> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fact0.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Observability Hub

> LLM call analytics, tool execution metrics, error rates, and conversation sessions — all derived from your agent's execution telemetry.

# Observability Hub

The Observability Hub aggregates the execution telemetry your agents emit via the Fact0 SDK (or OpenTelemetry) into four views: **LLM Calls**, **Tool Executions**, **Error Analytics**, and **Conversations**. All metrics are scoped to the last 30 days by default.

<Note>
  Metrics populate from execution spans. If you haven't integrated the SDK yet, start with the [Quickstart](/quickstart) or the [SDK overview](/sdk/overview).
</Note>

***

## How data gets here

Every time your agent completes a model call or tool invocation, the SDK emits a **span** to Fact0. The Observability Hub reads those spans and aggregates them server-side:

```python theme={null}
import fact0, os

client = fact0.Client(api_key=os.environ["FACT0_API_KEY"])

with client.telemetry.execution(agent_id="my-agent") as ex:

    # This span produces an LLM Calls data point
    with ex.span("gpt-4o call", span_type="MODEL_INVOCATION") as s:
        s.complete(model_invocation={
            "model_name": "gpt-4o",
            "model_provider": "openai",
            "prompt_tokens": 512,
            "completion_tokens": 128,
            "latency_ms": 870,
        })

    # This span produces a Tool Executions data point
    with ex.span("search", span_type="TOOL_CALL") as s:
        s.complete(tool_call={"tool_name": "web_search", "duration_ms": 340})
```

If you use OpenTelemetry, `gen_ai.*` semantic convention attributes are automatically classified as `MODEL_INVOCATION` and tool spans as `TOOL_CALL` — no code changes needed. See [OpenTelemetry integration](/integrations/opentelemetry).

***

## LLM Calls tab

Aggregated statistics for all `MODEL_INVOCATION` spans over the selected period.

| Metric                        | Description                             |
| ----------------------------- | --------------------------------------- |
| Total calls                   | Raw span count                          |
| Success rate                  | Spans without error / total             |
| Avg / P50 / P95 / P99 latency | Percentile latency in ms                |
| Prompt tokens                 | Input tokens consumed                   |
| Completion tokens             | Output tokens generated                 |
| Estimated cost (USD)          | Calculated from provider pricing tables |

**By model** — a per-model breakdown of call count, token usage, latency, error count, and estimated cost across all providers your agents use.

**Call frequency chart** — hourly/daily bucketed call volume over time, useful for spotting traffic spikes or agent regressions.

***

## Tool Executions tab

Aggregated statistics for all `TOOL_CALL` spans.

| Metric       | Description                 |
| ------------ | --------------------------- |
| Total calls  | Raw span count              |
| Success rate | Spans without error / total |
| Avg duration | Mean execution time in ms   |

**By tool** — per-tool breakdown of call count, duration, error count, and success rate. Useful for identifying slow or unreliable tools.

***

## Error Analytics tab

Error frequency derived from spans with `has_error = true`.

* **By model** — which LLM providers are producing errors and at what rate
* **By tool** — which tools fail most often
* **Time series** — error count over time to track regressions or incidents

***

## Conversations tab

Conversations groups related agent executions into **sessions** — a higher-level view of multi-turn agent interactions with a user or system.

A session is identified by a `session_id` metadata field you pass when starting an execution:

```python theme={null}
with client.telemetry.execution(
    agent_id="support-bot",
    metadata={"session_id": "sess_abc123"},
) as ex:
    ...
```

Each session card shows:

* Agent name and session ID
* Turn count (number of executions in the session)
* Total tokens consumed and estimated cost
* Session status (`active` / `completed`)

Selecting a session opens a **turn trace** view showing each execution in sequence, with its LLM calls, tool calls, token counts, latency, and cost.

***

## Prompt Registry

The **Prompt Registry** stores versioned prompt templates your agents fetch at runtime, keeping prompts auditable and out of application code. See the [Prompt Registry guide](/observability/prompt-registry) for full details.

***

## Date range

All tabs respect a shared time range (default: last 30 days). Pass `from` and `to` as ISO 8601 timestamps if querying the API directly:

```
GET /v1/me/analytics/llm?from=2026-06-01T00:00:00Z&to=2026-06-30T23:59:59Z
```