How to Use the LangSmith (LLM Observability & Hub) MCP in LlamaIndex

Q: How do I index traces from LangSmith (LLM Observability & Hub) into LlamaIndex?

Retrieve trace data using listruns through the MCP tool spec. Once you have the output, pass the raw data into your LlamaIndex document loader for ingestion.

Q: Can LlamaIndex query prompt templates from LangSmith (LLM Observability & Hub)?

Absolutely. Use listprompts to fetch your templates and add them to your knowledge base as context for your agents.

Q: Does the LlamaIndex integration support human-in-the-loop queues?

Yes, you can monitor listannotationqueues to see which items require human review. You can index these queue items to track common failure modes.

Index your LLM traces into LlamaIndex for searchable knowledge bases and grounded AI responses.

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Connect LangSmith (LLM Observability & Hub) MCP to LlamaIndex

Create your Vinkius account to connect LangSmith (LLM Observability & Hub) to LlamaIndex 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 (LLM Observability & Hub) with LlamaIndex

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Build knowledge from LlamaIndex traces

Convert your production traces into searchable documents to ground your RAG applications. Use `list_runs` to pull execution logs that you can then index into your vector store. This turns historical performance data into a searchable knowledge base. Your agents can then query past successes to resolve current issues.

Query project metadata in LlamaIndex

Map out your AI pipelines by indexing project configurations. By calling `list_projects`, you get a structured view of your monitored environments that LlamaIndex can ingest. This helps your agents maintain context across complex RAG pipelines. You effectively treat your monitoring infrastructure as part of your system's memory.

Sync evaluation data for RAG

Keep your RAG benchmarks updated by pulling evaluation datasets directly into your index. Use `list_datasets` to retrieve the current set of golden records. This keeps your evaluation logic grounded in live data. You can index these datasets to see how your agent's knowledge retrieval compares against known facts.

Setup guide

Set up LangSmith (LLM Observability & Hub) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all LangSmith (LLM Observability & Hub) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to LangSmith (LLM Observability & Hub) tools.",
)
response = await agent.run("List recent LangSmith (LLM Observability & Hub) data")

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Common questions about LangSmith (LLM Observability & Hub) MCP in LlamaIndex

Retrieve trace data using `list_runs` through the MCP tool spec. Once you have the output, pass the raw data into your LlamaIndex document loader for ingestion.

Absolutely. Use `list_prompts` to fetch your templates and add them to your knowledge base as context for your agents.

The server handles your trace logs, prompt variables, and dataset entries securely. Your data stays within the defined API boundaries you set during initialization.

Yes, you can monitor `list_annotation_queues` to see which items require human review. You can index these queue items to track common failure modes.

Use the server tools to fetch data in batches. You can then incrementally update your index to keep your agent's memory current without overloading the system.

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