View agent activity

Documentation Index

Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt

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

The Agents view gives you a turn-by-turn record of every conversation your agent had, along with token usage, tool invocations, and execution spans. Agent applications are hard to debug because the interesting behavior happens between the user’s request and the final response. The Agents view in W&B Weave makes that middle layer visible. Every conversation your agent had is captured here, with the full message history, span-level execution detail, and token costs attached. You can see at a glance whether an agent completed its task, how many tool calls it made, and where time or budget was spent. For teams building and iterating on agents, this is the starting point for understanding behavior in production.

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Get started

To enter the Agents view:

1. Navigate to https://wandb.ai and select your project.
2. In the sidebar menu, select Agents to view all agent conversations saved for your project.

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Agents tab

The Agents tab gives you a high-level view of all agents that have logged traces to this project. Use it to spot which agents are active, compare latency and error rates across agents, and identify agents that need attention before drilling into individual conversations. It is useful for scenarios such as:

• Monitoring a fleet of agents. The card grid lets you compare latency and error rate across all agents at once without opening individual conversations. A latency spike or a newly red error rate on one card signals a regression worth investigating.
• Identifying stale agents. Sorting by Last seen highlights agents that haven’t recorded activity recently. This is useful for confirming a deployment is live or spotting agents that may have stopped logging traces unexpectedly.
• Comparing versions. The version count on each card tells you how many distinct versions of that agent have been deployed. A high version count alongside a rising error rate may indicate a regression introduced in a recent deployment.
• Drilling into an agent. Click any card to open the detail panel for that agent, from which you can navigate to its conversations or spans:

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Agent cards

Each agent is represented as a card showing:

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Find and sort agents

Use the Search agents field to filter cards by agent name. Use the sort dropdown (default: Last seen) to reorder the grid. The available sort options are:

• Last seen: Most recently active agents first.
• Most invocations: Highest conversation volume first.
• Most input tokens: Highest token consumption first.
• Most errors: Highest error count first.

Sorting by Most errors is useful for a quick daily health check: agents with non-zero error rates surface immediately, and the red error rate on the card confirms at a glance which need investigation.

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Conversations tab

The Conversations tab on the Agents page lets you browse, filter, and inspect individual agent runs. Use it to investigate failures, measure token costs, and understand the sequence of LLM calls and tool executions that made up a run. For high-level questions about what an agent said and did across a conversation, start with the Conversations tab.

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Conversations table

The conversation table shows one row per conversation. The following columns appear by default. To show or hide additional columns, click Columns in the toolbar.

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Filtering and time window

Use the Filter bar to narrow results by agent, model, error status, or other attributes. Use the time window selector (1m, 1h, 6h, 24h, 7d, or 30d) to restrict the list to conversations that were active within that period. The conversation volume histogram above the list updates to reflect the selected window. Hover over any column header in the conversation list to filter that column to a specific value or range.

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Agent conversation detail

Click a conversation row to open a detail panel with two sub-tabs: turns and events.

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Turns

The conversation detail turns panel shows each turn in chronological order, numbered from 1. Each turn displays the number of intermediate responses and tool calls, and the total wall-clock duration. Expand a turn to see the full message thread.

Messages

Within a turn, messages are grouped by role. User messages show the message text and any attached media or content references. Assistant messages show:

• The agent name and the model used (for example, gpt-5.5-2026-04-23).
• Timestamp and duration.
• Input and output token counts (for example, 16086 in 295 out).
• An expandable Reasoning section when the model used extended thinking.
• The response text, which collapses automatically for long responses.

Tool calls show the tool name, timestamp, and duration. If argument or result data is available, the tool call is expandable and shows Args and Result in a key-value table. If the call failed, an ERROR badge appears.

Error states

When a tool call returns an error status, a red ERROR badge appears inline next to it. In the Events timeline, that event also displays in red regardless of its type.

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Events

The Events panel on the right shows a color-coded strip that represents the sequence of events within the selected turn. In the events timeline, each segment’s color indicates the event type. Use the Events timeline to get a quick sense of how a turn was structured. For example, you can see whether it was LLM-heavy, tool-heavy, or involved sub-agent delegation before reading the full message thread.

Scores

If any signals are active for the project, a Scores section provides metrics for the conversation. It shows the signal scorer name, an overall numeric rating from 0 to 1, a confidence percentage, and the individual rubric points that contributed to the score. Each rubric point also shows its own confidence. Use this to understand not just whether a turn scored well, but which specific rubric criteria passed or failed.

Meta summary

The Meta summary section shows aggregate statistics for the selected conversation.

Token breakdown

The Token breakdown section shows cache and reasoning details for the selected conversation.

Participants

The Participants section lists the agents and models involved in the conversation. In multi-agent conversations, different turns may show different model names here.

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Spans tab

The Spans tab shows every individual span recorded across all agent activity in the project. Where the Conversations tab aggregates activity into dialogue-level rows, the Spans tab exposes the raw operations underneath: each LLM call, tool execution, and agent invocation as its own row. Use it to trace exactly which call was slow, which model consumed unexpected tokens, or which tool invocation failed.

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Spans table

The span table shares most columns with the Conversations table (agent, model, tool, token counts, status). Some columns unique to this view are: Additional columns for cache token breakdowns, reasoning tokens, LLM parameters, and W&B run metadata are available through the Columns button. The Spans tab is most useful when you need operation-level precision that the Conversations tab doesn’t provide:

• Identifying expensive calls. Sort by In or Out tokens to find which individual LLM calls are driving cost, rather than seeing totals at the conversation level.
• Debugging a specific operation type. Filter by Operation to isolate all execute_tool spans and check error rates, or all chat spans for a specific model.
• Investigating truncation. Filter Finished by max_tokens to find spans where the model hit its token limit rather than completing normally.
• Correlating with a W&B run. Hidden-by-default columns expose W&B run IDs and run steps, letting you link a specific span back to a training or evaluation run in W&B.

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Trace grouping

Click any row to select its trace and highlight all other spans that share the same trace ID. This shows you the full set of operations that were executed as part of one agent invocation. Grouping here is by trace, not by conversation. This means a single conversation may contain multiple traces if it involved sub-agent delegation.

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Agent invocation detail

Click a row in the Spans table to open a detail panel populated with data from the complete agent invocation. At the top of the detail panel, a flame graph shows the wall-clock position and relative duration of every span in the selected trace. Each span appears as a colored block scaled to its actual duration, positioned at its start time in milliseconds from the beginning of the trace. Use the timeline to:

• Spot the longest operation at a glance. Wide blocks indicate spans that dominated total latency.
• See parallelism. Overlapping blocks indicate spans that executed concurrently rather than sequentially.
• Inspect any span inline. Click a block in the timeline to load that span’s details into view, including its input messages and output messages, token counts, and other metadata.

You can alternatively view the child spans as a hierarchical trace tree by selecting the Show trace tree icon in the detail panel header.

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Signals tab

The Signals tab shows the output of automated behavioral scoring applied to your agent’s turns. Where the Conversations and Spans tabs tell you what happened, Signals tell you how well it happened. They surface quality issues like low-effort responses or hallucinations, and error conditions like rate limiting or bugs, without requiring you to read individual traces.

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Signals table

Each row represents one signal score applied to a turn. The following columns appear by default. Use the time window selector and Filter bar to narrow results by scorer, agent, score range, or time period.

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Manage and edit signals

Select Manage signals to open a drawer that lists all active signals for the project. From there you can toggle signals on or off, delete them, or click the edit icon on any signal to modify it. The signal editor has the following fields: Select + New signal to create a new scorer for your agent. For more information on built-in signals, see Monitor using built-in signals.