Understanding the core concepts of LangWatch is essential for effective observability in your LLM applications. This guide explains key terms and their relationships using practical examples, like building an AI travel assistant or a text generation service.

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Threads: The Whole Conversation

Field: thread_id Think of a Thread as the entire journey a user takes in a single session. It’s the complete chat with your AI travel buddy, from “Where should I go?” to booking the flight. For the blog post generator, a thread_id bundles up the whole session – from brainstorming headlines to polishing the final SEO-optimized draft. It groups all the back-and-forth interactions (Traces) for a specific task or conversation.

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Traces: One Task, End-to-End

Field: trace_id
While previously LangWatch allowed you to pass in a custom trace_id, we now generate it for you automatically, and provide no way to pass in your own.
Zooming in from Threads, a Trace represents a single, complete task performed by your AI. It’s one round trip.
  • Travel Bot: A user asking, “What are the cheapest flights to Bali in July?” is one Trace. Asking, “Does the hotel allow llamas?” is another Trace.
  • Blog Tool: Generating headline options? That’s a Trace. Drafting the intro paragraph? Another Trace. Optimizing for keywords? You guessed it – a Trace.
Each trace_id captures an entire end-to-end generation, no matter how many internal steps (Spans) it takes.

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Spans: The Building Blocks

Field: span_id
While previously LangWatch allowed you to pass in a custom span_id, we now generate it for you automatically, and provide no way to pass in your own.
Now, let’s get granular! Spans are the individual steps or operations within a single Trace. Think of them as the building blocks of getting the job done.
  • Travel Bot Trace: Might have a Span for the LLM call figuring out destinations, another Span querying an airline API for prices, and a final Span formatting the response.
  • Blog Tool Trace: Could involve a Span for the initial text generation, a second Span where the LLM critiques its own work (clever!), and a third Span refining the text based on that critique.
Each span_id pinpoints a specific action taken by your system or an LLM call.

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User ID: Who’s Using the App?

Field: user_id Simple but crucial: The User ID identifies the actual end-user interacting with your product. Whether they’re planning trips or writing posts, this user_id (usually their account ID in your system) links the activity back to a real person, helping you see how different users experience your AI features.

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Customer ID: For Platform Builders

Field: customer_id Are you building a platform for other companies to create their own LLM apps? That’s where the Customer ID shines. If you’re providing the tools for others (your customers) to build AI assistants for their users, the customer_id lets you (and them!) track usage and performance per customer account. It’s perfect for offering custom analytics dashboards, showing your customers how their AI implementations are doing.

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Labels: Your Organizational Superpowers

Field: labels Think of Labels as flexible tags you can slap onto Traces to organize, filter, and compare anything you want! They’re your secret weapon for slicing and dicing your data.
  • Categorize Actions: Use labels like blogpost_title or blogpost_keywords.
  • Track Versions: Label traces with version:v1.0.0, then deploy an improved prompt and label new traces version:v1.0.1.
  • Run Experiments: Tag traces with experiment:prompt_a vs. experiment:prompt_b.
Labels make it easy to zoom in on specific features or A/B test different approaches right within the LangWatch dashboard.