or JSONL directly in the web app. You can also use the client to fetch runs for further analysis, to store in your own database, or to share with others. Let's fetch the run traces from the evaluation run.Note: It may be a few moments before all the runs are accessible.runs = client.list_runs(project_name=chain_results["project_name"], execution_order=1)# After some time, these will be populated.client.read_project(project_name=chain_results["project_name"]).feedback_statsConclusion‚ÄãCongratulations! You have successfully traced and evaluated an agent using LangSmith!This was a quick guide to get started, but there are many more ways to use LangSmith to speed up your developer flow and produce better results.For more information on how you can get the most out of LangSmith, check out LangSmith documentation, and please reach out with questions, feature requests, or feedback at support@langchain.dev.PreviousLangSmithNextRun LLMs locallyPrerequisitesLog runs to LangSmithEvaluate Agent1. Create a LangSmith dataset2. Initialize a new agent to benchmark3. Configure evaluation4. Run the agent and evaluatorsReview the test results(Optional) Compare to another promptExporting datasets and runsConclusionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Open In Colab
Open In Colab ->: or JSONL directly in the web app. You can also use the client to fetch runs for further analysis, to store in your own database, or to share with others. Let's fetch the run traces from the evaluation run.Note: It may be a few moments before all the runs are accessible.runs = client.list_runs(project_name=chain_results["project_name"], execution_order=1)# After some time, these will be populated.client.read_project(project_name=chain_results["project_name"]).feedback_statsConclusion‚ÄãCongratulations! You have successfully traced and evaluated an agent using LangSmith!This was a quick guide to get started, but there are many more ways to use LangSmith to speed up your developer flow and produce better results.For more information on how you can get the most out of LangSmith, check out LangSmith documentation, and please reach out with questions, feature requests, or feedback at support@langchain.dev.PreviousLangSmithNextRun LLMs locallyPrerequisitesLog runs to LangSmithEvaluate Agent1. Create a LangSmith dataset2. Initialize a new agent to benchmark3. Configure evaluation4. Run the agent and evaluatorsReview the test results(Optional) Compare to another promptExporting datasets and runsConclusionCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.