LangSmith
Freea unified platform from LangChain framework for: evaluation, collaboration HITL (Human In The Loop), logging and monitoring LLM applications.
FreeFree tier
About LangSmith
LangSmith is an agent LLM observability platform that provides complete visibility into AI agent behavior. It offers tracing, monitoring, and insights capabilities to debug, monitor, and improve LLM applications. LangSmith works with any LLM framework including OpenAI, Anthropic, LlamaIndex, and custom implementations. It features SmithDB, a purpose-built database for agent trace queries, online evals with LLM-as-judge, cost tracking, alerting, and self-hosted deployment options. LangSmith is part of the LangChain ecosystem and integrates with OpenTelemetry.
Key Features
Tracing: Find failures fast with agent tracing, step-by-step visibility, native tracing for popular frameworks, and OTel SDKs for Python, TypeScript, Go, and Java.
Monitoring: Real-time dashboards with cost tracking, online LLM-as-judge and code evals, tool and agent trajectory monitoring, webhook and PagerDuty alerts.
Insights: Automatic clustering and analysis of traces to detect usage patterns, error analysis, and executive summaries.
SmithDB: Purpose-built database for agent observability with sub-second queries across millions of traces, self-hostable in your VPC.
Deployment: Deploy and manage long-running agents with serverless or dedicated deployments, Assistants API, and cron scheduling.
Fleet: Create agents for daily tasks using natural language, prebuilt templates, and remote MCP server tools.
Engine: Find agent failures, diagnose root causes, and generate fixes with automated eval coverage.
Pros & Cons
Pros
- Provides deep visibility into agent behavior with step-by-step tracing
- Works with any LLM framework (OpenAI, Anthropic, LlamaIndex, etc.)
- Purpose-built database (SmithDB) for fast trace queries
- Supports self-hosting for data privacy and compliance
- Includes automated insights and error analysis
- Integrates with OpenTelemetry for existing observability pipelines
Cons
- Free tier limited to 5k base traces per month
- Pricing scales with usage beyond free tier
- May require integration effort for non-standard frameworks
Best For
Debugging agent failures in productionMonitoring LLM application performance and costsAnalyzing agent behavior and usage patternsEvaluating model outputs with online evalsSelf-hosting observability for sensitive data
FAQ
Why do teams need an LLM observability platform?
Teams need an LLM observability platform to understand how their AI applications behave in production, providing visibility into RAG pipelines, AI agent decisions, and helping debug complex failures and hallucinations by showing complete execution traces.
What metrics can I track in LangSmith monitoring dashboards?
Custom dashboards track token usage, latency (P50, P99), error rates, cost breakdowns, and feedback scores. Alerts can be configured via webhooks or PagerDuty when metrics cross thresholds.
What frameworks and libraries does LangSmith work with?
LangSmith works with any LLM framework, including OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex, or custom implementations, not just LangChain.
Does LangSmith support OTel?
Yes. LangSmith integrates with OpenTelemetry. You can send LangSmith trace data to your existing OTel tools or ingest OTel data into LangSmith.