Keywords AI

Agenta vs LangSmith

Compare Agenta and LangSmith side by side. Both are tools in the Observability, Prompts & Evals category.

Quick Comparison

Agenta
Agenta
LangSmith
LangSmith
CategoryObservability, Prompts & EvalsObservability, Prompts & Evals
PricingFreemium
Best ForLangChain developers who need integrated tracing, evaluation, and prompt management
Websiteagenta.aismith.langchain.com
Key Features
  • Trace visualization for LLM chains
  • Prompt versioning and management
  • Evaluation and testing suite
  • Dataset management
  • Tight LangChain integration
Use Cases
  • Debugging LangChain and LangGraph applications
  • Prompt iteration and A/B testing
  • LLM output evaluation and scoring
  • Team collaboration on prompt engineering
  • Regression testing for LLM apps

When to Choose Agenta vs LangSmith

LangSmith
Choose LangSmith if you need
  • Debugging LangChain and LangGraph applications
  • Prompt iteration and A/B testing
  • LLM output evaluation and scoring
Pricing: Freemium

About Agenta

Agenta is an open-source platform for prompt engineering, evaluation, and experimentation. It provides a prompt playground, version control for prompts, A/B testing, and evaluation pipelines. Teams can iterate on prompts collaboratively, track experiments, and deploy optimized prompts to production.

About LangSmith

LangSmith is LangChain's observability and evaluation platform for LLM applications. It provides detailed tracing of every LLM call, chain execution, and agent step—showing inputs, outputs, latency, token usage, and cost. LangSmith includes annotation queues for human feedback, dataset management for evaluation, and regression testing for prompt changes. It's the most comprehensive debugging tool for LangChain-based applications.

What is Observability, Prompts & Evals?

Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.

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