Bring Llm Observability
to Pydantic AI

Connect your LangSmith account to any AI agent and take full control of your LLM observability, tracing, and prompt management through natural conversation.

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

How it works

1. Subscribe to this server
2. Enter your LangSmith API Key and Endpoint
3. Start monitoring your LLM infrastructure from Claude, Cursor, or any MCP-compatible client

Who is this for?

• LLM Engineers — debug complex agentic traces and measure prompt performance through natural conversation without manual UI filtering
• AI Developers — retrieve the latest prompt templates from the Hub and verify evaluation dataset structures directly from your workspace
• AI Analysts — audit human feedback queues and report on overall model grounding and accuracy across multiple tracing projects