Reasoning

"""Example demonstrating Anthropic's extended thinking feature with thinking blocks."""

import os

from pydantic import SecretStr

from openhands.sdk import (
    LLM,
    Agent,
    Conversation,
    Event,
    LLMConvertibleEvent,
    RedactedThinkingBlock,
    ThinkingBlock,
)
from openhands.sdk.tool import Tool
from openhands.tools.terminal import TerminalTool


# Configure LLM for Anthropic Claude with extended thinking
api_key = os.getenv("LLM_API_KEY")
assert api_key is not None, "LLM_API_KEY environment variable is not set."
model = os.getenv("LLM_MODEL", "anthropic/claude-sonnet-4-5-20250929")
base_url = os.getenv("LLM_BASE_URL")

llm = LLM(
    usage_id="agent",
    model=model,
    base_url=base_url,
    api_key=SecretStr(api_key),
)

# Setup agent with bash tool
agent = Agent(llm=llm, tools=[Tool(name=TerminalTool.name)])


# Callback to display thinking blocks
def show_thinking(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        message = event.to_llm_message()
        if hasattr(message, "thinking_blocks") and message.thinking_blocks:
            print(f"\n🧠 Found {len(message.thinking_blocks)} thinking blocks")
            for i, block in enumerate(message.thinking_blocks):
                if isinstance(block, RedactedThinkingBlock):
                    print(f"  Block {i + 1}: {block.data}")
                elif isinstance(block, ThinkingBlock):
                    print(f"  Block {i + 1}: {block.thinking}")


conversation = Conversation(
    agent=agent, callbacks=[show_thinking], workspace=os.getcwd()
)

conversation.send_message(
    "Calculate compound interest for $10,000 at 5% annually, "
    "compounded quarterly for 3 years. Show your work.",
)
conversation.run()

conversation.send_message(
    "Now, write that number to RESULTs.txt.",
)
conversation.run()
print("✅ Done!")

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")

"""
Example: Responses API path via LiteLLM in a Real Agent Conversation

- Runs a real Agent/Conversation to verify /responses path works
- Demonstrates rendering of Responses reasoning within normal conversation events
"""

from __future__ import annotations

import os

from pydantic import SecretStr

from openhands.sdk import (
    Conversation,
    Event,
    LLMConvertibleEvent,
    get_logger,
)
from openhands.sdk.llm import LLM
from openhands.tools.preset.default import get_default_agent


logger = get_logger(__name__)

api_key = os.getenv("LLM_API_KEY") or os.getenv("OPENAI_API_KEY")
assert api_key, "Set LLM_API_KEY or OPENAI_API_KEY in your environment."

model = "openhands/gpt-5-mini-2025-08-07"  # Use a model that supports Responses API
base_url = os.getenv("LLM_BASE_URL")

llm = LLM(
    model=model,
    api_key=SecretStr(api_key),
    base_url=base_url,
    # Responses-path options
    reasoning_effort="high",
    # Logging / behavior tweaks
    log_completions=False,
    usage_id="agent",
)

print("\n=== Agent Conversation using /responses path ===")
agent = get_default_agent(
    llm=llm,
    cli_mode=True,  # disable browser tools for env simplicity
)

llm_messages = []  # collect raw LLM-convertible messages for inspection


def conversation_callback(event: Event):
    if isinstance(event, LLMConvertibleEvent):
        llm_messages.append(event.to_llm_message())


conversation = Conversation(
    agent=agent,
    callbacks=[conversation_callback],
    workspace=os.getcwd(),
)

# Keep the tasks short for demo purposes
conversation.send_message("Read the repo and write one fact into FACTS.txt.")
conversation.run()

conversation.send_message("Now delete FACTS.txt.")
conversation.run()

print("=" * 100)
print("Conversation finished. Got the following LLM messages:")
for i, message in enumerate(llm_messages):
    ms = str(message)
    print(f"Message {i}: {ms[:200]}{'...' if len(ms) > 200 else ''}")

# Report cost
cost = llm.metrics.accumulated_cost
print(f"EXAMPLE_COST: {cost}")