Description
LangSmith is designed to provide comprehensive observability for AI agents and LLMs, offering a platform to understand and optimize their performance in production. It allows developers to trace agent behavior, pinpoint issues affecting latency, cost, and response quality, and gain insights into usage patterns and failure modes.
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Key features include tracing, monitoring, and insights. Tracing allows users to see exactly what their agent is doing step by step, facilitating the identification of issues. Monitoring provides a real-time view of agent performance, enabling early issue detection and impact assessment. Insights automatically analyze and cluster traces to detect usage patterns and common agent behaviors. The platform supports native tracing for popular agent frameworks and OpenTelemetry SDKs for various programming languages.
SmithDB, a purpose-built database, is designed for agent observability, offering sub-second performance across millions of traces. It supports agent query patterns, including random access on individual runs, full-text search, and trajectory queries. LangSmith offers self-hosting options, including bring-your-own-cloud (BYOC) and self-hosted options, allowing teams to control where their data resides. The platform integrates with existing OpenTelemetry pipelines and provides custom dashboards to track metrics such as token usage, latency, error rates, and cost breakdowns.
LangSmith is valuable for developers, AI engineers, and teams building and deploying AI agents. It helps them debug agents, improve performance, and reduce costs. The platform is framework-agnostic, supporting applications built with various SDKs, including OpenAI, Anthropic, and Vercel AI SDK. With a free tier for development and small-scale production, LangSmith offers scalable paid plans and enterprise pricing options.
- CategoryMLOps & Model Deployment
- Pricing ModelFreemium
- DeploymentCloud, Self-hosted, BYOC
- PlatformsWeb
LangSmith: AI Agent Observability's Core Features
Agent tracing for debugging
Real-time monitoring dashboards
Cost and latency tracking
Error rate monitoring
Usage pattern analysis
Framework-agnostic integration
OpenTelemetry support
Self-hosting options
SmithDB for fast trace querying
Webhook and PagerDuty alerts
Customizable dashboards
How to use LangSmith: AI Agent Observability?
Integrate: Choose your preferred framework or use the SDKs (Python, Typescript, Go, or Java).
Trace: Implement tracing to capture agent behavior.
Monitor: Set up real-time monitoring dashboards to track key metrics.
Analyze: Use insights to detect usage patterns and failure modes.
Debug: Identify and resolve issues using tracing data.
Optimize: Improve agent performance based on insights and monitoring data.
LangSmith: AI Agent Observability's Use Cases
- Agent Debugging
- Performance Monitoring
- Cost Optimization
- Usage Analysis
- Failure Detection
- LLM Evaluation
