LangSmith (LLM Observability & Hub) Connector for AI agents.
6 live capabilities
Monitor LLM traces and prompt performance from your workspace.
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Why people use LangSmith (LLM Observability & Hub)
LangSmith for LLM Trace Debugging
This Connector changes the game by putting that data in your chat window. You can just ask your agent to pull the telemetry for a specific run or list your active projects. You get the facts you need in plain English, letting you stay in your flow while you fix the bugs.
What Vinkius changes
It puts your LLM observability data into a conversational interface so you can debug faster.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Debugging a failed production bot
An engineer asks the agent to list_runs for a specific project and then calls get_run on a failed ID to see the exact error string and prompt.
- Real-world use case 02
Grabbing a high-performing prompt template
A developer uses list_prompts to find a summarization template in the Hub and pulls the version history directly into their chat.
- Real-world use case 03
Checking human feedback backlog
An analyst uses list_annotation_queues to see how many prompts are waiting for human review and reports the count to the team.
Complete set · 6capabilities
The complete LangSmith (LLM Observability & Hub) capability set.
These are the exact actions your AI can choose when you ask it to work with LangSmith (LLM Observability & Hub).
01—03
3 capabilities in this set.
Part of 6 available through LangSmith (LLM Observability & Hub).
- 01 Capability
List annotation queues
Lists active human-in-the-loop annotation queues. Use this to monitor how many reviews are pending.
- 02 Capability
List runs
Lists specific LLM invocation runs within a selected project. It helps you isolate the raw interactions you need to see.
- 03 Capability
Get run
Pulls precise telemetry for a single LLM invocation run. Use this to see exact token counts and error strings.
04—06
3 capabilities in this set.
Part of 6 available through LangSmith (LLM Observability & Hub).
- 04 Capability
List datasets
Lists all evaluation and fine-tuning datasets mapped in LangSmith. This helps you verify your 'golden' data.
- 05 Capability
List prompts
Extracts prompt templates hosted in the LangChain Hub. Use this to pull the latest instructions into your workflow.
- 06 Capability
List projects
Shows all active LangSmith tracing projects and sessions. Use this to get an overview of your monitored pipelines.
Set up in minutes
One URL. Then ask LangSmith (LLM Observability & Hub) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use LangSmith (LLM Observability & Hub) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_ixIJrwVWDWlC1bEOf4IyffAPgAEw302Ns6BE1oLo/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it LangSmith (LLM Observability & Hub), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable LangSmith (LLM Observability & Hub) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_ixIJrwVWDWlC1bEOf4IyffAPgAEw302Ns6BE1oLo/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the LangSmith (LLM Observability & Hub) URL.
- Step 03
Save and start
Save the connection and enable LangSmith (LLM Observability & Hub) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"langsmith-llm-observability-hub": {
"url": "https://edge.vinkius.com/vk_preview_ixIJrwVWDWlC1bEOf4IyffAPgAEw302Ns6BE1oLo/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using LangSmith (LLM Observability & Hub)
Open Agent mode in chat and ask: "Using LangSmith (LLM Observability & Hub), help me...". 6 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"langsmith-llm-observability-hub": {
"url": "https://edge.vinkius.com/vk_preview_ixIJrwVWDWlC1bEOf4IyffAPgAEw302Ns6BE1oLo/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using LangSmith (LLM Observability & Hub)
Ask Copilot: "Using LangSmith (LLM Observability & Hub), help me...". 6 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"langsmith-llm-observability-hub": {
"url": "https://edge.vinkius.com/vk_preview_ixIJrwVWDWlC1bEOf4IyffAPgAEw302Ns6BE1oLo/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using LangSmith (LLM Observability & Hub)
Open Cascade and ask: "Using LangSmith (LLM Observability & Hub), help me...". 6 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"langsmith-llm-observability-hub": {
"url": "https://edge.vinkius.com/vk_preview_ixIJrwVWDWlC1bEOf4IyffAPgAEw302Ns6BE1oLo/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using LangSmith (LLM Observability & Hub)
Ask Cline: "Using LangSmith (LLM Observability & Hub), help me...". 6 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add langsmith-llm-observability-hub --transport http "https://edge.vinkius.com/vk_preview_ixIJrwVWDWlC1bEOf4IyffAPgAEw302Ns6BE1oLo/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using LangSmith (LLM Observability & Hub)
Ask Claude: "Using LangSmith (LLM Observability & Hub), show me...". 6 tools are ready
Where the request belongs
Work LangSmith can move forward.
This is for the engineers and developers who are tired of clicking through deep dashboard menus to find out why a production agent just failed.
LLM Engineer
Debugging complex agentic traces and measuring prompt performance through natural conversation without manual UI filtering.
AI Developer
Retrieving the latest prompt templates from the Hub and verifying evaluation dataset structures directly from their workspace.
AI Analyst
Auditing human feedback queues and reporting on overall model grounding and accuracy across multiple tracing projects.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Aporia
Monitor AI models and validate LLM interactions with guardrails directly from your AI agent to ensure safety and observability.
Bring your own AI
Change the model, client or framework. Keep LangSmith connected.
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Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about LangSmith.
The practical details behind the request, access and result.
Can I see my LLM traces with the LangSmith MCP?
Yes, you can pull logs and see exactly what happened during an LLM run. This includes the prompts sent, responses received, and any intermediate steps the agent took.
How does the LangSmith MCP help with prompt management?
It lets you pull prompt templates directly from the LangChain Hub into your chat. You can check version histories and instructions without leaving your current workspace.
Can I use the LangSmith MCP to see human feedback?
Yes, you can view active human-in-the-loop annotation queues. This helps you see how many reviews are pending and how human reviewers are scoring your model's outputs.
Does the LangSmith MCP show token usage?
Yes, it pulls precise telemetry for specific runs, including total tokens, prompt tokens, and completion tokens, helping you monitor your costs.
Can I manage my datasets with the LangSmith MCP?
You can list all evaluation and fine-tuning datasets mapped in LangSmith. This makes it easy to verify which datasets are being used for your automated tests.
Can I use the LangSmith MCP to debug agent reasoning?
Yes, it allows you to deep-dive into multi-turn agentic workflows. You can see nested capability calls and the internal reasoning paths the agent used to reach its conclusion.
Can I see the token usage for a specific LLM run through my agent?
Yes. Use the get_run_telemetry capability with a specific Run ID. Your agent will retrieve the exact token count (prompt + completion) and latency metrics calculated by LangSmith for that interaction.
How do I fetch a prompt template from the LangChain Hub using natural language?
The list_prompts capability allows your agent to navigate your hosted Hub repository. You can ask your agent to find a specific prompt by name to inspect its instruction text, variables, and version history.
Can my agent check the status of human annotation queues?
Absolutely. Use the list_annotation_queues capability to retrieve all active queues where human feedback is being collected. Your agent can report on the number of pending traces and general alignment scores established by your reviewers.
One connection away
Give your agent a direct line to LangSmith.
Connect LangSmith once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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