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

# Agent SDK

> Build agents, teams, and workflows using the Agno SDK.

Agno is a Python SDK for building agent platforms. It gives you three primitives (agents, teams and workflows) and a large set of capabilities you can attach to them.

<CodeGroup>
  ```python Agent theme={null}
  from agno.agent import Agent
  from agno.db.sqlite import SqliteDb
  from agno.tools.workspace import Workspace

  workbench = Agent(
      name="Workbench",
      model="openai:gpt-5.5",
      db=SqliteDb(db_file="workbench.db"),
      tools=[Workspace(".")],
      enable_agentic_memory=True,
      add_history_to_context=True,
      num_history_runs=3,
      markdown=True,
  )

  workbench.print_response("Inventory this folder.")
  ```

  ```python Team theme={null}
  from agno.agent import Agent
  from agno.team import Team
  from agno.tools.yfinance import YFinanceTools

  bull = Agent(
      name="Bull",
      model="openai:gpt-5.4",
      role="Make the case FOR investing.",
      tools=[YFinanceTools()],
  )
  bear = Agent(
      name="Bear",
      model="openai:gpt-5.4",
      role="Make the case AGAINST investing.",
      tools=[YFinanceTools()],
  )

  team = Team(
      name="Investment Committee",
      members=[bull, bear],
      instructions="Hear both sides, then synthesize a balanced recommendation.",
  )

  team.print_response("Should I invest in NVIDIA?")
  ```

  ```python Workflow theme={null}
  from agno.agent import Agent
  from agno.team import Team
  from agno.tools.yfinance import YFinanceTools
  from agno.workflow import Step, Workflow

  researcher = Agent(
      model="openai:gpt-5.4",
      tools=[YFinanceTools()],
      instructions="Gather raw market data.",
  )

  bull = Agent(model="openai:gpt-5.4", role="Make the case FOR investing.")
  bear = Agent(model="openai:gpt-5.4", role="Make the case AGAINST investing.")
  committee = Team(
      name="Investment Committee",
      members=[bull, bear],
      instructions="Debate the position.",
  )

  writer = Agent(
      model="openai:gpt-5.4",
      instructions="Write a 200-word investment brief.",
  )

  workflow = Workflow(
      name="Stock Research",
      steps=[
          Step(name="Research", agent=researcher),
          Step(name="Debate", team=committee),
          Step(name="Report", agent=writer),
      ],
  )

  workflow.print_response("Analyze NVIDIA for investment.")
  ```
</CodeGroup>

## Primitives

| Primitive                       | Description                                                                                                        |
| ------------------------------- | ------------------------------------------------------------------------------------------------------------------ |
| [Agent](/agents/overview)       | Model-driven programs with tools and instructions                                                                  |
| [Team](/teams/overview)         | Multiple agents working together as a team                                                                         |
| [Workflow](/workflows/overview) | DAG-based orchestration across agents, teams, and functions. Supports linear steps, loops, branches, parallel work |

## Capabilities

### Model and tools

| Capability                               | What it adds                                            |
| ---------------------------------------- | ------------------------------------------------------- |
| [Models](/models/overview)               | 30+ providers behind one API                            |
| [Tools](/tools/overview)                 | 100+ integrations and the ability to write your own     |
| [Skills](/skills/overview)               | Composable abilities you can attach to agents and teams |
| [Multimodal](/multimodal/overview)       | Image, audio, and video input and output                |
| [Structured I/O](/input-output/overview) | Type-safe input and output with Pydantic schemas        |

### Memory and context

| Capability                                       | What it adds                                                             |
| ------------------------------------------------ | ------------------------------------------------------------------------ |
| [Storage](/database/overview)                    | Durability and persistence with supported database backends              |
| [Sessions](/sessions/overview)                   | Multi-turn session management with summaries, history, and metrics       |
| [State](/state/overview)                         | Session and agentic state agents can read and update mid-run             |
| [Memory](/memory/overview)                       | Store facts about each user and recall them in later conversations       |
| [Knowledge](/knowledge/overview)                 | Search over documents, URLs, and databases                               |
| [Learning](/learning/overview)                   | Agents that improve over time with learned behavior and decisions        |
| [Compression](/compression/overview)             | Compress tool call results to save context space                         |
| [Context Providers](/context-providers/overview) | Inject live data from Calendar, Gmail, Drive, Slack, Wiki, MCP, and more |

### Control and safety

| Capability                          | What it adds                                                  |
| ----------------------------------- | ------------------------------------------------------------- |
| [Guardrails](/guardrails/overview)  | Input validation, PII detection, and prompt injection defense |
| [Hooks](/hooks/overview)            | Lifecycle hooks for input, output, and state                  |
| [Human-in-the-Loop](/hitl/overview) | Pause runs for approval, input, or external execution         |

### Operations

| Capability                                             | What it adds                                                                  |
| ------------------------------------------------------ | ----------------------------------------------------------------------------- |
| [Background execution](/background-execution/overview) | Continue long-running work after the initial API request returns              |
| [Evals](/evals/overview)                               | Measure accuracy, performance, and reliability; agent-as-judge                |
| [Observability](/observability/overview)               | Tracing with Langfuse, Logfire, Arize, The Context Company, and 12+ providers |
| [Scheduler](/scheduler/overview)                       | Run agents, teams, and workflows on recurring schedules                       |

## Components

Agents, teams, and workflows become runnable **components** once you add their models, tools, state, and configuration. Code-defined components stay in Python. Components created in Studio or through the `/components` API use draft and published versions, with a `current` version that you can promote or roll back.

### Versioned components

When components are created via the API, they carry a versioned configuration. Published versions are immutable, and run requests accept a `version` parameter so you can pin clients to a specific version. A `current` pointer decides which version your production API serves: set it to a newer version to promote, or an earlier one to roll back.

Tune a component's instructions, model, or tools and publish the change as a new version. Promote the new version or roll back to an earlier version based on its results.

## Learn more

<CardGroup cols={3}>
  <Card title="SDK Introduction" icon="rocket" href="/sdk/introduction">
    Agents, teams, and workflows in pure Python.
  </Card>

  <Card title="Build Agents" icon="users" href="/agents/overview">
    Create agents with models, tools, and instructions.
  </Card>

  <Card title="Build Agent Platform" icon="layer-group" href="/agent-platform/overview">
    Assemble a full platform on the AgentOS runtime.
  </Card>
</CardGroup>