---
title: "Browse and debug conversations | Grafana Cloud documentation"
description: "Search, filter, and drill into conversations to understand what your agents did, where they failed, and how they performed."
---

> For a curated documentation index, see [llms.txt](/llms.txt). For the complete documentation index, see [llms-full.txt](/llms-full.txt).

# Browse and debug conversations

The Conversations view in Agent Observability lets you search, filter, and inspect full conversation threads. Use it to understand what happened during an agent interaction, identify failures, and analyze performance.

## Search conversations

Go to **Conversations** in Agent Observability. Use the search bar and filters to find conversations by:

- Time range.
- Agent name or version.
- Model provider or name.
- Conversation content.
- Error status.
- Tags or metadata values.
- Evaluation results (pass, fail, or specific evaluator scores).
- Feedback ratings (thumbs up or thumbs down).

Conversations display a summary showing the agent, model, token count, cost, duration, and any evaluation scores. The metrics bar shows evaluation pass, fail, and neutral counts for each conversation.

## Inspect a conversation

Click a conversation to open the detail view. The conversation detail shows:

- A timeline of all generations in the conversation.
- Each generation’s input messages, output messages, tool calls, and tool results.
- Image and video media parts when captured by your SDK.
- Token usage breakdown (input, output, cache read, cache write, reasoning).
- Timing data (start, first token, end).
- Evaluation scores attached to each generation.

Tool calls within a generation are grouped together, and thinking/reasoning content is collapsible. Renderable image and video content appears inline in the thread, generation detail, and tool result cards. Media references that the browser can’t render directly show a placeholder with the stored reference instead. Generations with no captured content show an inline placeholder.

Select the error badge in the metrics bar to jump directly to the first errored generation in the conversation.

## Understand the Dependencies and Workflow tabs

Conversation detail has two graph views for agent structure: **Dependencies** and **Workflow**. They represent different layers of your agent, and you can use both together.

### Inspect the generation DAG in Dependencies

Use **Dependencies** to inspect relationships between LLM calls. Each node is one generation, and edges come from `parent_generation_ids`. You set these parent IDs explicitly in your code; no SDK layer fills them automatically.

This tab is always available. If no generation declares parents, it still shows independent nodes and an inline hint that no dependencies were instrumented. Dependencies work across processes and services because the IDs are just strings you forward over the wire.

### Review the agent execution graph in Workflow

Use **Workflow** to inspect the full orchestration around your LLM calls. Each node is an agent execution step, for example, routing, planning, retrieval, validation, or tool orchestration. A step can wrap zero, one, or many generations. A retrieval step with no LLM call still appears in the graph with its state, duration, and errors.

Steps link to each other with `parent_step_ids` and link to the LLM calls that happened inside them with `linked_generation_ids`. You can build this graph in two ways. Supported framework adapters fill both fields automatically when workflow-step capture is enabled. The core SDK can also send workflow steps manually to represent your custom orchestration. The **Workflow** tab appears only when the conversation includes workflow-step telemetry.

### Choose the right view

Expand table

| Situation                                                            | Recommended view                                                                                                              |
|----------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------|
| Using a framework adapter with workflow-step support                 | Enable workflow-step capture. **Workflow** shows the full graph automatically.                                                |
| Building your own orchestration without a framework                  | Send workflow steps manually with the core SDK. **Workflow** shows your custom execution graph.                               |
| Calling LLMs directly via provider wrappers with no framework        | Forward `parent_generation_ids` between calls. **Dependencies** shows the generation DAG.                                     |
| Connecting calls across different services or processes              | Pass the upstream generation ID over the wire and set it as the parent on the next call. **Dependencies** stitches the graph. |
| Non-LLM work (vector search, ranking, routing) that you want visible | Use **Workflow**. A step with zero generations still appears in the graph. Dependencies can’t show non-LLM nodes.             |

You can combine both: **Workflow** gives the high-level execution path, while **Dependencies** gives the generation-level DAG for debugging multi-agent handoffs and downstream quality signals.

## Drill down into traces

Each generation links to its OpenTelemetry trace. Select the trace link to open the trace view in Tempo, where you can inspect the full span tree including:

- LLM call duration and error status.
- Tool execution spans.
- Nested agent invocations.

Use trace drilldown to identify latency bottlenecks, understand tool call sequences, and diagnose timeout or error scenarios.

## Provide feedback

Use feedback to capture human judgment about a conversation. Users with the Feedback Writer or Admin role can add ratings and annotations from the conversation detail view.

Use the feedback controls in the conversation header to mark a conversation as good or bad and optionally add context. Ratings appear in the conversation list and detail header so reviewers can quickly find conversations that need attention.

## Annotate conversations

Open the **Annotations** panel from the conversation detail view to add threaded notes. Annotations support key-value tags and are visible to all team members with the Feedback Writer or Admin role. Use annotations to:

- Flag conversations for review.
- Add context about specific agent behaviors.
- Track investigation notes alongside the conversation data.

The conversation list shows an annotation count badge, and you can browse annotated conversations from a dedicated pane in the conversation browser.

## Save conversations

Save conversations that you want to review later or reuse in evaluation workflows. Saved conversations appear in the **Saved** tab in the conversation browser.

Organize saved conversations into collections. Collections let you group representative examples, failure cases, or review sets that you can [reuse in offline SDK experiments](/docs/grafana-cloud/observe-and-act/agent-observability/guides/experiments).

## Next steps

- [Use the agent catalog](/docs/grafana-cloud/observe-and-act/agent-observability/guides/agent-catalog)
- [Use built-in dashboards](/docs/grafana-cloud/observe-and-act/agent-observability/guides/dashboards)