LangSmith: AI Agent and LLM Observability Platform
LangSmith helps software companies monitor AI agent behavior and track performance metrics. It is designed for teams that need to debug agent failures and monitor LLM costs and latency.
At a glance
- Category
- Software Development
- Best for
- Software companies, AI engineering teams, Developers building LLM-based agents, Enterprise teams with specific data residency needs
- Pricing
- LangSmith uses a freemium model starting with a $0 Developer plan. The Plus plan is $39 per seat per month. Both plans use usage-based billing for traces, with base traces at $2.50 per 1k and extended traces at $5.00 per 1k.
- Key use cases
- Debugging Agent Failures, Tracking LLM Expenditures, Improving Response Quality, Implementing Human Feedback
- Integrations
- OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex, OpenTelemetry
- Official website
- www.langchain.com/langsmith
LangSmith is an observability platform for teams developing AI agents and applications using Large Language Models (LLMs). It provides visibility into the internal steps an agent takes, which may help developers identify where failures or hallucinations occur in a production environment.
The tool is designed for software companies and engineering teams. It supports Python, TypeScript, Go, and Java SDKs and is framework-agnostic, meaning it can work with various LLM stacks regardless of whether the team uses LangChain.
Beyond monitoring, the platform includes tools for evaluation and deployment, as well as a no-code agent builder called Fleet. Buyers should consider their data residency requirements, as the tool offers different hosting options ranging from managed cloud to self-hosted environments.
Businesses should review the trace-based pricing model to understand how their specific volume of agent executions will impact monthly costs.
Key Features
Provides step-by-step visibility into agent executions to help locate failures and understand decision paths.
Tracks token usage, latency (P50, P99), error rates, and cost breakdowns through custom dashboards.
Supports LLM-as-judge and code-based evaluators to score agent quality based on production trace data.
A no-code builder that allows non-technical users to create and manage agents using natural language.
Infrastructure for deploying and scaling long-running agents with support for human-in-the-loop approvals.
Analyzes and clusters traces to detect common failure modes and usage patterns.
Use Cases
Using execution traces to find the specific step where an AI agent deviated from the intended path.
Monitoring token usage and cost breakdowns to manage the budget for LLM API calls.
Running evaluations on production traces to compare how changes to prompts or models affect performance.
Using annotation queues to allow domain experts to review and label agent outputs.
Best For
Integrations
Pricing
LangSmith uses a freemium model starting with a $0 Developer plan. The Plus plan is $39 per seat per month. Both plans use usage-based billing for traces, with base traces at $2.50 per 1k and extended traces at $5.00 per 1k.
FAQ
Yes, it is framework agnostic and supports the OpenAI SDK, Anthropic SDK, Vercel AI SDK, LlamaIndex, and custom implementations.
It offers a free Developer plan, a $39/seat Plus plan, and custom Enterprise pricing. Plans include usage-based billing for traces, with costs varying by retention period (base vs. extended).
No, LangSmith does not use your traces, prompts, or outputs to train models, and users retain the rights to their data.
Yes, LangSmith offers hybrid and self-hosted options for teams with specific data residency or security requirements, available on the Enterprise plan.
Source category: Software Development
Source subcategory: Observability Platform
