Reasoning

"""Reasoning-capable Agno agent for the reasoning family of demos.Backs three showcase cells:    - reasoning-custom             (custom amber ReasoningBlock slot)    - reasoning-default            (CopilotKit's built-in reasoning card)    - tool-rendering-reasoning-chain (reasoning + sequential tool calls)Mirrors `showcase/integrations/langgraph-python/src/agents/reasoning_agent.py`(shared across the three reasoning demos there).Uses reasoning=False with a custom AGUI handler in agent_server.py thatemits REASONING_MESSAGE_* AG-UI events. The primary channel is Agno'snative `reasoning_content` (chat-completions stream a `delta.reasoning_content`field surfaced on each `RunContentEvent.reasoning_content`), tee'd into theAG-UI reasoning stream; parsing of <reasoning>...</reasoning> XML tags in themodel output remains as a defensive fallback when the native channel is empty.Running with reasoning=False avoids Agno's multi-call CoT loop (which breaksaimock fixtures) while still producing the proper AG-UI events thatCopilotKit's frontend renders via the reasoningMessage slot.For the reasoning-chain demo we also expose the same shared backend tools(`get_weather`, `search_flights`, `get_stock_price`, `roll_dice`) as theprimary agent so the catch-all tool renderer can observe a fullreasoning -> tool call -> reasoning -> tool call chain."""from __future__ import annotationsimport jsonfrom agno.agent.agent import Agentfrom agno.models.openai import OpenAIChatfrom agno.tools import toolfrom dotenv import load_dotenvfrom tools import (    get_weather_impl,    search_flights_impl,)from tools.types import Flightload_dotenv()@tooldef get_weather(location: str):    """    Get the weather for a given location. Ensure location is fully spelled out.    Args:        location (str): The location to get the weather for.    Returns:        str: Weather data as JSON.    """    return json.dumps(get_weather_impl(location))@tooldef search_flights(flights: list[dict]):    """    Search for flights and display the results as rich A2UI cards.    Return exactly 2 flights.    Args:        flights (list[dict]): List of flight objects to display.    Returns:        str: A2UI operations as JSON.    """    typed_flights = [Flight(**f) for f in flights]    result = search_flights_impl(typed_flights)    return json.dumps(result)@tooldef get_stock_price(ticker: str):    """    Get a mock current price for a stock ticker.    Args:        ticker (str): The ticker symbol to look up.    Returns:        str: Mock price data as JSON.    """    from random import choice, randint    return json.dumps(        {            "ticker": ticker.upper(),            "price_usd": round(100 + randint(0, 400) + randint(0, 99) / 100, 2),            "change_pct": round(choice([-1, 1]) * (randint(0, 300) / 100), 2),        }    )@tooldef roll_dice(sides: int = 6):    """    Roll a single die with the given number of sides.    Args:        sides (int): The number of sides on the die. Defaults to 6.    Returns:        str: Dice roll result as JSON.    """    from random import randint    return json.dumps({"sides": sides, "result": randint(1, max(2, sides))})# NOTE: reasoning=False (the default) is used here intentionally.## Agno's reasoning=True triggers a multi-call Chain-of-Thought loop that# makes up to `reasoning_max_steps` sequential LLM calls. This breaks in# proxy/fixture environments (aimock, D5 probes) where only the first# call matches a fixture — subsequent calls don't match and either fall# through to the real API (slow, non-deterministic) or fail entirely.## Instead, the custom AGUI handler in agent_server.py synthesizes# REASONING_MESSAGE_* AG-UI events from the agent's response text. The# system prompt instructs the model to prefix its answer with a reasoning# block delimited by <reasoning>...</reasoning> tags. The custom handler# parses those tags and emits proper AG-UI reasoning events that# CopilotKit's frontend renders via the reasoningMessage slot.## This approach:#   - Works with aimock (single LLM call)#   - Emits proper AG-UI REASONING_MESSAGE_* events (unlike Agno's stock#     AGUI handler which only emits STEP_STARTED/STEP_FINISHED)#   - Keeps the demo visually identical to native reasoning modelsagent = Agent(    model=OpenAIChat(id="gpt-4o-mini", timeout=120),    tools=[get_weather, search_flights, get_stock_price, roll_dice],    reasoning=False,    tool_call_limit=10,    description=(        "You are a helpful assistant. For each user question, first think "        "step-by-step about the approach, then answer concisely. When the "        "question calls for a tool, call it explicitly rather than guessing."    ),    instructions="""        REASONING STYLE:        Always begin your response with a reasoning block wrapped in        <reasoning>...</reasoning> XML tags. Inside the tags, think        step-by-step (two to four short steps is plenty). After the closing        tag, give your concise final answer. Example:        <reasoning>        Step 1: Identify what the user is asking.        Step 2: Consider which tool to use.        Step 3: Formulate the answer.        </reasoning>        Here is my answer...        TOOLS (reasoning-chain cell):        - get_weather: use when the user asks about weather.        - search_flights: use when the user asks about flights. Generate 2          realistic flights. Flight shape: airline, airlineLogo (Google          favicon URL), flightNumber, origin, destination, date          ("Tue, Mar 18"), departureTime, arrivalTime, duration ("4h 25m"),          status ("On Time"|"Delayed"), statusColor (hex), price ("$289"),          currency ("USD").        - get_stock_price: use when the user asks about a ticker. Consider          fetching a second related ticker for comparison.        - roll_dice: use when the user asks to roll a die. Consider rolling          twice with different numbers of sides.    """,)

"""Reasoning-capable Agno agent for the reasoning family of demos.Backs three showcase cells:    - reasoning-custom             (custom amber ReasoningBlock slot)    - reasoning-default            (CopilotKit's built-in reasoning card)    - tool-rendering-reasoning-chain (reasoning + sequential tool calls)Mirrors `showcase/integrations/langgraph-python/src/agents/reasoning_agent.py`(shared across the three reasoning demos there).Uses reasoning=False with a custom AGUI handler in agent_server.py thatemits REASONING_MESSAGE_* AG-UI events. The primary channel is Agno'snative `reasoning_content` (chat-completions stream a `delta.reasoning_content`field surfaced on each `RunContentEvent.reasoning_content`), tee'd into theAG-UI reasoning stream; parsing of <reasoning>...</reasoning> XML tags in themodel output remains as a defensive fallback when the native channel is empty.Running with reasoning=False avoids Agno's multi-call CoT loop (which breaksaimock fixtures) while still producing the proper AG-UI events thatCopilotKit's frontend renders via the reasoningMessage slot.For the reasoning-chain demo we also expose the same shared backend tools(`get_weather`, `search_flights`, `get_stock_price`, `roll_dice`) as theprimary agent so the catch-all tool renderer can observe a fullreasoning -> tool call -> reasoning -> tool call chain."""from __future__ import annotationsimport jsonfrom agno.agent.agent import Agentfrom agno.models.openai import OpenAIChatfrom agno.tools import toolfrom dotenv import load_dotenvfrom tools import (    get_weather_impl,    search_flights_impl,)from tools.types import Flightload_dotenv()@tooldef get_weather(location: str):    """    Get the weather for a given location. Ensure location is fully spelled out.    Args:        location (str): The location to get the weather for.    Returns:        str: Weather data as JSON.    """    return json.dumps(get_weather_impl(location))@tooldef search_flights(flights: list[dict]):    """    Search for flights and display the results as rich A2UI cards.    Return exactly 2 flights.    Args:        flights (list[dict]): List of flight objects to display.    Returns:        str: A2UI operations as JSON.    """    typed_flights = [Flight(**f) for f in flights]    result = search_flights_impl(typed_flights)    return json.dumps(result)@tooldef get_stock_price(ticker: str):    """    Get a mock current price for a stock ticker.    Args:        ticker (str): The ticker symbol to look up.    Returns:        str: Mock price data as JSON.    """    from random import choice, randint    return json.dumps(        {            "ticker": ticker.upper(),            "price_usd": round(100 + randint(0, 400) + randint(0, 99) / 100, 2),            "change_pct": round(choice([-1, 1]) * (randint(0, 300) / 100), 2),        }    )@tooldef roll_dice(sides: int = 6):    """    Roll a single die with the given number of sides.    Args:        sides (int): The number of sides on the die. Defaults to 6.    Returns:        str: Dice roll result as JSON.    """    from random import randint    return json.dumps({"sides": sides, "result": randint(1, max(2, sides))})# NOTE: reasoning=False (the default) is used here intentionally.## Agno's reasoning=True triggers a multi-call Chain-of-Thought loop that# makes up to `reasoning_max_steps` sequential LLM calls. This breaks in# proxy/fixture environments (aimock, D5 probes) where only the first# call matches a fixture — subsequent calls don't match and either fall# through to the real API (slow, non-deterministic) or fail entirely.## Instead, the custom AGUI handler in agent_server.py synthesizes# REASONING_MESSAGE_* AG-UI events from the agent's response text. The# system prompt instructs the model to prefix its answer with a reasoning# block delimited by <reasoning>...</reasoning> tags. The custom handler# parses those tags and emits proper AG-UI reasoning events that# CopilotKit's frontend renders via the reasoningMessage slot.## This approach:#   - Works with aimock (single LLM call)#   - Emits proper AG-UI REASONING_MESSAGE_* events (unlike Agno's stock#     AGUI handler which only emits STEP_STARTED/STEP_FINISHED)#   - Keeps the demo visually identical to native reasoning modelsagent = Agent(    model=OpenAIChat(id="gpt-4o-mini", timeout=120),    tools=[get_weather, search_flights, get_stock_price, roll_dice],    reasoning=False,    tool_call_limit=10,    description=(        "You are a helpful assistant. For each user question, first think "        "step-by-step about the approach, then answer concisely. When the "        "question calls for a tool, call it explicitly rather than guessing."    ),    instructions="""        REASONING STYLE:        Always begin your response with a reasoning block wrapped in        <reasoning>...</reasoning> XML tags. Inside the tags, think        step-by-step (two to four short steps is plenty). After the closing        tag, give your concise final answer. Example:        <reasoning>        Step 1: Identify what the user is asking.        Step 2: Consider which tool to use.        Step 3: Formulate the answer.        </reasoning>        Here is my answer...        TOOLS (reasoning-chain cell):        - get_weather: use when the user asks about weather.        - search_flights: use when the user asks about flights. Generate 2          realistic flights. Flight shape: airline, airlineLogo (Google          favicon URL), flightNumber, origin, destination, date          ("Tue, Mar 18"), departureTime, arrivalTime, duration ("4h 25m"),          status ("On Time"|"Delayed"), statusColor (hex), price ("$289"),          currency ("USD").        - get_stock_price: use when the user asks about a ticker. Consider          fetching a second related ticker for comparison.        - roll_dice: use when the user asks to roll a die. Consider rolling          twice with different numbers of sides.    """,)