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Overview

Agentic agents use an agentic ACP architecture that:
  • Supports both streaming and non-streaming responses
  • Requires explicit task creation before sending events
  • Ideal for complex workflows and long-running operations

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Key API Method: send_event()

For agentic (ACP) agents, you always use the send_event() method to communicate with the agent. This is different from sync agents, which use send_message() for direct communication. 
# Agentic agents use send_event()
# 1. Create a task first (REQUIRED for agentic agents)
task = await client.agents.create_task(agent_id=agent.id, params={})

# 2. Send events to the task
await client.agents.send_event(
    agent_id=agent.id,
    params={
        "task_id": task.id,
        "content": {"type": "text", "author": "user", "content": "Hello!"}
    }
)
Key Differences from Sync Agents:
  • Task creation required - Must create a task before sending events (sync agents don’t need this)
  • Event-based communication - Send events to tasks, not direct messages to agent names
  • Asynchronous processing - Events are processed asynchronously, poll or stream for responses
The event-based model enables asynchronous processing, state management across multiple turns, and complex workflow orchestration.

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ACP Types: Base vs Temporal

Agentic agents can be implemented using two different backend types:
  • Base ACP - Simple event-driven architecture, suitable for learning and simple use cases
  • Temporal - Robust workflow engine for production, handles race conditions automatically
Important: From the client’s perspective, both base ACP and Temporal agents use the exact same API (both use send_event()). The choice between base and Temporal is purely a server-side implementation detail that is transparent to client code. You can switch an agent from base to Temporal (or vice versa) without changing any client code.

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Setup

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Prerequisites

pip install agentex-sdk

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Environment Variables

export AGENTEX_API_KEY="your-api-key"
export AGENTEX_BASE_URL="agentex-base-url"
export SGP_ACCOUNT_ID="your-account-id"

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Initialize the Client

import asyncio
from agentex import AsyncAgentex
from agentex.types.text_content import TextContent

# Initialize the async AgentEx client
client = AsyncAgentex(
    base_url=os.environ.get("AGENTEX_BASE_URL"),
    default_headers={
        "x-api-key": os.enviorn.get("AGENTEX_API_KEY"),
        "x-selected-account-id": os.environ.get("SGP_ACCOUNT_ID")
    }
)

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Basic Usage

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1. Get Agent Information

from agentex.types import Agent

# List all agents
agents = await client.agents.list()
for agent in agents:
    print(f"Agent: {agent.name} - {agent.id} - {agent.acp_type}")

# Retrieve a specific agent by name
AGENT_NAME = "<your-agent-name>"  # Replace with your agent name
agent = await client.agents.retrieve_by_name(agent_name=AGENT_NAME)

print(f"Agent ID: {agent.id}")
print(f"Agent Type: {agent.acp_type}")
print(f"Description: {agent.description}")

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2. Create a Task

Agentic agents require a task to be created before sending events: 
import uuid

# Create a new task
create_task_response = await client.agents.create_task(
    agent_id=agent.id,
    params={
        "name": f"{uuid.uuid4()}-task",
        "params": {}
    }
)

task = create_task_response.result

print(f"Task ID: {task.id}")
print(f"Task Status: {task.status}")

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3. Send an Event

# Create event content
event_content = TextContent(
    type="text",
    author="user",
    content="Hello! Can you explain what quantum computing is?"
)

# Send the event to the task
event_response = await client.agents.send_event(
    agent_id=agent.id,
    params={
        "task_id": task.id,
        "content": event_content
    }
)

event = event_response.result
print(f"Event ID: {event.id}")
print(f"Event created at: {event.created_at}")

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4. Receive Messages (Polling)

Since agentic agents process events asynchronously, you need to poll for messages: 
from datetime import datetime

has_received_response = False
messages = []
timeout = 30
start_time = datetime.now()

# Poll for messages
while not has_received_response and (datetime.now() - start_time).seconds < timeout:
    messages = await client.messages.list(task_id=task.id)

    for message in messages:
        content = message.content
        if content.author == "agent":
            has_received_response = True
            break

    await asyncio.sleep(1)

# Print received messages
print(f"\nReceived {len(messages)} messages:\n")
for message in messages:
    if message.content and message.content.type == "text":
        print(f"[{message.content.author}]: {message.content.content}")
        print("-" * 80)

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Streaming Messages

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Helper Function for Streaming

Use the built-in helper to subscribe to task messages when testing in a local environmet: 
from agentex.lib.utils.dev_tools import subscribe_to_async_task_messages

async def send_and_stream_event(task_id: str, user_message: str):
    """Send an event and stream the agent's response."""

    # Send the event
    event_content = TextContent(
        type="text",
        author="user",
        content=user_message
    )

    await client.agents.send_event(
        agent_id=agent.id,
        params={
            "task_id": task_id,
            "content": event_content
        }
    )

    print(f"User: {user_message}\n")

    # Subscribe to task messages
    messages = await subscribe_to_async_task_messages(
        client=client,
        task_id=task_id,
        timeout=30,
        max_retries=10,
    )

    return messages

# Usage
await send_and_stream_event(
    task.id,
    "Can you tell me about machine learning?"
)

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Custom Streaming Implementation

For more control over streaming or for production environments, implement custom streaming logic: 
import json

async def custom_send_stream_event(task_id: str, user_message: str):
    """Send an event and stream the agent's response with custom logic."""

    # Send the event
    event_content = TextContent(
        type="text",
        author="user",
        content=user_message
    )

    await client.agents.send_event(
        agent_id=agent.id,
        params={
            "task_id": task_id,
            "content": event_content
        }
    )

    print(f"User: {user_message}\n")
    print("Agent: ", end="", flush=True)

    # Stream events
    async with client.tasks.with_streaming_response.stream_events(
        task_id=task_id,
        timeout=30
    ) as stream:
        try:
            response_message = ""

            async for task_message_update in stream.iter_lines():
                if task_message_update.startswith("data: "):
                    # Remove 'data: ' prefix
                    task_message_update_json = task_message_update.strip()[6:]
                    task_message_update_data = json.loads(task_message_update_json)

                    if task_message_update_data.get("type") == "delta":
                        delta = task_message_update_data.get("delta", {})
                        if delta.get("type") == "text":
                            text_delta = delta.get("text_delta", "")
                            response_message += text_delta
                            print(text_delta, end="", flush=True)

                    elif task_message_update_data.get("type") == "done":
                        print("\n")
                        break

        except Exception as e:
            print(f"\nError streaming messages: {e}")

# Usage
await custom_send_stream_event(
    task.id,
    "Can you tell me about tennis?"
)

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Multi-Turn Conversations

Agentic agents automatically maintain conversation history: 
# Create a new task for the conversation
conversation_task_id = str(uuid.uuid4())
create_task_response = await client.agents.create_task(
    agent_id=agent.id,
    id=conversation_task_id,
    params={}
)

conversation_task = create_task_response.result

# Define a multi-turn conversation
messages = [
    "Can you tell me about machine learning?",
    "What are some practical applications?",
    "How do I get started learning it?",
]

# Send each message and stream responses
for message in messages:
    print(f"Task ID: {conversation_task.id}")
    await custom_send_stream_event(conversation_task.id, message)
    print("=" * 80 + "\n")

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View Conversation History

Retrieve all messages from a task: 
# Get all messages from the task
all_messages = await client.messages.list(task_id=conversation_task.id)

print("\n" + "=" * 80)
print("COMPLETE CONVERSATION HISTORY")
print("=" * 80 + "\n")

for idx, message in enumerate(all_messages, 1):
    if message.content and message.content.type == "text":
        author = message.content.author.upper()
        print(f"{idx}. [{author}]")
        print(f"{message.content.content}\n")
        print("-" * 80)

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Managing Tasks

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Check Task Status

# Retrieve task status
task_info = await client.tasks.retrieve(task_id=task.id)

print(f"Task ID: {task_info.id}")
print(f"Status: {task_info.status}")
print(f"Status Reason: {task_info.status_reason}")
print(f"Created At: {task_info.created_at}")
print(f"Updated At: {task_info.updated_at}")

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List All Tasks for an Agent

# List tasks
tasks = await client.tasks.list(agent_id=agent.id)

for task in tasks:
    print(f"Task {task.id}: {task.status}")

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State Management

Agentic agents can maintain state across events: 
# List states for an agent
states = await client.states.list(agent_id=agent.id)

for state in states:
    print(f"State ID: {state.id}")
    print(f"Task ID: {state.task_id}")
    print(f"State Data: {state.state}")