LangSmith (LLM Observability & Hub) MCP Server for CrewAI 6 tools — connect in under 2 minutes
Connect your CrewAI agents to LangSmith (LLM Observability & Hub) through the Vinkius — pass the Edge URL in the `mcps` parameter and every LangSmith (LLM Observability & Hub) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="LangSmith (LLM Observability & Hub) Specialist",
goal="Help users interact with LangSmith (LLM Observability & Hub) effectively",
backstory=(
"You are an expert at leveraging LangSmith (LLM Observability & Hub) tools "
"for automation and data analysis."
),
# Your Vinkius token — get it at cloud.vinkius.com
mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)
task = Task(
description=(
"Explore all available tools in LangSmith (LLM Observability & Hub) "
"and summarize their capabilities."
),
agent=agent,
expected_output=(
"A detailed summary of 6 available tools "
"and what they can do."
),
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
* 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 (LLM Observability & Hub) MCP Server
Connect your LangSmith account to any AI agent and take full control of your LLM observability, tracing, and prompt management through natural conversation.
When paired with CrewAI, LangSmith (LLM Observability & Hub) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call LangSmith (LLM Observability & Hub) tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.
What you can do
- Trace Orchestration — List active tracing projects and retrieve detailed execution logs for specific LLM invocation runs directly from your agent
- Performance Telemetry — Extract precise metrics including token consumption, prompt latency, and exact error strings from your AI pipelines
- Prompt Hub Access — Navigate and retrieve managed prompt templates, variable definitions, and version histories hosted in the LangChain Hub
- Evaluation Datasets — Enumerate curated 'golden' datasets used for automated evaluation of prompt logic or few-shot injection models
- Human-in-the-Loop Audit — Monitor active annotation queues where human reviewers assess the alignment, accuracy, and safety of generated LLM traces
- Agentic Step Analysis — Deep-dive into multi-turn agentic workflows to understand nested tool calls and internal reasoning paths securely
The LangSmith (LLM Observability & Hub) MCP Server exposes 6 tools through the Vinkius. Connect it to CrewAI 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 (LLM Observability & Hub) to CrewAI via MCP
Follow these steps to integrate the LangSmith (LLM Observability & Hub) MCP Server with CrewAI.
Install CrewAI
Run pip install crewai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Customize the agent
Adjust the role, goal, and backstory to fit your use case
Run the crew
Run python crew.py — CrewAI auto-discovers 6 tools from LangSmith (LLM Observability & Hub)
Why Use CrewAI with the LangSmith (LLM Observability & Hub) MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with LangSmith (LLM Observability & Hub) through the Model Context Protocol.
Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools
CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
LangSmith (LLM Observability & Hub) + CrewAI Use Cases
Practical scenarios where CrewAI combined with the LangSmith (LLM Observability & Hub) MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries LangSmith (LLM Observability & Hub) for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff
Scheduled intelligence reports: set up a crew that periodically queries LangSmith (LLM Observability & Hub), analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain LangSmith (LLM Observability & Hub) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow
Compliance and audit automation: a compliance agent queries LangSmith (LLM Observability & Hub) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
LangSmith (LLM Observability & Hub) MCP Tools for CrewAI (6)
These 6 tools become available when you connect LangSmith (LLM Observability & Hub) to CrewAI via MCP:
get_run
Get precise telemetry for a single LLM invocation run
list_annotation_queues
List active human-in-the-loop annotation queues
list_datasets
List all evaluation and fine-tuning datasets mapped in LangSmith
list_projects
Maps out the boundaries of distinct AI pipelines currently monitored by LangSmith. List all active LangSmith tracing projects/sessions
list_prompts
Extract prompt templates hosted in the LangChain Hub
list_runs
Isolates the raw interactions containing prompts sent to and responses received from the AI models. List explicit LLM invocation runs within a specific project
Example Prompts for LangSmith (LLM Observability & Hub) in CrewAI
Ready-to-use prompts you can give your CrewAI agent to start working with LangSmith (LLM Observability & Hub) immediately.
"List all active tracing projects in LangSmith"
"Show me the telemetry for the last run in the 'Production-Bot-V2' project"
"List all prompts hosted in our Hub repository"
Troubleshooting LangSmith (LLM Observability & Hub) MCP Server with CrewAI
Common issues when connecting LangSmith (LLM Observability & Hub) to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
LangSmith (LLM Observability & Hub) + CrewAI FAQ
Common questions about integrating LangSmith (LLM Observability & Hub) MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.Can different agents in the same crew use different MCP servers?
mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.What happens when an MCP tool call fails during a crew run?
Can CrewAI agents call multiple MCP tools in parallel?
process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.Can I run CrewAI crews on a schedule (cron)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect LangSmith (LLM Observability & Hub) 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 (LLM Observability & Hub) to CrewAI
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.