LLM Observability
Get actionable insights to improve performance, catch failures before they impact users, and optimize your AI investments.
LLM metrics built for AI Engineers & Product Teams
Monitor what matters with LangWatch's extensive LLM observability metrics
•Prompt & response tracing
Capture the full lifecycle of every LLM call, including inputs, outputs, retries, and context variables.
•Metadata-Rich Logs
Attach user IDs, session context, features used, or any custom metadata for deeper filtering and analysis.
•Latency & error tracking
Understand performance bottlenecks, slow generations, and model failures—across all environments.
•Error Tracking
Identify and analyze failures and rate-limiting issues
•Token Usage
Track input and output tokens across models and requests
•User Journey Mapping
Follow how users interact with your LLM applications. In-depth user-analytics, export Analytics via API to anywhere.
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Trace and debug your agent with easy
Visualize your multi-step LLM interactions, log requests in real-time and pinpoint root cause of errors.
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Complete observability for your AI applications
LangWatch provides seamless monitoring and debugging tools for your entire LLM stack
Open the blackbox: Track inputs, outputs, latency, tokens, cost, and metadata across your entire LLM pipeline. Get started with setting up your traces.
Identify exactly where issues arise with a full stack trace and control flow visualization of your AI products. Monitor latency, and ensure optimal throughput. Setup custom Dashboards to view the performance and act on it.
Triggers and alerts
LangWatch lets you define trigger conditions to flag anomalies, failed evaluations, or specific patterns—helping you automate monitoring and improve GenAI reliability in real-time.
LangWatch works with any LLM framework - LangChain, DSPy, direct API calls, and custom implementations. Integrate our Python SDK, Typescript SDK, via OpenTelemetry or RestAPI. New model available on the market? Immediately available via LiteLLM on LangWatch.