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Documentation Index

Fetch the complete documentation index at: https://whyops.com/docs/llms.txt

Use this file to discover all available pages before exploring further.

The core visual feature of the WhyOps dashboard is the Decision Graph. When you navigate to a specific Trace in your WhyOps Project, you won’t see a flat list of logs. Instead, you’ll see a Directed Acyclic Graph (DAG) that maps out exactly how your agent arrived at its final conclusion.

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Why Graphs?

Autonomous agents do not execute linearly. They:
  • Think (Chain of Thought).
  • Plan.
  • Execute tools.
  • Retry when tools fail.
  • Compile multiple pieces of context into a final answer.
A traditional log view forces you to read through hundreds of JSON lines to figure out what happened when. A graph shows you the causality immediately.

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Reading the Graph

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Nodes

Each node in the graph represents a specific Event that occurred during the trace.
  • User Message: The starting point. The prompt that kicked off the trace.
  • LLM Call: Nodes representing a request to an LLM provider.
  • Thinking: Intermediate reasoning steps (like Anthropic’s extended thinking blocks).
  • Tool Execution: Nodes representing a call to an external tool or database.
  • Errors: Highlighted in red, representing a failure (e.g., an upstream timeout or a tool crash).

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Edges

The lines connecting the nodes represent causality and data flow.
  • A solid line indicates chronological and causal flow (e.g., LLM requested a tool → Tool executed).
  • If your agent uses complex frameworks that fan-out (running multiple tools in parallel), you will see the graph branch out and then converge back into the next LLM call.

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Interactions

  • Pan and Zoom: The graph is fully interactive. You can drag the canvas to explore massive multi-step reasoning traces.
  • Node Selection: Clicking on any node in the graph opens the Trace Inspector pane on the right side of the screen, revealing the deep technical details of that specific step.
  • Layout Management: Powered by reactflow and dagre, the layout automatically organizes itself to minimize crossing edges, making even the most complex traces readable.

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Detecting Loops visually

Because we render traces as graphs, infinite retry loops become instantly obvious. If your agent calls a search tool, gets an error, and calls the exact same search tool 5 times in a row, you will see a visual cluster of identical tool execution nodes, often flagged with a red warning badge generated by our Static Analysis engine.