LangSmith
AI agent and LLM observability platform from LangChain with tracing, evaluation, and monitoring for production applications across any framework
Overview
LangSmith is an AI agent and LLM observability platform built by LangChain, designed to provide complete visibility into agent behavior in production. It offers end-to-end tracing, real-time monitoring, and evaluation capabilities across any framework—not just LangChain. The platform features SDKs for Python, TypeScript, Go, and Java, with native OpenTelemetry support, SmithDB (a purpose-built database delivering 12x faster trace queries), and deployment options including managed cloud, BYOC, and self-hosted. Used by enterprises like Klarna, Nvidia, LinkedIn, Coinbase, and Home Depot, LangSmith helps teams debug, test, and monitor AI applications at scale.
The Verdict
Who Should Use LangSmith?
Best For
- Teams using LangChain or LangGraph frameworks
- Production agent debugging and monitoring
- Organizations needing deep evaluation tooling
- Enterprises requiring self-hosted/BYOC options
- Multi-SDK environments (Python, TS, Go, Java)
Not Ideal For
- Teams wanting fully open-source solutions
- Non-LangChain projects seeking minimal setup
- Budget-sensitive teams (costs scale with usage)
- Beginners without LLM pipeline experience
What's Great
- Near-zero setup for LangChain/LangGraph users
- Framework-agnostic with OpenTelemetry support
- SmithDB delivers 12x faster trace queries (71ms vs 860ms)
- Comprehensive evaluation framework with LLM-as-judge
- Real-time P50/P99 latency and cost monitoring
- Self-hosted and BYOC deployment options
- HIPAA, SOC 2 Type 2, GDPR compliance
- 4 SDK languages (Python, TypeScript, Go, Java)
Watch Out For
- Vendor lock-in risk with LangChain-native instrumentation
- Costs scale quickly with usage-based pricing ($39/seat + traces)
- UI can feel overwhelming with many concurrent runs
- Steep learning curve for LLM beginners
- Migration to other platforms requires full re-instrumentation
Pricing
View all features & details
Core Features
- Distributed tracing with nested spans
- Real-time monitoring dashboards
- P50/P99 latency tracking
- Cost tracking per trace
- LLM-as-judge evaluations
- Prompt versioning & Hub
- Annotation queues
- Webhook & PagerDuty alerts
AI Integrations
- LangChain / LangGraph
- OpenAI SDK
- Anthropic SDK
- Vercel AI SDK
- LlamaIndex
- Custom implementations
- OpenTelemetry
SmithDB Performance
- 12x faster trace queries (71ms)
- 9x faster thread queries (131ms)
- 15x faster full-text search (400ms)
- 6x faster filtering (82ms)
- Sub-second across millions of traces
Deployment Options
- Managed cloud (GCP us-central-1)
- Bring-your-own-cloud (BYOC)
- Self-hosted on Kubernetes
- AWS, GCP, Azure support
- HIPAA / SOC 2 / GDPR compliant
How It Compares
| Feature | LangSmith | Langfuse | Logfire | Arize Phoenix |
|---|---|---|---|---|
| Open Source | SDK only | Fully OSS | SDK only | Fully OSS |
| Self-Hosted | Enterprise | Yes (free) | Enterprise | Yes |
| Free Tier | 5K traces | 50K obs | 10M records | Unlimited local |
| LangChain Native | Best-in-class | Good | Good | Good |
| Multi-SDK | 4 languages | 2 languages | 3 languages | 2 languages |
| Evaluation Tools | Extensive | Basic | Via Evals | Strong |
| OTel Native | Yes | No | Yes | No |
| Best For | LangChain teams | Self-hosted | Pydantic/OTel | RAG evaluation |