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LangSmith

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LLM observability and evaluation platform by LangChain for tracing, testing, and monitoring production AI agents and chains with dataset-driven evaluation.

LangSmith is an LLM development and operations platform built by LangChain and launched in 2023 that gives developers full visibility into every LLM call, tool invocation, and reasoning step in their AI applications. The platform captures execution traces as visual graphs for debugging, enables structured prompt evaluation against test datasets, and provides production monitoring dashboards for latency, cost, and error rates. LangSmith integrates natively with LangChain and LangGraph - the most widely used open-source LLM frameworks - providing essentially zero-config tracing for teams already in that ecosystem. With LangChain reaching over 1 million developers, LangSmith became one of the most widely adopted LLM observability platforms within a year of launch.

#llm-observability
#ai-development
#debugging
#developer-tools
#tracing
Freemium

Free plan available

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smith.langchain.com
Freemium
Pricing Model
Code & Development
Category
2023
Since
Free Plan
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Key Features

  • Execution tracing captures every LLM call, tool invocation, and chain step as a searchable visual graph
  • Dataset and evaluation system for running structured regression tests against prompts and chains
  • Prompt versioning and A/B comparison to measure performance changes across iterations
  • Production monitoring dashboards for latency, cost per run, and error rates of deployed agents
  • Human annotation and feedback tools for labeling trace outputs and building evaluation datasets
  • Native integration with LangChain and LangGraph via Python SDK with zero-config auto-tracing
  • Playground for interactively testing prompt variants against captured real-production traces

Use Cases

  • Developers debugging complex LangChain agents by inspecting each step inputs, outputs, and latency
  • Teams running automated evaluation suites against prompts before deploying updates to production
  • Production engineering teams monitoring LLM application health through latency and cost dashboards
  • ML engineers building evaluation datasets from production traces to measure response quality over time

Pros

  • Native LangChain integration provides automatic full-stack tracing with zero instrumentation code added
  • Dataset evaluation system enables systematic prompt regression testing rather than manual spot checks
  • Free tier with 5,000 traces per month is sufficient for development and most small production applications

Cons

  • Primary value is for LangChain users - teams using other frameworks get less out-of-the-box integration
  • Trace volume pricing scales quickly for high-traffic production applications generating millions of calls
  • Observability concepts like spans and traces add a learning curve for developers new to LLM ops

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