Agent Observability

AgentOS comes with a built-in tracing provider that routes every run to your own database. There’s no observability service to sign up for and no tracing API key to manage.

from agno.os import AgentOS

agent_os = AgentOS(
    agents=[agent],
    db=db,
    tracing=True,
)

tracing=True turns on OpenTelemetry instrumentation for agent, team, and workflow runs. Every run produces a trace tree: spans for the LLM call, each tool, team delegation, and workflow step. The database exporter writes the aggregate trace to agno_traces and individual spans to agno_spans.

Where trace data goes

The built-in exporter writes trace records to the database you configure:

Configuration	Trace destination
`AgentOS(db=trace_db, tracing=True)`	`trace_db`
`AgentOS(tracing=True)` with component databases	First database found on a native agent, team, or workflow
`AgentOS(tracing=True)` with no database	Tracing is skipped and AgentOS logs a warning
Custom OpenTelemetry exporter	The exporter destination you configure

Tracing does not change where models and tools send data. An agent can still call external model providers, tools, or exporters. Trace attributes can contain prompts, tool arguments, and model output, so apply the same access and retention controls you use for other sensitive application data.

agno_traces and agno_spans tables in a database client — Traces stored in your own database, viewed in a SQL client

What gets captured

Each record in agno_spans stores the span name, parent, status, timestamps, duration, and an attributes JSON object. The aggregate row in agno_traces stores the run, session, user, and component IDs when present, plus overall status, start and end times, and duration. Traces follow OpenInference semantic conventions, so you can query them directly:

-- Top 10 slowest span types by average duration
SELECT
    name,
    AVG(duration_ms) AS avg_ms,
    COUNT(*) AS calls
FROM ai.agno_spans
GROUP BY name
ORDER BY avg_ms DESC
LIMIT 10;

PostgresDb creates its tables in the ai schema by default (override with PostgresDb(db_schema=...)). Qualify the table as ai.agno_spans or add ai to your search_path.

In the AgentOS UI

The control plane renders the same traces visually. Click a run to see the full tree: LLM hops, tool calls with their inputs and outputs, and sub-agent traces. Filter by user, session, or time range.

Multi-database tracing

Traces are high-volume and write-heavy, with a different cost profile, retention, and access pattern than sessions. For production, route them to a dedicated database by pointing the AgentOS db at it:

from agno.agent import Agent
from agno.db.postgres import PostgresDb
from agno.models.openai import OpenAIResponses
from agno.os import AgentOS

# Each agent keeps its own database
agent_db = PostgresDb(db_url="postgresql+psycopg://primary/...")

# Dedicated database for traces
trace_db = PostgresDb(db_url="postgresql+psycopg://traces/...")

agent = Agent(name="Research Agent", model=OpenAIResponses(id="gpt-5.2"), db=agent_db)

agent_os = AgentOS(
    agents=[agent],
    db=trace_db,   # All traces are written here
    tracing=True,
)

The AgentOS db is where traces land. Keeping it separate isolates trace write volume from your agent data and gives you independent retention and scaling. See Multi-DB tracing for the setup_tracing() variant with batch tuning.

External providers

You can configure OpenTelemetry exporters for Langfuse, LangSmith, Arize, Logfire, MLflow, The Context Company, or another OpenTelemetry endpoint. See the Observability section: Langfuse, LangSmith, Arize, Logfire, MLflow, The Context Company.

Welcome

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Features

Use Cases

Agent Observability

Where trace data goes

What gets captured

In the AgentOS UI

Multi-database tracing

External providers

Developer Resources

​Where trace data goes

​What gets captured

​In the AgentOS UI

​Multi-database tracing

​External providers

​Developer Resources

Where trace data goes

What gets captured

In the AgentOS UI

Multi-database tracing

External providers

Developer Resources