The outcome

Learn the interaction loop using a small task with a clear outcome.

Agno is a Agent platform formerly known as Phidata for Python agents, teams, workflows, memory, knowledge, evaluation, tracing, and AgentOS operations. Building Python agents, specialist teams, and deterministic workflows, then serving and managing them through AgentOS with sessions, memory, knowledge, tracing, evaluation, RBAC, and approvals. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Define the task, users and tenants, model, tools, knowledge, state, memory, output schema, autonomy, approvals, quality, safety, retention, cost, and deployment requirements.

  2. 02

    Start with one typed agent and only add a team when specialization is measured or a workflow when deterministic control, branching, pause, or recovery is required.

  3. 03

    Use a private database and authenticated AgentOS instance, scope user and session IDs, register narrow tools, and test guardrails, human approval, persistence, and structured output.

  4. 04

    Evaluate representative and adversarial runs, inspect traces and stored state, verify tenant isolation and tool authorization, then canary the version and retain rollback and deletion procedures.

Working standard

What good use looks like.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.

Phidata is the former name; current code and documentation use Agno and AgentOS, and older tutorials may reference removed or deprecated APIs. Persistent sessions, memory, knowledge, traces, callable caches, teams, and tools create privacy and cross-user risks. Bind state to authenticated identities, authorize tools independently, use narrow roles and approvals, pin versions, redact logs, cap loops and cost, and test migrations and deletion.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.