debug-instrumentation

name: debug-instrumentation description: Debug and improve your LangWatch traces. Inspects production traces for missing input/output, disconnected spans, unlabeled traces, and missing metadata. Use when traces look broken or incomplete. license: MIT compatibility: Requires the langwatch CLI with a valid LANGWATCH_API_KEY. Works with any coding agent. metadata: category: recipe

Debug Your LangWatch Instrumentation

This recipe uses the langwatch CLI to inspect your production traces and identify instrumentation issues.

Prerequisites

Use langwatch docs <path> to read documentation as Markdown. Some useful entry points:

langwatch docs                                    # Docs index
langwatch docs integration/python/guide           # Python integration
langwatch docs integration/typescript/guide       # TypeScript integration
langwatch docs prompt-management/cli              # Prompts CLI
langwatch scenario-docs                           # Scenario docs index

Discover commands with langwatch --help and langwatch <subcommand> --help. List and get commands accept --format json for machine-readable output. Read the docs first instead of guessing SDK APIs or CLI flags.

If no shell is available, fetch the same Markdown over plain HTTP — append .md to any docs path (e.g. https://langwatch.ai/docs/integration/python/guide.md). Index: https://langwatch.ai/docs/llms.txt. Scenario index: https://langwatch.ai/scenario/llms.txt

Step 1: Fetch Recent Traces

langwatch trace search --limit 25 --start-date 2026-01-01 --format json

(Adjust --start-date to "last 24h" or "last 7d" — the CLI accepts ISO strings.)

For each trace, ask:

• How many traces are there?
• Do they have inputs and outputs populated, or are they <empty>?
• Are there labels and metadata (user_id, thread_id)?

langwatch status is a fast sanity check that the CLI is talking to the right project.

Step 2: Inspect Individual Traces

langwatch trace get <traceId>            # Human-readable digest
langwatch trace get <traceId> -f json    # Full span hierarchy as JSON

For traces that look problematic, check for:

• Empty input/output: The most common issue. Check if autotrack_openai_calls(client) (Python) or experimental_telemetry (TypeScript/Vercel AI) is configured.
• Disconnected spans: Spans that don't connect to a parent trace. Usually means @langwatch.trace() decorator is missing on the entry function.
• Missing labels: No way to filter traces by feature/version. Add labels via langwatch.get_current_trace().update(metadata={"labels": ["feature_name"]}).
• Missing user_id/thread_id: Can't correlate traces to users or conversations. Add via trace metadata.
• Slow spans: Unusually long completion times may indicate API timeouts or inefficient prompts.

Step 3: Read the Integration Docs

Use the CLI to read the integration guide for the project's framework. Compare the recommended setup with what's in the code.

langwatch docs                                  # Browse the docs index
langwatch docs integration/python/guide         # Python (or your framework)
langwatch docs integration/typescript/guide     # TypeScript (or your framework)

Step 4: Apply Fixes

For each issue found:

1. Identify the root cause in the code
2. Apply the fix following the framework-specific docs
3. Run the application to generate new traces
4. Re-inspect with langwatch trace search and langwatch trace get to verify the fix

Step 5: Verify Improvement

After fixes, compare before/after:

• Are inputs/outputs now populated?
• Are spans properly nested?
• Are labels and metadata present?

You can also export a sample for diff:

langwatch trace export --format jsonl --limit 50 -o traces.jsonl

Common Issues and Fixes