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Introduction

The Agents Module provides comprehensive functionality for managing AI agents, creating tasks, and executing workflows within the xpander.ai platform.

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Overview

In this module, you can:
  • List, retrieve, and manage multiple AI agents
  • Load specific agents and interact with their components
  • Manage tasks, tools, and knowledge bases associated with each agent
  • Access configurable elements like MCP (Model Context Protocol) servers

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Examples

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Listing all Agents

This example demonstrates how to list all available agents in both asynchronous and synchronous styles.

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Asynchronous Example

Using alist() to retrieve agents asynchronously: 
from xpander_sdk import Agents

agents = Agents()
agents_list = await agents.alist()
for agent in agents_list:
    print(f"Agent: {agent.name} (ID: {agent.id})")

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Synchronous Example

Using list() to retrieve agents synchronously: 
from xpander_sdk import Agents

agents = Agents()
agents_list = agents.list()
for agent in agents_list:
    print(f"Agent: {agent.name} (ID: {agent.id})")

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Loading a Specific Agent

Learn how to load a specific agent by its ID, which allows you to interact with its tools and tasks.

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Asynchronous Example

agent = await agents.aget("agent-123")
print(f"Agent name: {agent.name}")

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Synchronous Example

agent = agents.get("agent-123")
print(f"Agent name: {agent.name}")

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Creating and Managing Tasks

This section illustrates creating, managing, and executing tasks assigned to agents.

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Asynchronous Example

task = await agent.acreate_task(
    prompt="Analyze this dataset",
    file_urls=["https://example.com/data.csv"],
    events_streaming=True,
    output_format=OutputFormat.Json
)

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Synchronous Example

task = agent.create_task(
    prompt="Analyze this dataset",
    file_urls=["https://example.com/data.csv"],
    events_streaming=True,
    output_format=OutputFormat.Json
)
For more details on task management, see the Tasks API Reference.
Continue to the Agents API Reference for detailed documentation on classes and methods.