tracing

Original：🇺🇸 English
Translated

Add LangWatch tracing and observability to your code. Use for both onboarding (instrument an entire codebase) and targeted operations (add tracing to a specific function or module). Supports Python and TypeScript with all major frameworks.

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Sourcelangwatch/skills
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NPX Install

npx skill4agent add langwatch/skills tracing

Tags

Translated version includes tags in frontmatter
langwatchllm-observabilitycode-instrumentationtracingai-monitoring

SKILL.md Content

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Add LangWatch Tracing to Your Code

Determine Scope

If the user's request is general ("instrument my code", "add tracing", "set up observability"):
  • Read the full codebase to understand the agent's architecture
  • Study git history to understand what changed and why — focus on agent behavior changes, prompt tweaks, bug fixes. Read commit messages for context.
  • Add comprehensive tracing across all LLM call sites
If the user's request is specific ("add tracing to the payment function", "trace this endpoint"):
  • Focus on the specific function or module
  • Add tracing only where requested
  • Verify the instrumentation works in context

Detect Context

This skill is code-only — there is no platform path for tracing. If the user has no codebase, explain that tracing requires code instrumentation and point them to the LangWatch docs.

Step 1: Set up the LangWatch MCP

First, install the LangWatch MCP server so you have access to framework-specific documentation:
See MCP Setup for installation instructions.
If MCP installation fails, see docs fallback to fetch docs directly via URLs.

Step 2: Get the API Key

See API Key Setup.
Add the API key to the project's 
.env
file:
LANGWATCH_API_KEY=your-key-here

Step 3: Read the Integration Docs

Use the LangWatch MCP to fetch the correct integration guide for this project:
  • Call 
    fetch_langwatch_docs
    with no arguments to see the docs index
  • Find the integration guide matching the project's framework (OpenAI, LangGraph, Vercel AI, Agno, Mastra, etc.)
  • Read the specific integration page for step-by-step instructions
CRITICAL: Do NOT guess how to instrument. Read the actual documentation for the specific framework. Different frameworks have different instrumentation patterns.

Step 4: Install the LangWatch SDK

For Python:
bash
pip install langwatch
# or: uv add langwatch
For TypeScript:
bash
npm install langwatch
# or: pnpm add langwatch

Step 5: Add Instrumentation

Follow the integration guide you read in Step 3. The general pattern is:
Python:
python
import langwatch
langwatch.setup()

@langwatch.trace()
def my_function():
    # your existing code
    pass
TypeScript:
typescript
import { LangWatch } from "langwatch";
const langwatch = new LangWatch();
IMPORTANT: The exact pattern depends on the framework. Always follow the docs, not these examples.

Step 6: Verify

Run the application and check that traces appear in your LangWatch dashboard at https://app.langwatch.ai

Common Mistakes

  • Do NOT invent instrumentation patterns — always read the docs for the specific framework
  • Do NOT skip the 
    langwatch.setup()
    call in Python
  • Do NOT forget to add LANGWATCH_API_KEY to .env
  • Do NOT use 
    platform_
    MCP tools — this skill is about adding code, not creating platform resources