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support reasoning
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‎.gitignore‎

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.envrc
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codegen.log
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Brewfile.lock.json
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.DS_Store

‎examples/tutorials/10_agentic/10_temporal/020_state_machine/dev.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"id": "0",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"id": "1",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"id": "2",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Task(id='5d950471-ac85-4847-8102-209d0a79b0a6', created_at=datetime.datetime(2025, 8, 13, 0, 15, 24, 722362, tzinfo=TzInfo(UTC)), name='93ec41a3-task', status='RUNNING', status_reason='Task created, forwarding to ACP server', updated_at=datetime.datetime(2025, 8, 13, 0, 15, 24, 722362, tzinfo=TzInfo(UTC)))\n"
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]
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}
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],
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"source": [
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"# (REQUIRED) Create a new task. For Agentic agents, you must create a task for messages to be associated with.\n",
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"import uuid\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"id": "3",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Event(id='75379175-48cc-4d45-93f5-0e2125e6465d', agent_id='71580630-2e43-4db2-912f-124a80d8aca4', sequence_id=1, task_id='5d950471-ac85-4847-8102-209d0a79b0a6', content=TextContent(author='user', content='Hello tell me the latest news about AI and AI startups', attachments=None, format='plain', style='static', type='text'), created_at=datetime.datetime(2025, 8, 13, 0, 15, 26, 693593, tzinfo=TzInfo(UTC)))\n"
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]
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}
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],
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"source": [
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"# Send an event to the agent\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"id": "4",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ffff; text-decoration-color: #00ffff\">╭───────────────────────── </span><span style=\"color: #00ffff; text-decoration-color: #00ffff; font-weight: bold\">USER</span><span style=\"color: #00ffff; text-decoration-color: #00ffff\"> [08/13/2025 00:15:26] ─────────────────────────╮</span>\n",
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"<span style=\"color: #00ffff; text-decoration-color: #00ffff\">│</span> Hello tell me the latest news about AI and AI startups <span style=\"color: #00ffff; text-decoration-color: #00ffff\">│</span>\n",
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"<span style=\"color: #00ffff; text-decoration-color: #00ffff\">╰──────────────────────────────────────────────────────────────────────────────╯</span>\n",
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"</pre>\n"
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],
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"text/plain": [
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"\u001b[96m╭─\u001b[0m\u001b[96m────────────────────────\u001b[0m\u001b[96m \u001b[0m\u001b[1;96mUSER\u001b[0m\u001b[96m [08/13/2025 00:15:26] \u001b[0m\u001b[96m────────────────────────\u001b[0m\u001b[96m─╮\u001b[0m\n",
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"\u001b[96m│\u001b[0m Hello tell me the latest news about AI and AI startups \u001b[96m│\u001b[0m\n",
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"\u001b[96m╰──────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Streaming timed out after 5 seconds - returning collected messages\n"
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]
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}
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],
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"source": [
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"# Subscribe to the async task messages produced by the agent\n",
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"from agentex.lib.utils.dev_tools import subscribe_to_async_task_messages\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 6,
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"id": "5",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Event(id='f0bbedb7-95db-4708-acc0-e3094f34036a', agent_id='71580630-2e43-4db2-912f-124a80d8aca4', sequence_id=2, task_id='5d950471-ac85-4847-8102-209d0a79b0a6', content=TextContent(author='user', content='I want to know what viral news came up and which startups failed, got acquired, or became very successful or popular in the last 3 months', attachments=None, format='plain', style='static', type='text'), created_at=datetime.datetime(2025, 8, 13, 0, 15, 32, 633013, tzinfo=TzInfo(UTC)))\n"
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]
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}
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],
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"source": [
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"# Send a follow up event to the agent in response to the agent's follow up question\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 7,
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"id": "6",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #00ffff; text-decoration-color: #00ffff\">╭───────────────────────── </span><span style=\"color: #00ffff; text-decoration-color: #00ffff; font-weight: bold\">USER</span><span style=\"color: #00ffff; text-decoration-color: #00ffff\"> [08/13/2025 00:15:32] ─────────────────────────╮</span>\n",
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"<span style=\"color: #00ffff; text-decoration-color: #00ffff\">│</span> I want to know what viral news came up and which startups failed, got <span style=\"color: #00ffff; text-decoration-color: #00ffff\">│</span>\n",
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"<span style=\"color: #00ffff; text-decoration-color: #00ffff\">│</span> acquired, or became very successful or popular in the last 3 months <span style=\"color: #00ffff; text-decoration-color: #00ffff\">│</span>\n",
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"<span style=\"color: #00ffff; text-decoration-color: #00ffff\">╰──────────────────────────────────────────────────────────────────────────────╯</span>\n",
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"\u001b[96m╭─\u001b[0m\u001b[96m────────────────────────\u001b[0m\u001b[96m \u001b[0m\u001b[1;96mUSER\u001b[0m\u001b[96m [08/13/2025 00:15:32] \u001b[0m\u001b[96m────────────────────────\u001b[0m\u001b[96m─╮\u001b[0m\n",
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"\u001b[96m│\u001b[0m I want to know what viral news came up and which startups failed, got \u001b[96m│\u001b[0m\n",
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"\u001b[96m│\u001b[0m acquired, or became very successful or popular in the last 3 months \u001b[96m│\u001b[0m\n",
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"\u001b[96m╰──────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
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]
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"data": {
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"text/html": [
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #008000; text-decoration-color: #008000\">╭──────────────────────── </span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">AGENT</span><span style=\"color: #008000; text-decoration-color: #008000\"> [08/13/2025 00:15:32] ─────────────────────────╮</span>\n",
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"<span style=\"color: #008000; text-decoration-color: #008000\">│</span> Starting deep research process based on your query and follow-up <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n",
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"<span style=\"color: #008000; text-decoration-color: #008000\">│</span> responses... <span style=\"color: #008000; text-decoration-color: #008000\">│</span>\n",
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"<span style=\"color: #008000; text-decoration-color: #008000\">╰──────────────────────────────────────────────────────────────────────────────╯</span>\n",
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"</pre>\n"
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],
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"\u001b[32m╭─\u001b[0m\u001b[32m───────────────────────\u001b[0m\u001b[32m \u001b[0m\u001b[1;32mAGENT\u001b[0m\u001b[32m [08/13/2025 00:15:32] \u001b[0m\u001b[32m────────────────────────\u001b[0m\u001b[32m─╮\u001b[0m\n",
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"\u001b[32m│\u001b[0m Starting deep research process based on your query and follow-up \u001b[32m│\u001b[0m\n",
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"\u001b[32m│\u001b[0m responses... \u001b[32m│\u001b[0m\n",
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"\u001b[32m╰──────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n"
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},
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"metadata": {},
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Streaming timed out after 30 seconds - returning collected messages\n"
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]
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}
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],
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"source": [
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"# Subscribe to the async task messages produced by the agent\n",
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"from agentex.lib.utils.dev_tools import subscribe_to_async_task_messages\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.9"
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"version": "3.12.11"
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"nbformat": 4,

‎pyproject.toml‎

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"pytest>=8.4.0",
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"pytest-asyncio>=1.0.0",
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"scale-gp-beta==0.1.0a20",
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"ipykernel>=6.29.5",
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"ipykernel>=6.29.5",
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"openai>=1.99.9",
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]
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requires-python = ">= 3.12,<4"
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classifiers = [

‎src/agentex/lib/core/services/adk/providers/openai.py‎

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)
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from agentex.types.task_message_content import (
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TextContent,
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ReasoningContent,
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ToolRequestContent,
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)
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content=tool_response_content,
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),
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)
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elif event.item.type == "reasoning_item":
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# Handle reasoning items
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reasoning_item = event.item.raw_item
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reasoning_content = ReasoningContent(
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author="agent",
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summary=[summary.text for summary in reasoning_item.summary],
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content=[content.text for content in reasoning_item.content] if reasoning_item.content else None,
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)
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# Create reasoning content using streaming context (immediate completion)
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async with (
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self.streaming_service.streaming_task_message_context(
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task_id=task_id,
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initial_content=reasoning_content,
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) as streaming_context
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):
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# The message has already been persisted, but we still need to send an update
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await streaming_context.stream_update(
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update=StreamTaskMessageFull(
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parent_task_message=streaming_context.task_message,
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content=reasoning_content,
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),
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)
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elif event.type == "raw_response_event":
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if isinstance(event.data, ResponseTextDeltaEvent):

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