LangSmith MCP Server for OpenAI Agents SDK 3 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect LangSmith through the Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails — no manual schema definitions required.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="LangSmith Assistant",
instructions=(
"You help users interact with LangSmith. "
"You have access to 3 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from LangSmith"
)
print(result.final_output)
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 LangSmith MCP Server
Connect your AI agent to LangSmith — the observability platform from the LangChain team that gives you complete visibility into your LLM applications.
The OpenAI Agents SDK auto-discovers all 3 tools from LangSmith through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns — chain multiple agents where one queries LangSmith, another analyzes results, and a third generates reports, all orchestrated through the Vinkius.
What you can do
- List Projects — View all tracing projects with aggregate metrics: total runs, median latency, feedback scores, and creation dates
- List Runs — Browse recent traces in any project. See run names, types (LLM, chain, tool), status (success/error), token usage, and timing
- Run Details — Deep-dive into any specific run to see its full execution trace, inputs, outputs, and associated feedback
The LangSmith MCP Server exposes 3 tools through the Vinkius. Connect it to OpenAI Agents SDK 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 LangSmith to OpenAI Agents SDK via MCP
Follow these steps to integrate the LangSmith MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 3 tools from LangSmith
Why Use OpenAI Agents SDK with the LangSmith MCP Server
OpenAI Agents SDK provides unique advantages when paired with LangSmith through the Model Context Protocol.
Native MCP integration via `MCPServerSse` — pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
LangSmith + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the LangSmith MCP Server delivers measurable value.
Automated workflows: build agents that query LangSmith, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents — one queries LangSmith, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through LangSmith tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query LangSmith to resolve tickets, look up records, and update statuses without human intervention
LangSmith MCP Tools for OpenAI Agents SDK (3)
These 3 tools become available when you connect LangSmith to OpenAI Agents SDK via MCP:
langsmith_get_run
Useful for debugging specific LLM calls or agent actions. Get detailed information about a specific run/trace by its ID
langsmith_list_projects
Each project groups related traces together and shows aggregate metrics like total runs, median latency, and feedback counts. List all tracing projects in your LangSmith account with run counts, latency stats, and feedback metrics
langsmith_list_runs
Each run represents a single LLM call, chain execution, or agent action. Shows status (success/error), latency, and token consumption. List recent traces/runs in a specific LangSmith project. Shows run names, types, status, token usage, and timing
Example Prompts for LangSmith in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with LangSmith immediately.
"List all my LangSmith projects and show their metrics."
"Show me the last 5 runs in my production-agent project."
"Get details on the failed run a0b1c2."
Troubleshooting LangSmith MCP Server with OpenAI Agents SDK
Common issues when connecting LangSmith to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
LangSmith + OpenAI Agents SDK FAQ
Common questions about integrating LangSmith MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect LangSmith 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 LangSmith to OpenAI Agents SDK
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.