Baseten MCP Server for LangChain 6 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Baseten 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({
"baseten": {
"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 Baseten, 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 Baseten MCP Server
Connect your Baseten account to any AI agent and track, deploy, and execute your machine learning models through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Baseten 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.
O que você pode fazer
- Model Management — List managed models, fetch configurations, and understand active routing boundaries
- Serverless Deployments — Inspect exact replica states, autoscaling configurations, and deployment versions
- Inference Execution — Run direct predictions (
predict) pushing tensor payloads or JSON directly to GPU weights - Workspace Secrets — Enumerate active environment secrets securely mapped inside the isolated orchestration ecosystem
Como funciona
1. Subscribe to this server
2. Enter your Baseten API Key
3. Gain complete ML-Ops control over your active inference nodes using Claude, Cursor, or your preferred agent
Scale unified AI infrastructure without bouncing between terminal windows. Your agent becomes a capable Machine Learning Operator tracking your GPU lifecycle.
Para quem é?
- ML Engineers — execute test payloads to deployments instantaneously without spinning up local Python notebooks
- DevOps/SREs — audit running deployment resources and verify replica states reliably from your core IDE
- AI Researchers — inspect version schemas and manage inference pipeline architectures quickly
The Baseten 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 Baseten to LangChain via MCP
Follow these steps to integrate the Baseten 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 Baseten via MCP
Why Use LangChain with the Baseten MCP Server
LangChain provides unique advantages when paired with Baseten through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Baseten 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 Baseten queries for multi-turn workflows
Baseten + LangChain Use Cases
Practical scenarios where LangChain combined with the Baseten MCP Server delivers measurable value.
RAG with live data: combine Baseten tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Baseten, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Baseten tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Baseten tool call, measure latency, and optimize your agent's performance
Baseten MCP Tools for LangChain (6)
These 6 tools become available when you connect Baseten to LangChain via MCP:
get_deployment
Get explicit details of a running deployment
get_model
Get a specific Baseten model
list_deployments
List active inferences bounds matching a specific model
list_models
List Baseten managed models
list_secrets
List securely managed workspace secrets without showing values
predict
Formulate the explicit tensor shapes or dictionaries strictly matching the deployed instance. Invoke a serverless model inference prediction
Example Prompts for Baseten in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Baseten immediately.
"List standard machine learning models we currently host on Baseten."
"Run a prediction against the Sentiment model ID 12345 using this text input: 'The new feature completely broke my workflow.'"
"Check if our Baseten project has a secret scoped as 'OPENAI_API_KEY_FALLBACK'."
Troubleshooting Baseten MCP Server with LangChain
Common issues when connecting Baseten to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersBaseten + LangChain FAQ
Common questions about integrating Baseten 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 Baseten 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 Baseten to LangChain
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.