps to integrate the Orkes Conductor 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 6 tools from Orkes Conductor via MCP
Why Use LangChain with the Orkes Conductor MCP Server
LangChain provides unique advantages when paired with Orkes Conductor through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Orkes Conductor 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 Orkes Conductor queries for multi-turn workflows
Orkes Conductor + LangChain Use Cases
Practical scenarios where LangChain combined with the Orkes Conductor MCP Server delivers measurable value.
RAG with live data: combine Orkes Conductor tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Orkes Conductor, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Orkes Conductor tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Orkes Conductor tool call, measure latency, and optimize your agent's performance
Orkes Conductor MCP Tools for LangChain (6)
These 6 tools become available when you connect Orkes Conductor to LangChain via MCP:
get_execution
Get deep state details of a specific Workflow Execution
get_workflow_def
Get a specific Workflow Definition explicitly by name
list_running
List active, running workflow instances by explicit workflow name
list_task_defs
List all explicitly registered Task Definitions via Conductor API
list_workflow_defs
List all registered overarching Workflow Definitions via Orkes API
search_workflows
Perform an elastic Search across all Workflow executions
Example Prompts for Orkes Conductor in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Orkes Conductor immediately.
"Show me all registered workflow definitions."
"Are there any failed workflows in the last 24 hours?"
"How many instances of the order-processing workflow are currently running?"
Troubleshooting Orkes Conductor MCP Server with LangChain
Common issues when connecting Orkes Conductor to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersOrkes Conductor + LangChain FAQ
Common questions about integrating Orkes Conductor 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 Orkes Conductor 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 Orkes Conductor to LangChain
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.