Ragas MCP Server for LangChain 7 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Ragas 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({
"ragas": {
"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 Ragas, 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 Ragas MCP Server
Integrate Ragas with your AI agent to bring professional grade RAG (Retrieval-Augmented Generation) evaluation and tracking into your chat interface. By subscribing to this server, the AI can seamlessly manage datasets and measure LLM performance on demand.
LangChain's ecosystem of 500+ components combines seamlessly with Ragas through native MCP adapters. Connect 7 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
- Dataset Management — Upload, list, and organize evaluation datasets directly inside your environment.
- Run Evaluations — Automatically trigger Ragas evaluations on your RAG pipelines and fetch detailed scoring.
- Track Experiments — Monitor and compare iterative improvements by viewing tracked metrics across different agent versions.
- Project Organization — Associate evaluations with specific projects within your Ragas dashboard.
The Ragas MCP Server exposes 7 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 Ragas to LangChain via MCP
Follow these steps to integrate the Ragas 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 7 tools from Ragas via MCP
Why Use LangChain with the Ragas MCP Server
LangChain provides unique advantages when paired with Ragas through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Ragas 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 Ragas queries for multi-turn workflows
Ragas + LangChain Use Cases
Practical scenarios where LangChain combined with the Ragas MCP Server delivers measurable value.
RAG with live data: combine Ragas tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Ragas, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Ragas tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Ragas tool call, measure latency, and optimize your agent's performance
Ragas MCP Tools for LangChain (7)
These 7 tools become available when you connect Ragas to LangChain via MCP:
get_dataset
Retrieves details for a specific evaluation dataset
get_experiment
Retrieves detailed information for a specific experiment
get_results
Retrieves the results of a completed experiment
list_datasets
Lists available evaluation datasets
list_experiments
Lists experiments associated with a specific dataset
list_metrics
Lists all available evaluation metrics
run_evaluation
g., faithfulness, answer_relevancy). Triggers a new evaluation run for a dataset
Example Prompts for Ragas in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Ragas immediately.
"List all Ragas datasets available in my project."
"Fetch the metrics and results for the recent experiment 'Support Bot V3'."
"Create a new Ragas project named 'Financial_RAG_Testing'."
Troubleshooting Ragas MCP Server with LangChain
Common issues when connecting Ragas to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersRagas + LangChain FAQ
Common questions about integrating Ragas 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 Ragas 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 Ragas to LangChain
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.