What URL should I use for the R2R API URL?

If you are running R2R locally via Docker, it's typically `http://localhost:7272`. If you are using SciPhi Cloud or have it deployed on your own infrastructure, provide the exact public or private endpoint.

Do I need an R2R API Key?

It depends on your deployment. Open deployments for local testing may not require a key. Production deployments or SciPhi Cloud environments require you to provide the generated key.

What is the difference between RAG and Search?

The `search` tool issues a standard vector similarity match—it returns relevant raw snippets from your database. The `rag_query` tool asks the R2R server to perform the search and compute an intelligent answer wrapping those snippets using an LLM.

Are document ingestions possible via chat?

No. This integration is designed for observational toolsets (listing documents, inspecting states, querying the index). Heavy ingestions of PDFs or websites should be handled through scripts or the dashboard.

R2R MCP Server for LangChain 6 tools — connect in under 2 minutes

Name: R2R
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 6 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect R2R through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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({
        "r2r": {
            "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 R2R, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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 R2R MCP Server

Connect your R2R (Rag to Riches) deployment to an AI agent, bringing your RAG infrastructure inside your chat interface. By linking this server, the AI can query its own constructed knowledge base on demand.

LangChain's ecosystem of 500+ components combines seamlessly with R2R 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

Vector Search — Perform semantic similarity queries across your document database to retrieve contextually relevant chunks of information.
Execute RAG Queries — Use the 'rag_query' endpoint to have the R2R server directly summarize information based on vector data.
Knowledge Management — Call the API to list ingested documents, read metadata attributes, and filter logical collections.
Instance Health Monitoring — Quickly ping the connection using health checks to verify your system is responsive.

The R2R 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 R2R to LangChain via MCP

Follow these steps to integrate the R2R 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 R2R via MCP

Why Use LangChain with the R2R MCP Server

LangChain provides unique advantages when paired with R2R through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine R2R 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 R2R queries for multi-turn workflows

R2R + LangChain Use Cases

Practical scenarios where LangChain combined with the R2R MCP Server delivers measurable value.

RAG with live data: combine R2R tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query R2R, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain R2R tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every R2R tool call, measure latency, and optimize your agent's performance

R2R MCP Tools for LangChain (6)

These 6 tools become available when you connect R2R to LangChain via MCP:

get_document

Retrieves details for a specific document

get_health

Checks the health status of the R2R server

list_collections

Lists all document collections

list_documents

Lists all ingested documents in the R2R system

rag_query

Executes a RAG (Retrieval-Augmented Generation) query

search

Performs a vector search across ingested documents

Example Prompts for R2R in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with R2R immediately.

"Perform a vector search for 'Company Holiday Policy 2026'."

"Query the RAG engine to summarize known advanced RAG chunking strategies."

"Verify the operational health of the R2R server."

Troubleshooting R2R MCP Server with LangChain

Common issues when connecting R2R to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

R2R + LangChain FAQ

Common questions about integrating R2R MCP Server with LangChain.

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.

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.