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LangSmith has launched over 30 evaluation templates, so quality checks for AI agents no longer need to be written from scratch.
Assessing whether an AI agent is "useful" is currently one of the most time-consuming parts of development.
Agents may call the correct tools but produce incorrect response formats, perform normally in single-turn conversations but crash in multi-turn dialogues, or produce seemingly reasonable answers but retrieve incorrect documents in the intermediate steps.
Developers need to set checkpoints at multiple levels—single steps, complete trajectories, multi-turn conversations, specific tool calls—and each evaluator must go through writing prompts, calibrating with real data, and repeated tuning.
Starting from scratch often takes several weeks.
LangSmith now offers over 30 ready-made templates covering five categories: safety and protection (prompt injection detection, personal information leakage checks, bias and toxicity), response quality (accuracy, usefulness, tone), execution trajectory (whether the agent followed the correct steps), user behavior analysis (language distribution, satisfaction signals), and multimodal (voice and image output review).
The templates include fine-tuned LLM evaluation prompts and rule-based code evaluators, which can be used directly or customized, suitable for both online monitoring and offline experiments.
Reusable evaluators address organizational management issues: the newly added Evaluators tab centrally displays all evaluators within the workspace, allows one-click deployment to new projects, and updates to prompts take effect globally without maintaining duplicate copies in each project.
The above templates are open-sourced simultaneously with the release of openevals v0.2.0, which adds support for multimodal evaluation.
(Source: BlockBeats)