Portkey MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Portkey through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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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({
"portkey": {
"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 Portkey, 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 Portkey MCP Server
What you can do
Connect AI agents to the Portkey AI Gateway for enterprise-grade observability and management:
LangChain's ecosystem of 500+ components combines seamlessly with Portkey through native MCP adapters. Connect 10 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.
- Monitor logs and traces of all LLM calls passing through your gateway
- Analyze token usage, latency, and costs across models and teams
- Submit feedback (Likes/Dislikes) to improve model quality and agent performance
- Export logs for audit trails, compliance, and offline cost analysis
- Review gateway configurations including retry policies, fallbacks, and cache settings
- Manage virtual keys to track provider API key usage and limits
- Discover supported models from 1,600+ LLMs available via Portkey
- Enforce budget policies to prevent runaway AI costs per team or project
The Portkey MCP Server exposes 10 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 Portkey to LangChain via MCP
Follow these steps to integrate the Portkey 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 10 tools from Portkey via MCP
Why Use LangChain with the Portkey MCP Server
LangChain provides unique advantages when paired with Portkey through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Portkey 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 Portkey queries for multi-turn workflows
Portkey + LangChain Use Cases
Practical scenarios where LangChain combined with the Portkey MCP Server delivers measurable value.
RAG with live data: combine Portkey tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Portkey, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Portkey tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Portkey tool call, measure latency, and optimize your agent's performance
Portkey MCP Tools for LangChain (10)
These 10 tools become available when you connect Portkey to LangChain via MCP:
create_policy
Requires policy name, budget limit (USD or token count), and optionally the target users or virtual keys to restrict. Returns the created policy details. Use this to enforce cost controls on specific teams or projects using the gateway. Create a new budget or usage policy for AI gateway access
delete_policy
Requires the policy ID. Use this when a project ends or budget constraints are no longer needed. Remove a budget or usage policy from Portkey
export_logs
Optionally filters by date range, model, or user. Returns an export ID or download URL. Use this for audit trails, cost reporting, or offline analysis of AI usage patterns. Export AI gateway logs for external analysis or compliance reporting
get_log_details
Requires the log ID from list_logs results. Use this for deep debugging of specific AI interactions. Get detailed information about a specific AI gateway log entry
get_virtual_keys
Virtual keys map to underlying provider keys (OpenAI, Anthropic, etc.) with metadata, usage limits, and policy associations. Returns key IDs, names, provider targets, current usage, and status. Use this to audit API key usage or identify keys approaching limits. List all virtual API keys managed by Portkey
list_configs
Returns config IDs, names, creation dates, and associated virtual keys. Use this to review how LLM requests are routed or to audit gateway behavior. List all gateway configurations stored in Portkey
list_logs
Returns log IDs, timestamps, model names, token usage, latency, costs, and status codes. Use this to monitor AI usage, identify expensive calls, or debug latency issues. Supports pagination via limit/offset. List recent AI gateway logs and traces from Portkey
list_models
). Returns model names, provider names, supported endpoints (chat, embeddings, etc.), and capabilities. Use this to discover which models are routable via your gateway. List all LLM models supported by the Portkey gateway
list_policies
Returns policy names, limits, current consumption, and affected users/keys. Use this to review guardrails preventing runaway AI costs. List all budget and usage policies defined in Portkey
submit_feedback
Requires the log ID, rating (LIKE, DISLIKE, or UNLIKE to remove), and optional text feedback. Use this to build RLHF datasets or monitor user satisfaction with AI outputs. Submit user feedback (Like/Dislike) for a specific AI response log
Example Prompts for Portkey in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Portkey immediately.
"Show me the most expensive LLM calls from the last 24 hours"
"Create a budget policy limiting the Marketing team to $500/month on LLM usage"
"Export all logs from last week for our compliance audit"
Troubleshooting Portkey MCP Server with LangChain
Common issues when connecting Portkey to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersPortkey + LangChain FAQ
Common questions about integrating Portkey 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 Portkey 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 Portkey to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.