Evaluating Deep Agents using LangSmith on AWS

This article presents a practical guide to evaluating deep agents using LangSmith on AWS, combining learnings from LangChain and Anthropic. The guide covers five evaluation patterns and provides a method for building offline evaluations using pytest and LangSmith. Engineers can use this approach to assess the performance of their deep agents in a controlled environment. However, they should be aware that this approach may not capture real-world complexities and edge cases. This guide is particularly useful for ML Engineers looking to optimize their deep agents' performance.

⚡ Key Takeaways

• Five evaluation patterns are provided for deep agents.
• LangSmith is used for building offline evaluations.
• Pytest is used for testing and evaluation.
• Engineers need to consider the tradeoff between offline evaluations and real-world performance.
• The LangSmith API is used for building evaluations.
• Limitation, caveat, or prerequisite: This guide assumes familiarity with LangChain, Anthropic, and pytest.
• WhyItMatters: Evaluating deep agents is crucial for optimizing their performance and ensuring they meet production requirements. This guide provides a practical approach to evaluating deep agents using LangSmith on AWS.
• TechnicalLevel: Intermediate
• TargetAudience: ML Engineers
• PracticalSteps:
• Install LangSmith and pytest using pip.
• Import LangSmith and pytest in your Python script.
• Define evaluation patterns and use LangSmith to build offline evaluations.
• ToolsMentioned: LangSmith, pytest, LangChain, Anthropic
• Tags: LLM, RAG, LANGCHAIN

🔧 Tools & Libraries

Evaluating deep agents is crucial for optimizing their performance and ensuring they meet production requirements. This guide provides a practical approach to evaluating deep agents using LangSmith on AWS.

✅ Practical Steps

1. Install LangSmith and pytest using pip.
2. Import LangSmith and pytest in your Python script.
3. Define evaluation patterns and use LangSmith to build offline evaluations.

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