LLM observability and analytics platform for monitoring AI agent quality, costs, and user experience with real-time dashboards and automated guardrails.
Monitor your AI's quality and costs in production — catch issues, track spending, and understand how users interact with your AI.
LangWatch is an observability and analytics platform designed for monitoring LLM applications and AI agents in production. It provides real-time visibility into agent performance, quality, costs, and user experience through comprehensive tracing, automated evaluations, and customizable dashboards. The platform helps teams ensure their agents maintain quality standards while optimizing costs and identifying issues before they impact users.
The platform captures detailed traces of every agent interaction including prompts, completions, tool calls, retrieval steps, and metadata. These traces are automatically evaluated against configurable quality checks — sentiment analysis, PII detection, topic adherence, toxicity filtering, and custom business rules. Failed checks can trigger alerts, block responses, or flag interactions for human review.
LangWatch's analytics engine provides insights into agent usage patterns, user satisfaction, conversation flows, and cost trends. Custom dashboards can track business-specific KPIs like resolution rates, escalation frequency, and user engagement. The platform identifies conversation drop-off points and common failure patterns to guide agent improvement.
Integration is straightforward with SDKs for Python and TypeScript that auto-instrument popular frameworks including LangChain, LlamaIndex, OpenAI, and Anthropic. A REST API enables integration with any language or framework. The platform supports both cloud-hosted and self-hosted deployments.
LangWatch's guardrails feature enables real-time content filtering and quality enforcement before responses reach users. This includes PII redaction, topic restriction, response length enforcement, and custom validation rules. The combination of monitoring and guardrails makes LangWatch both an observability tool and an active safety layer for production agent systems.
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Configurable quality checks on every interaction — sentiment, PII detection, topic adherence, toxicity — with automatic alerting and response blocking.
Use Case:
Active content filtering and validation before responses reach users, including PII redaction, topic restriction, and custom rules.
Use Case:
Usage patterns, satisfaction tracking, conversation flows, and drop-off analysis to understand how users interact with agents.
Use Case:
Track LLM costs per request, user, feature, and time period with alerts for budget anomalies and cost optimization recommendations.
Use Case:
Build dashboards tracking business-specific KPIs like resolution rates, escalation frequency, and user engagement metrics.
Use Case:
SDKs for Python and TypeScript auto-instrument LangChain, LlamaIndex, OpenAI, and Anthropic with minimal code changes.
Use Case:
Free
€59/month
Custom pricing
Ready to get started with LangWatch?
View Pricing Options →Production AI applications requiring comprehensive monitoring and debugging
Teams developing complex multi-agent systems needing simulation testing
Organizations requiring AI safety controls and compliance monitoring
Development teams optimizing prompts and model performance systematically
Enterprises needing collaborative workflows for AI system evaluation and improvement
We believe in transparent reviews. Here's what LangWatch doesn't handle well:
LangWatch adds active guardrails (PII detection, content filtering) on top of observability. Langfuse focuses purely on tracing and analytics without real-time intervention capabilities.
Yes, guardrail checks add processing time. Simple checks (PII regex) are fast; LLM-based evaluations add more latency. You can configure which checks run synchronously vs asynchronously.
Yes, self-hosted deployment is available on Enterprise plans for organizations requiring full data sovereignty.
Yes. LangWatch captures streaming responses and applies guardrails and evaluations on the complete response while maintaining streaming to the user.
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