AI agent observability platform
AI agent? How to work with us.
Grade: B — Score: 75/100
LangSmith Observability is an AI agent observability platform with tracing, monitoring, and insights. It supports native tracing for popular agent frameworks and OpenTelemetry SDKs for Python, TypeScript, Go, and Java.
Teams can use tracing to find failures fast, monitoring to cut through production noise with real-time quality scoring and alerts, and Insights to automatically analyze traces for usage patterns, common behaviors, and failure modes. SmithDB is purpose-built for agent observability and supports random access on individual runs, full-text search, JSON key-path filtering, and trajectory queries.
Risks noted in the source include handling sensitive trace data and the need for deployment choices that match security and compliance requirements. LangSmith offers managed cloud, BYOC, and self-hosted options, and states that the SDK sends traces asynchronously so application performance is not impacted.
Developer: $0 / seat per month, then pay as you go
Plus: $39 / seat per month, then pay as you go
Enterprise: Custom pricing, then pay as you go
Yes. LangSmith records traces so you can inspect agent runs step by step, including LLM calls, tool calls and multi-turn message threading. It supports popular frameworks through native tracing and OpenTelemetry, with SDKs for Python, TypeScript, Go and Java.
Yes. LangSmith monitoring tracks cost, latency, token usage, error rates and feedback scores, and it supports online evals so you can score behavior on the metrics that matter to your team. Alerts are available through webhooks or PagerDuty.
Yes. LangSmith Insights automatically analyzes and clusters traces to surface usage patterns, common agent behaviors and failure modes. The product also includes topic clustering, error-analysis templates and executive summaries for faster triage.
Yes. LangSmith Deployment provides managed infrastructure with 1-click deployment, streaming intermediate steps and final output, auth and authorization, cron scheduling and API endpoints. The product distinguishes serverless and dedicated deployments, and dedicated deployments can autoscale for higher-demand use cases.
Yes. LangSmith Fleet is described as a way to create agents for daily tasks using everyday language rather than code. It includes prebuilt templates, tool connections through a remote MCP server and the ability to trigger an agent via API.
Yes. LangSmith Sandboxes are ephemeral, isolated environments designed for agent-generated code. They support auth proxy with custom callbacks, bring your own image and configurable TTLs.
Yes. LangSmith publishes Developer, Plus and Enterprise plans, with Developer at $0 per seat per month then pay as you go and Plus at $39 per seat per month then pay as you go. Enterprise is custom pricing.
LangSmith documents custom SSO, ABAC and RBAC on the Enterprise plan. The pricing page also lists SSO with Google and GitHub sign-in options. If your access-control requirement depends on custom policy logic, Enterprise is the documented tier to review.
Yes. LangSmith says customers may export Customer Data from Cloud Deployments during a subscription term or within 30 days after, subject to the configured retention period. It also says LangChain will delete Customer Data in its custody and control within 30 days of a written request following termination or expiration.
LangSmith states that Customer Data and Customer Confidential Information remain owned exclusively by the customer, the user or their licensors, as applicable. It also says LangChain will not use Customer Data to train, develop or otherwise improve its products, including large language models.