LangGraph vs LangSmith

Detailed side-by-side comparison to help you choose the right tool

LangGraph

Agent Frameworks

Graph-based stateful orchestration runtime for agent loops.

Starting Price

Custom

LangSmith

Monitoring & Observability

Tracing, evaluation, and observability for LLM apps and agents.

Starting Price

Custom

Feature Comparison

FeatureLangGraphLangSmith
CategoryAgent FrameworksMonitoring & Observability
Pricing Plans19 tiers16 tiers
Starting Price
Key Features
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling
  • Workflow Runtime
  • Tool and API Connectivity
  • State and Context Handling

LangGraph - Pros & Cons

Pros

  • State-machine approach provides fine-grained control over agent flows
  • Tight integration with the broader LangChain ecosystem
  • Built-in persistence for durable, long-running workflows
  • Cloud deployment option via LangSmith for production scale
  • Supports cyclic graphs enabling iterative agent reasoning

Cons

  • Tightly coupled to LangChain — harder to use standalone
  • Graph-based paradigm has a learning curve for new developers
  • Cloud features require a LangSmith subscription
  • Verbose configuration for simple linear workflows

LangSmith - Pros & Cons

Pros

  • Best-in-class LLM tracing and debugging platform
  • Deep integration with LangChain ecosystem
  • Powerful evaluation and testing workflows for prompt development
  • Dataset management for building evaluation harnesses
  • Visual trace viewer makes debugging complex chains intuitive

Cons

  • Most valuable when used with LangChain — less useful standalone
  • Paid plans required for team features and higher volume
  • Data sent to LangSmith's servers — privacy considerations
  • Can add overhead to development workflow

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