Anyscale MCP Server for LangChain 7 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Anyscale 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({
"anyscale": {
"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 Anyscale, 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 Anyscale MCP Server
Connect your Anyscale environment to your AI agent and manage both AI inference and backend scalable infrastructure natively through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Anyscale 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
- Model Discovery and Querying — List all active foundational models inside your environment and send conversational or zero-shot instruct prompts
- Embeddings Pipeline — Generate semantic vector embeddings for arrays of text inputs directly in-flight
- Services Fleet — Monitor deployed Ray services, fetch cluster states, and map live service endpoint configurations
- Cluster Jobs — Query Ray batch jobs to inspect recent execution statuses and training metrics right from your terminal
The Anyscale 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 Anyscale to LangChain via MCP
Follow these steps to integrate the Anyscale 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 Anyscale via MCP
Why Use LangChain with the Anyscale MCP Server
LangChain provides unique advantages when paired with Anyscale through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Anyscale 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 Anyscale queries for multi-turn workflows
Anyscale + LangChain Use Cases
Practical scenarios where LangChain combined with the Anyscale MCP Server delivers measurable value.
RAG with live data: combine Anyscale tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Anyscale, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Anyscale tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Anyscale tool call, measure latency, and optimize your agent's performance
Anyscale MCP Tools for LangChain (7)
These 7 tools become available when you connect Anyscale to LangChain via MCP:
chat_completion
Pass an array of messages with roles (user, assistant, system). Generate conversational responses via Anyscale LLMs
generate_embeddings
Generate semantic vector embeddings for text
get_service
Retrieve details about a specific Anyscale service
list_jobs
List Anyscale batch or training jobs
list_models
g., meta-llama/Llama-2-70b-chat-hf). List available AI models on Anyscale Endpoints
list_services
List Anyscale deployed services
text_completion
Use for foundational instruct generation. Generate text completion using Anyscale generic completion API
Example Prompts for Anyscale in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Anyscale immediately.
"List all active models from my Anyscale cluster."
"Check the status of our latest training jobs."
"Generate vector embeddings for the text: 'The sun rises in the east and sets in the west'"
Troubleshooting Anyscale MCP Server with LangChain
Common issues when connecting Anyscale to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersAnyscale + LangChain FAQ
Common questions about integrating Anyscale 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 Anyscale 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 Anyscale to LangChain
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.