Trajectory Evaluators | ğŸ¦œï¸�ğŸ”— Langchain





Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKGet startedIntroductionInstallationQuickstartModulesModel I/â€‹OData connectionChainsMemoryAgentsCallbacksModulesGuidesEvaluationString EvaluatorsComparison EvaluatorsTrajectory EvaluatorsCustom Trajectory EvaluatorAgent TrajectoryExamplesDebuggingDeploymentLangSmithModel ComparisonEcosystemAdditional resourcesGuidesEvaluationTrajectory EvaluatorsTrajectory EvaluatorsğŸ“„ï¸� Custom Trajectory EvaluatorYou can make your own custom trajectory evaluators by inheriting from the AgentTrajectoryEvaluator class and overwriting the evaluateagenttrajectory (and aevaluateagentaction) method.ğŸ“„ï¸� Agent TrajectoryAgents can be difficult to holistically evaluate due to the breadth of actions and generation they can make. We recommend using multiple evaluation techniques appropriate to your use case. One way to evaluate an agent is to look at the whole trajectory of actions taken along with their responses.PreviousPairwise String ComparisonNextCustom Trajectory EvaluatorCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.