LangSmith

Free

AI Agent Observability Platform

FreeFree tier

Type

Open Source

Founded

2023

Company

LangChain

Visit Website GitHub (38 stars)

About LangSmith

LangSmith is an AI agent observability platform that provides complete visibility into agent behavior through tracing, monitoring, and insights. It offers native tracing for popular LLM frameworks (LangChain, OpenAI SDK, Anthropic SDK, etc.) and supports OpenTelemetry. LangSmith's monitoring dashboards track cost, latency, error rates, and feedback scores with customizable alerts. Its Insights feature automatically analyzes trace data to detect usage patterns and failure modes. SmithDB, a purpose-built query engine, enables sub-second searches across millions of traces and supports self-hosted deployment for data privacy. LangSmith is part of the LangChain ecosystem, designed to help teams debug, monitor, and improve AI agents in production.

Key Features

Tracing with step-by-step agent action visibility and multi-turn message threading

Monitoring dashboards tracking cost, latency, error rates, and feedback scores with webhook/PagerDuty alerts

Insights for automatic trace clustering, error analysis, and executive summaries

SmithDB purpose-built query engine for sub-second searches across millions of traces

Native tracing support for LangChain, OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex, and more

OpenTelemetry integration for sending or ingesting trace data

Self-hosted deployment option for sensitive data within your VPC

Online and offline evaluations with LLM-as-judge and code evals

Pros & Cons

Pros

Provides complete end-to-end visibility into agent behavior
Supports a wide range of LLM frameworks and SDKs
SmithDB offers significantly faster query performance than general-purpose databases
Self-hosted option ensures sensitive data stays within your infrastructure
Comprehensive monitoring with cost tracking and custom alerts
Free tier available for solo developers

Cons

Free tier limited to 5,000 base traces per month and 1 seat
Advanced features (deployment, engine) require paid plans
May have a learning curve for teams new to observability concepts
Requires integration and setup effort to fully leverage

Best For

Debugging agent failures and hallucinations by examining full execution tracesMonitoring production agent performance (latency, cost, error rates)Analyzing user behavior patterns and detecting anomalies in agent usageImproving response quality through online evals and feedback collectionComplying with data privacy by self-hosting trace 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. It provides visibility into RAG pipelines, AI agent decisions, tracks model performance metrics like cost and latency, and helps debug complex failures and hallucinations by showing the complete execution trace from end-to-end.

What metrics can I track in LangSmith monitoring dashboards?

Custom dashboards track token usage, latency (P50, P99), error rates, cost breakdowns, and feedback scores. You can configure alerts 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, and custom implementations. It also supports OpenTelemetry for integration with existing pipelines.

Does LangSmith support OTel?

Yes. If your team has observability infrastructure on OpenTelemetry, LangSmith integrates with your existing pipelines. You can send LangSmith trace data to your tools or ingest OTel data into LangSmith.