Helicone (LLM Observability) MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Helicone (LLM Observability) 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({
"helicone-llm-observability": {
"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 Helicone (LLM Observability), 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 Helicone (LLM Observability) MCP Server
Connect your Helicone account to any AI agent and take full control of your LLM observability and gateway monitoring through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Helicone (LLM Observability) 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.
What you can do
- Request Monitoring — Query deep proxy logs to inspect exact prompts and outputs sent to LLM APIs directly from your agent
- Cost Analysis — Break down spending by model, user, or custom metadata properties to monitor your AI burn rate in real-time
- Latency Optimization — Measure Time To First Token (TTFT) and pinpoint slowness caused by specific upstream LLM providers
- Prompt Management — Access managed prompt versions and track iterative changes in your AI instruction logic natively
- Session Tracing — Isolate and analyze multi-turn graph traces connecting consecutive LLM calls to debug complex agentic workflows
- User Insights — Track precise LLM interactions based on Helicone tags and identify your most active human clients
- Feedback & RLHF — Extract user critiques (Thumbs Up/Down) and log offline Human-in-the-Loop verdicts to improve model grounding
The Helicone (LLM Observability) 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 Helicone (LLM Observability) to LangChain via MCP
Follow these steps to integrate the Helicone (LLM Observability) 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 Helicone (LLM Observability) via MCP
Why Use LangChain with the Helicone (LLM Observability) MCP Server
LangChain provides unique advantages when paired with Helicone (LLM Observability) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Helicone (LLM Observability) 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 Helicone (LLM Observability) queries for multi-turn workflows
Helicone (LLM Observability) + LangChain Use Cases
Practical scenarios where LangChain combined with the Helicone (LLM Observability) MCP Server delivers measurable value.
RAG with live data: combine Helicone (LLM Observability) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Helicone (LLM Observability), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Helicone (LLM Observability) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Helicone (LLM Observability) tool call, measure latency, and optimize your agent's performance
Helicone (LLM Observability) MCP Tools for LangChain (10)
These 10 tools become available when you connect Helicone (LLM Observability) to LangChain via MCP:
get_prompt_versions
Irreversibly vaporize explicit validations extracting rich Churn flags
list_properties
Identify precise active arrays spanning native Gateway auth
log_feedback
Identify precise active arrays spanning native Hold parsing
query_costs
Perform structural extraction of properties driving active Account logic
query_feedback
Inspect deep internal arrays mitigating specific Plan Math
query_latency
Provision a highly-available JSON Payload generating hard Customer bindings
query_prompts
Retrieve explicit Cloud logging tracing explicit Vault limits
query_requests
Identify bounded CRM records inside the Headless Helicone Platform
query_sessions
Enumerate explicitly attached structured rules exporting active Billing
query_users
Dispatch an automated validation check routing explicit Gateway history
Example Prompts for Helicone (LLM Observability) in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Helicone (LLM Observability) immediately.
"How much did we spend on GPT-4o yesterday?"
"Show me the 10 slowest requests from the last hour"
"List all versions for the 'customer-service-bot' prompt"
Troubleshooting Helicone (LLM Observability) MCP Server with LangChain
Common issues when connecting Helicone (LLM Observability) to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersHelicone (LLM Observability) + LangChain FAQ
Common questions about integrating Helicone (LLM Observability) 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 Helicone (LLM Observability) 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 Helicone (LLM Observability) to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.