AI Agent Builders Tools
PRODUCT OVERVIEW
LangSmith is LangChain's commercial agent engineering platform for building, testing, deploying, monitoring, and improving AI agents. Its Observability service gives complete visibility into agent behavior: trace applications built with LangChain or other stacks via Python, TypeScript, Go, or Java SDKs, and inspect end-to-end execution traces of RAG pipelines and agent decisions. Custom dashboards track token usage, latency, error rates, cost breakdowns, and feedback scores, with alerts via webhooks or PagerDuty. Evaluation, deployment, sandboxes, an LLM gateway, and a no-code agent builder extend the platform. It targets developers and teams shipping agents to production, with managed cloud, BYOC, and self-hosted options.
Sign up at smith.langchain.com, then instrument your application with the LangSmith SDK for Python, TypeScript, Go, or Java to send traces.
Trace applications built with the OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex, or custom implementations; OpenTelemetry support connects to existing pipelines.
Use custom dashboards to track token usage, latency (P50, P99), error rates, cost breakdowns, and feedback scores, and configure alerts via webhooks or PagerDuty.
Observability and Evaluation work independently; start with tracing and monitoring, then add online and offline evals.
Use managed cloud, bring-your-own-cloud, or self-hosted deployment depending on data residency and security requirements.
Debugging complex agent failures and hallucinations with full execution traces
Monitoring production RAG pipelines and agent performance
Tracking model cost and latency metrics across applications
Collecting human feedback and building evaluation datasets
Deploying and scaling agents in production
It provides visibility into how AI applications behave in production, including RAG pipelines and agent decisions, and helps debug failures and hallucinations by showing the complete execution trace.
Custom dashboards track token usage, latency (P50, P99), error rates, cost breakdowns, and feedback scores, with alerts configurable via webhooks or PagerDuty.
LangSmith works with any LLM framework, including the OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex, and custom implementations, plus OpenTelemetry.
Yes. Observability and Evaluation work well together but do not require each other; you can start with tracing and monitoring and add evals later.
Yes. LangSmith offers managed cloud, bring-your-own-cloud, and self-hosted options for teams with data residency requirements.
No. The SDK uses an async callback handler that sends traces to a distributed collector, so application performance is not impacted.
There is a free Developer plan with up to 5k base traces per month, and the Plus plan costs $39 per seat per month with up to 10k base traces; usage beyond that is pay-as-you-go. Enterprise pricing is custom.
LangChain states it will not train on data sent to LangSmith and that you own all rights to your data, per the Terms of Service.
Prices are per seat per month with pay-as-you-go usage beyond included traces. Usage is metered in LangChain Standard Units (LSU) at $1.00 per LSU. Enterprise pricing is custom.
Free plan for solo users getting started, with pay-as-you-go usage beyond the included traces.
Plan for teams building and deploying agents, with more included traces and access to additional services.
Custom plan for teams with advanced hosting, security, and support needs.
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Open agent engineering platform and open source frameworks for building, testing, deploying, monitoring, and improving AI agents.
Managed AI engineering platform for tracing, evaluating, and improving AI agents and LLM applications with observability, evals, and continual improvement workflows.
