ReActChat

Question: [User's question]
Thought: [Agent's reasoning about what to do]
Action: [Tool name to use]
Action Input: [Tool parameters]
Observation: [Tool result]
Thought: [Further reasoning based on observation]
... (repeat as needed)
Thought: I now know the final answer
Final Answer: [Response to user]

from qwen_agent.agents import ReActChat

agent = ReActChat(
    function_list=['code_interpreter', 'web_search'],
    llm={'model': 'qwen-max-latest', 'model_type': 'qwen_dashscope'},
    system_message='You are a helpful assistant.',
    name='ReActAgent',
    description='A reasoning agent',
    files=['./context.txt']
)

from qwen_agent.agents import ReActChat
from qwen_agent.tools.base import BaseTool, register_tool
import json5

@register_tool('get_weather')
class WeatherTool(BaseTool):
    description = 'Get current weather for a location'
    parameters = [{
        'name': 'location',
        'type': 'string',
        'description': 'City name',
        'required': True
    }]
    
    def call(self, params: str, **kwargs) -> str:
        location = json5.loads(params)['location']
        return json5.dumps({
            'location': location,
            'temperature': 72,
            'condition': 'sunny'
        })

agent = ReActChat(
    function_list=['get_weather'],
    llm={'model': 'qwen-max-latest', 'model_type': 'qwen_dashscope'}
)

messages = [{
    'role': 'user',
    'content': 'What should I wear in San Francisco today?'
}]

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

Thought: I need to check the current weather in San Francisco first
Action: get_weather
Action Input: {"location": "San Francisco"}
Observation: {"location": "San Francisco", "temperature": 72, "condition": "sunny"}
Thought: I now know the final answer
Final Answer: Since it's sunny and 72°F in San Francisco today, I'd recommend wearing light layers - perhaps a t-shirt with a light jacket that you can take off if it gets warmer.

from qwen_agent.agents import ReActChat

agent = ReActChat(
    function_list=['code_interpreter', 'web_search'],
    llm={'model': 'qwen-max-latest', 'model_type': 'qwen_dashscope'}
)

messages = [{
    'role': 'user',
    'content': 'Find the population of Tokyo and calculate what 10% of that would be'
}]

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

# Output will show:
# Thought: I need to search for Tokyo's population
# Action: web_search
# Action Input: {"query": "Tokyo population"}
# Observation: [search results]
# Thought: Now I need to calculate 10% of that number
# Action: code_interpreter
# Action Input: {"code": "population = 13960000\nresult = population * 0.1\nprint(result)"}
# Observation: 1396000
# Thought: I now know the final answer
# Final Answer: Tokyo's population is approximately 13.96 million, so 10% of that would be about 1.396 million people.

from qwen_agent.agents import ReActChat

agent = ReActChat(
    function_list=['code_interpreter'],
    llm={'model': 'qwen-max-latest', 'model_type': 'qwen_dashscope'},
    files=['./sales_data.csv']
)

messages = [{
    'role': 'user',
    'content': 'Analyze the sales data and tell me which product performed best'
}]

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

from qwen_agent.agents import ReActChat

agent = ReActChat(
    function_list=['calculator', 'web_search'],
    llm={'model': 'qwen-max-latest', 'model_type': 'qwen_dashscope'}
)

messages = [{'role': 'user', 'content': 'Complex query...'}]

for response in agent.run(messages=messages):
    content = response[-1]['content']
    
    # Extract reasoning steps
    if 'Thought:' in content:
        thoughts = [line for line in content.split('\n') if line.startswith('Thought:')]
        print('Reasoning steps:', thoughts)
    
    # Check which tools were used
    if 'Action:' in content:
        actions = [line for line in content.split('\n') if line.startswith('Action:')]
        print('Tools used:', actions)

custom_react_prompt = """Answer questions using these tools: {tool_names}

Format:
Question: [question]
Thinking: [your reasoning]
Tool: [tool to use]
Input: [tool input]
Result: [tool output]
Thinking: [more reasoning]
Answer: [final answer]

Begin!
"""

agent = ReActChat(
    function_list=['calculator'],
    llm={'model': 'qwen-max-latest', 'model_type': 'qwen_dashscope'},
    system_message=custom_react_prompt
)