Enhance Weave With Log Agent Data

by Felix Dubois 34 views

Hey guys! Today, we're diving deep into an exciting feature enhancement for Weave, focusing on logging agent-relevant data. This is a crucial step in making Weave even more insightful and powerful. Let's explore the problem, the proposed solution, and how it can transform the way we use Weave.

The Problem: Missing Agent Insights

Currently, Weave lacks detailed logging of agent-specific data. This absence of granular data makes it challenging to understand how agents are performing, which tools they are utilizing, and the overall efficiency of their operations. Imagine trying to optimize a complex system without knowing which components are working well and which are bottlenecks. That's the situation we're in now, and it's why this enhancement is so vital. We need comprehensive agent data to truly unlock Weave's potential.

Specifically, we're missing key metrics such as:

  • The number of tools an agent has access to.
  • Which specific tools are being used most frequently.
  • The total number of tool calls made by an agent.
  • The number of distinct calls, highlighting the variety of tasks an agent handles.
  • The number of tokens per call, indicating the complexity of the interactions.
  • The number of tokens generated, reflecting the agent's output volume.

Without these data points, we're flying blind. We can't effectively analyze agent behavior, identify areas for improvement, or even accurately assess the cost associated with different agent activities. This lack of visibility hinders our ability to optimize Weave for peak performance and efficiency. Therefore, capturing and logging this agent-relevant data is paramount.

The Solution: Logging Agent Data in on_sample_end

The proposed solution involves leveraging the on_sample_end hook within Weave. This hook provides an ideal opportunity to capture and log agent-related data at the end of each sample execution. By inspecting the SampleEnd object, we can extract valuable metrics and log them for analysis. This approach ensures that we capture data consistently and comprehensively, providing a rich dataset for understanding agent behavior. The on_sample_end hook acts as a strategic checkpoint for gathering agent performance metrics.

Here’s how it works:

  1. Intercepting SampleEnd Objects: The on_sample_end hook is triggered whenever a sample execution completes. This provides a natural point to inspect the results and log relevant information.
  2. Data Extraction: Within the hook, we can examine the SampleEnd object, which contains a wealth of data about the execution, including agent-related metrics.
  3. Logging Useful Data: We'll extract key metrics such as the number of tools used, the frequency of tool calls, and token usage. These metrics provide insights into the agent's activity and resource consumption.
  4. Centralized Logging: To ensure organization and ease of analysis, all agent-related data will be logged under a dedicated tag, such as `