How to Use the LangSmith MCP in AutoGen

Connect LangSmith to AutoGen to observe the debate between your agents and verify their consensus decisions.

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Connect LangSmith MCP to AutoGen

Create your Vinkius account to connect LangSmith to AutoGen and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup LangSmith with AutoGen

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Perplexity

Observe AutoGen agent debates in real-time

The `langsmith_list_runs` tool lets you view the history of multi-agent conversations. You see which agent said what and how they reached a conclusion. This is essential when your agents are debating complex tasks. You identify the exact point where the consensus started to drift.

Track AutoGen project execution health

Use `langsmith_list_projects` to see how your agent groups are performing across different test sets. You track success counts and feedback metrics. This gives you a bird's-eye view of your multi-agent architecture. You know which team of agents is reliable and which one needs more guardrails.

Inspect AutoGen agent tool calls

Call `langsmith_get_run` to see the full trace of an agent's tool execution. You verify if the agent followed the right protocol during its deliberation. You stop wondering why an agent made a specific move. The trace shows you the exact reasoning path taken during the conversation.

Setup guide

Set up LangSmith MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes LangSmith tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="LangSmith_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent LangSmith data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="LangSmith_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent LangSmith data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LangSmith. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about LangSmith MCP in AutoGen

You pull the conversation history via the list runs tool to see the message sequence between agents. This lets you debug the decision-making process within your AutoGen setup.

Yes, every agent interaction is logged with its associated token usage. You can monitor the costs of your multi-agent debates over time.

You can. The get run tool returns the full result of any tool called during a conversation, allowing you to check if your agents are using the right data.

Yes, the project list tool aggregates metrics for your different agent configurations. You can compare how different agent teams handle the same task.

The server only logs performance telemetry and trace metadata. It never stores the raw content of your private conversations, ensuring your agent logic stays confidential.

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