FnCallAgent

from qwen_agent.agents import FnCallAgent

class FnCallAgent(Agent):
    def __init__(
        self,
        function_list: Optional[List[Union[str, Dict, BaseTool]]] = None,
        llm: Optional[Union[Dict, BaseChatModel]] = None,
        system_message: Optional[str] = DEFAULT_SYSTEM_MESSAGE,
        name: Optional[str] = None,
        description: Optional[str] = None,
        files: Optional[List[str]] = None,
        **kwargs
    )

def run(
    self,
    messages: List[Union[Dict, Message]],
    lang: str = 'en',
    **kwargs
) -> Iterator[List[Message]]

from qwen_agent.agents import FnCallAgent
from qwen_agent.llm.schema import Message

# Create agent with tools
agent = FnCallAgent(
    function_list=['code_interpreter'],
    llm={'model': 'qwen-max', 'api_key': 'your-api-key'}
)

messages = [
    Message(
        role='user',
        content='Calculate the factorial of 10'
    )
]

for response in agent.run(messages):
    for msg in response:
        if msg.content:
            print(f"Content: {msg.content}")
        if msg.function_call:
            print(f"Tool: {msg.function_call.name}")
            print(f"Args: {msg.function_call.arguments}")

agent = FnCallAgent(
    function_list=[
        'code_interpreter',
        'web_extractor',
        {'name': 'retrieval', 'max_ref_token': 4000}
    ],
    llm={'model': 'qwen-max'}
)

messages = [
    Message(
        role='user',
        content='Search for Python tutorials and create a study plan'
    )
]

for response in agent.run(messages):
    print(response[-1].content)

agent = FnCallAgent(
    function_list=['code_interpreter'],
    llm={'model': 'qwen-max'},
    files=['data.csv', 'https://example.com/dataset.xlsx']
)

messages = [
    Message(
        role='user',
        content='Analyze the data.csv file and create visualizations'
    )
]

for response in agent.run(messages):
    for msg in response:
        print(msg.content)

from qwen_agent.tools.base import BaseTool, register_tool

@register_tool('weather_tool')
class WeatherTool(BaseTool):
    description = 'Get weather information for a city'
    parameters = {
        'type': 'object',
        'properties': {
            'city': {
                'type': 'string',
                'description': 'City name'
            }
        },
        'required': ['city']
    }
    
    def call(self, params, **kwargs):
        params = self._verify_json_format_args(params)
        city = params['city']
        # Simulate weather API call
        return f"Weather in {city}: Sunny, 22°C"

# Use custom tool
agent = FnCallAgent(
    function_list=[WeatherTool()],
    llm={'model': 'qwen-max'}
)

messages = [Message(role='user', content='What is the weather in Paris?')]

for response in agent.run(messages):
    print(response[-1].content)

agent = FnCallAgent(
    function_list=['code_interpreter'],
    llm={'model': 'qwen-max'}
)

messages = []

# First turn
messages.append(Message(role='user', content='Create a list of prime numbers up to 20'))
for response in agent.run(messages):
    pass
messages.extend(response)

print(f"Assistant: {messages[-1].content}")

# Second turn - uses previous context
messages.append(Message(role='user', content='Now calculate their sum'))
for response in agent.run(messages):
    pass
messages.extend(response)

print(f"Assistant: {messages[-1].content}")

agent = FnCallAgent(
    function_list=['code_interpreter'],
    llm={'model': 'qwen-max'}
)

messages = [
    Message(
        role='user',
        content='Run this code: print(undefined_variable)'
    )
]

for response in agent.run(messages):
    for msg in response:
        if msg.role == 'function':
            # Tool result (may contain error message)
            print(f"Tool result: {msg.content}")
        elif msg.content:
            # Agent's response after seeing tool result
            print(f"Agent: {msg.content}")

# Chinese language agent
agent_cn = FnCallAgent(
    function_list=['code_interpreter'],
    llm={'model': 'qwen-max'},
    system_message='你是一个有用的AI助手。'
)

messages = [Message(role='user', content='计算1到100的和')]

for response in agent_cn.run(messages, lang='zh'):
    print(response[-1].content)

agent = FnCallAgent(
    function_list=['code_interpreter'],
    llm={'model': 'qwen-max'}
)

messages = [Message(role='user', content='Generate random numbers')]

# Get complete response at once
response = agent.run_nonstream(messages)
for msg in response:
    if msg.function_call:
        print(f"Called: {msg.function_call.name}")
    elif msg.role == 'function':
        print(f"Result: {msg.content}")
    elif msg.content:
        print(f"Final: {msg.content}")

# User asks a question
messages = [Message(role='user', content='What is 15 factorial?')]

# Agent execution:
# 1. LLM receives: [user message] + [available functions]
# 2. LLM responds with function call:
#    Message(role='assistant', function_call={
#        'name': 'code_interpreter',
#        'arguments': '{"code": "import math\nprint(math.factorial(15))"}'
#    })
# 3. Agent executes tool:
#    result = code_interpreter.call(...)
# 4. Tool result added:
#    Message(role='function', name='code_interpreter', content='1307674368000')
# 5. LLM receives updated messages
# 6. LLM responds with final answer:
#    Message(role='assistant', content='The factorial of 15 is 1,307,674,368,000')

from qwen_agent.memory import Memory

agent = FnCallAgent(
    function_list=['code_interpreter'],
    llm={'model': 'qwen-max'},
    files=['document.pdf']
)

# Access memory
print(f"System files: {agent.mem.system_files}")

from qwen_agent.settings import MAX_LLM_CALL_PER_RUN

# Default is typically 10
# Modify in settings if needed
print(f"Max LLM calls per run: {MAX_LLM_CALL_PER_RUN}")