from LightAgent import LightAgent

# Initialize Agent
agent = LightAgent(model="gpt-4.1", api_key="your_api_key", base_url="your_base_url")

# Run Agent
response = agent.run("Hello, who are you?")
print(response)

from LightAgent import LightAgent

# Initialize Agent
agent = LightAgent(
     role="Please remember that you are LightAgent, a useful assistant that helps users use multiple tools.",  # system role description
     model="gpt-4.1",  # Supported models: openai, chatglm, deepseek, qwen, etc.
     api_key="your_api_key",  # Replace with your large model provider API Key
     base_url="your_base_url",  # Replace with your large model provider api url
 )
# Run Agent
response = agent.run("Who are you?")
print(response)

from LightAgent import LightAgent

# Define Tool
def get_weather(city_name: str) -> str:
    """
    Get the current weather for `city_name`
    """
    return f"Query result: {city_name} is sunny."
# Define tool information inside the function
get_weather.tool_info = {
    "tool_name": "get_weather",
    "tool_description": "Get current weather information for the specified city.",
    "tool_params": [
        {"name": "city_name", "description": "The name of the city to query", "type": "string", "required": True},
    ]
}

tools = [get_weather]

# Initialize Agent
agent = LightAgent(model="gpt-4.1", api_key="your_api_key", base_url="your_base_url", tools=tools)

# Run Agent
response = agent.run("Please check the weather in Shanghai.")
print(response)

# Enable Memory Module

# Or use a custom memory module, here is an example with mem0 https://github.com/mem0ai/mem0/
from mem0 import Memory
from LightAgent import LightAgent
import os
from loguru import logger

class CustomMemory:
    def __init__(self):
        self.memories = []
        os.environ["OPENAI_API_KEY"] = "your_api_key"
        os.environ["OPENAI_API_BASE"] = "your_base_url"
        # Initialize Mem0
        config = {
            "version": "v1.1"
        }
        # Use qdrant as a vector database for storing memories in mem0, change config to the code below
        # config = {
        #     "vector_store": {
        #         "provider": "qdrant",
        #         "config": {
        #             "host": "localhost",
        #             "port": 6333,
        #         }
        #     },
        #     "version": "v1.1"
        # }
        self.m = Memory.from_config(config_dict=config)

    def store(self, data: str, user_id):
        """Store memory. Developers can modify the internal implementation of the storage method; the current example is the mem0 method for adding memory."""
        result = self.m.add(data, user_id=user_id)
        return result

    def retrieve(self, query: str, user_id):
        """Retrieve related memory. Developers can modify the internal implementation of the retrieval method; the current example is the mem0 method for searching memory."""
        result = self.m.search(query, user_id=user_id)
        return result

agent = LightAgent(
        role="Please remember that you are LightAgent, a useful assistant to help users use multiple tools.",  # system role description
        model="gpt-4.1",  # Supported models: openai, chatglm, deepseek, qwen, etc.
        api_key="your_api_key",  # Replace with your large model provider API Key
        base_url="your_base_url",  # Replace with your large model provider api url
        memory=CustomMemory(),  # Enable memory function
        tree_of_thought=False,  # Enable Chain of Thought
    )

# Memory-enabled test & if tools need to be added, you can add tools to the agent for memory-enabled tool calls

user_id = "user_01"
logger.info("\n=========== next conversation ===========")
query = "Introduce me to the attractions in Sanya. Many of my friends have traveled to Sanya, and I want to visit too."
print(agent.run(query, stream=False, user_id=user_id))
logger.info("\n=========== next conversation ===========")
query = "Where should I travel?"
print(agent.run(query, stream=False, user_id=user_id))

=========== next conversation ===========
2025-01-01 21:55:15.886 | INFO     | __main__:run_conversation:115 - 
Starting to think about the question: Introduce me to the attractions in Sanya, many of my friends have traveled to Sanya, and I want to visit too.
2025-01-01 21:55:28.676 | INFO     | __main__:run_conversation:118 - Final Reply: 
Sanya is a popular tourist city in Hainan Province, China, known for its beautiful beaches, tropical climate, and rich tourist resources. Here are some attractions worth visiting in Sanya:

1. **Yalong Bay**: Known as the "Hawaii of the East," it has a long beach and clear waters, ideal for swimming, diving, and sunbathing.

2. **Tianya Haijiao**: This is a famous cultural landscape, attracting tourists with its magnificent sea view and romantic legends. The giant rocks here are inscribed with the words "Tianya" and "Haijiao," symbolizing eternal love.

3. **Nanshan Cultural Tourism Zone**: Here there is a 108-meter-tall Nanshan Sea Guanyin statue, the highest sea Guanyin statue in the world. Visitors can experience Buddhist culture and visit temples and gardens.

4. **Wuzhizhou Island**: This small island is known for its pristine natural scenery and rich water activities. Visitors can engage in diving, snorkeling, and sea fishing among other activities.

5. **Dadonghai**: This is a beach located in Sanya city, favored by tourists for its convenient transportation and vibrant nightlife.

6. **Sanya Bay**: It is a 22-kilometer long beach and a great place to watch the sunset. This beach is relatively quiet, suitable for visitors who enjoy tranquility.

7. **Ya Nui National Park**: This is a tropical rainforest park where visitors can experience the natural scenery of tropical rainforests and participate in various adventure activities.

8. **Luohuitou Park**: A park located on a mountaintop, offering a panoramic view of Sanya city and Sanya Bay. There is also a beautiful legend about a deer.

9. **Xidao**: This is a relatively pristine small island attracting tourists with its serene beaches and rich marine life.

10. **Sanya Ancient Love**: This is a large cultural theme park showcasing the history and culture of Hainan through performances and exhibitions.

In addition to the above attractions, Sanya has many other places worth exploring, such as tropical botanical gardens and seafood markets. The local cuisine, especially fresh seafood and tropical fruits, should not be missed. When planning a trip, it is advisable to check the weather forecast and attraction opening times in advance to ensure a pleasant travel experience.
2025-01-01 21:55:28.676 | INFO     | __main__:<module>:191 - 
=========== next conversation ===========
2025-01-01 21:55:28.676 | INFO     | __main__:run_conversation:115 - 
Starting to think about the question: Where should I travel?
Relevant memory found:
User wants to travel to Sanya
User's friends have traveled to Sanya.
2025-01-01 21:55:38.797 | INFO     | __main__:run_conversation:118 - Final Reply: 
Based on previously mentioned information, the user’s friends have already traveled to Sanya, and the user has also expressed interest in going there. Therefore, Sanya might be a suitable travel destination for the user. Here is some travel information about Sanya for the user’s reference:

### Recommended Travel Spots in Sanya:
1. **Yalong Bay**: Known as the "Hawaii of the East," it has beautiful beaches and clear waters, suitable for swimming and sunbathing.
2. **Tianya Haijiao**: This iconic site of Sanya attracts tourists with its unique rocks and romantic legends.
3. **Nanshan Cultural Tourism Area**: It features the famous Nanshan Temple and the 108-meter-high Sea Guanyin statue, an important Buddhist cultural site.
4. **Wuzhizhou Island**: Suitable for diving and marine sports, the island is home to a rich marine ecosystem and coral reefs.
5. **Dadonghai**: A beach within Sanya city, conveniently located and suitable for family and couple visits.

### Other Recommendations:
If the user is already familiar with Sanya or wishes to explore other destinations, here are some other popular travel places:
1. **Guilin**: Known for its unique karst landscape and Lijiang River scenery.
2. **Lijiang**: The ancient town and Jade Dragon Snow Mountain are its main attractions, suitable for those who enjoy history and natural scenery.
3. **Zhangjiajie**: Famous for its unique stone pillars and natural scenery, it is one of the shooting locations for the movie "Avatar."

Users can choose suitable travel destinations based on their interests and schedule. If the user needs more detailed information or assistance in planning the trip, feel free to let us know!

import requests
from LightAgent import LightAgent

# Define Tool
def get_weather(
        city_name: str
) -> str:
    """
    Get weather information for a city
    :param city_name: Name of the city
    :return: Weather information
    """
    if not isinstance(city_name, str):
        raise TypeError("City name must be a string")

    key_selection = {
        "current_condition": ["temp_C", "FeelsLikeC", "humidity", "weatherDesc", "observation_time"],
    }
    try:
        resp = requests.get(f"https://wttr.in/{city_name}?format=j1")
        resp.raise_for_status()
        resp = resp.json()
        ret = {k: {_v: resp[k][0][_v] for _v in v} for k, v in key_selection.items()}
    except:
        import traceback
        ret = "Error encountered while fetching weather data!\n" + traceback.format_exc()

    return str(ret)
# Define tool information inside the function
get_weather.tool_info = {
    "tool_name": "get_weather",
    "tool_title": "get weather",
    "tool_description": "Get current weather information for the specified city.",
    "tool_params": [
        {"name": "city_name", "description": "The name of the city to query", "type": "string", "required": True},
    ]
}

def search_news(
        keyword: str,
        max_results: int = 5
) -> str:
    """
    Search news based on keywords
    :param keyword: Search keyword
    :param max_results: Maximum number of results to return, default is 5
    :return: News search results
    """
    results = f"By searching for {keyword}, I've found {max_results} related pieces of information."
    return str(results)

# Define tool information inside the function
search_news.tool_info = {
    "tool_name": "search_news",
    "tool_title": "search news",
    "tool_description": "Search news based on keywords.",
    "tool_params": [
        {"name": "keyword", "description": "Search keyword", "type": "string", "required": True},
        {"name": "max_results", "description": "Maximum number of results to return", "type": "int", "required": False},
    ]
}

def get_user_info(
        user_id: str
) -> str:
    """
    Get user information
    :param user_id: User ID
    :return: User information
    """
    if not isinstance(user_id, str):
        raise TypeError("User ID must be a string")

    try:
        # Assume using a user info API; this is a sample URL
        url = f"https://api.example.com/users/{user_id}"
        response = requests.get(url)
        response.raise_for_status()
        user_data = response.json()
        user_info = {
            "name": user_data.get("name"),
            "email": user_data.get("email"),
            "created_at": user_data.get("created_at")
        }
    except:
        import traceback
        user_info = "Error encountered while fetching user data!\n" + traceback.format_exc()

    return str(user_info)

# Define tool information inside the function
get_user_info.tool_info = {
    "tool_name": "get_user_info",
    "tool_description": "Retrieve information for the specified user.",
    "tool_params": [
        {"name": "user_id", "description": "User ID", "type": "string", "required": True},
    ]
}

# Custom Tools
tools = [get_weather, search_news, get_user_info]  # including all tools

# Initialize Agent
# Replace with your model parameters, API key, and base URL
agent = LightAgent(model="gpt-4.1", api_key="your_api_key", base_url="your_base_url", tools=tools)

query = "How is the weather in Sanya today?"
response = agent.run(query, stream=False)  # Use agent to run the query
print(response)

import json
import os
import sys
from LightAgent import LightAgent

# Initialize LightAgent
agent = LightAgent(
    name="Agent A",  # Agent name
    instructions="You are a helpful agent.",  # Role description
    role="Please remember that you are a tool generator; your task is to automatically generate corresponding tool code based on the text description provided by the user and save it to the specified directory. Please ensure that the generated code is accurate, usable, and meets the user's needs.",  # Tool generator's role description
    model="gpt-4.1",  # Replace with your model. Supported models: openai, chatglm, deepseek, qwen, etc.
    api_key="your_api_key",  # Replace with your API Key
    base_url="your_base_url",  # Replace with your API URL
)

# Sample text description
text = """
The Sina stock interface provides functionalities for obtaining stock market data, including stock quotes, real-time trading data, and K-line chart data.

Introduction to Sina stock interface functions
1. Get stock quote data:
Realtime quote data: Using the real-time quote API, you can obtain the latest prices, trading volume, and changes for stocks.
Minute line quote data: Using the minute line quote API, you can obtain the minute-by-minute trading data for stocks, including opening price, closing price, highest price, and lowest price.

2. Obtain historical K-line chart data:
K-line chart data: Through the K-line chart API, you can obtain the historical trading data for stocks, including opening price, closing price, highest price, lowest price, trading volume, etc. You can choose different time periods and moving average periods as needed.
Adjusted data: You can choose to retrieve adjusted K-line data, including pre-adjusted and post-adjusted data, for more accurate analysis of stock price changes.

Example of obtaining data from the Sina stock interface
1. Get stock quote data:
API address: http://hq.sinajs.cn/list=[stock_code]
Example: To obtain real-time quote data for the stock code "sh600519" (Kweichow Moutai), you can use the following API address: http://hq.sinajs.cn/list=sh600519
By sending an HTTP GET request to the above API address, you will receive a response containing the real-time data for that stock.

2. Get historical K-line chart data:
API address: http://money.finance.sina.com.cn/quotes_service/api/json_v2.php/CN_MarketData.getKLineData?symbol=[stock_code]&scale=[time_period]&ma=[average_period]&datalen=[data_length]
Example: To obtain daily K-line chart data for the stock code "sh600519" (Kweichow Moutai), you can use the following API address: http://money.finance.sina.com.cn/quotes_service/api/json_v2.php/CN_MarketData.getKLineData?symbol=sh600519&scale=240&ma=no&datalen=1023
By sending an HTTP GET request to the above API address, you will receive a response containing the historical K-line chart data for that stock.
"""

# Build the path to the tools directory
project_root = os.path.dirname(os.path.abspath(__file__))
tools_directory = os.path.join(project_root, "tools")

# Create tools directory if it does not exist
if not os.path.exists(tools_directory):
    os.makedirs(tools_directory)

print(f"Tools directory has been created: {tools_directory}")

# Use agent to generate tool code
agent.create_tool(text, tools_directory=tools_directory)

# Enable Tree of Thought
agent = LightAgent(
    model="gpt-4.1", 
    api_key="your_api_key", 
    base_url="your_base_url", 
    tree_of_thought=True,  # Enable Tree of Thought
    tot_model="gpt-4o", 
    tot_api_key="sk-uXx0H0B***17778F1",  # your deepseek r1 API Key
    tot_base_url="https://api.openai.com/v1",  # api url
    filter_tools=False,  # Disable the adaptive tool mechanism
)

from LightAgent import LightAgent, LightSwarm
# Set Environment Variables OPENAI_API_KEY and OPENAI_BASE_URL
# The default model uses gpt-4o-mini

# Create an instance of LightSwarm
light_swarm = LightSwarm()

# Create multiple agents
agent_a = LightAgent(
    name="Agent A",
    instructions="I am Agent A, the front desk receptionist.",
    role="Receptionist responsible for welcoming visitors and providing basic information guidance. Before each reply, please state your identity and that you can only guide users to other roles, not directly answer business questions. If you cannot help the user, please respond: Sorry, I am currently unable to assist!"
)

agent_b = LightAgent(
    name="Agent B",
    instructions="I am Agent B, responsible for the reservation of meeting rooms.",
    role="Meeting room reservation administrator in charge of handling reservations, cancellations, and inquiries for meeting rooms 1, 2, and 3."
)

agent_c = LightAgent(
    name="Agent C",
    instructions="I am Agent C, a technical support specialist, responsible for handling technical issues. Please state your identity before each reply, offering detailed responses to technical inquiries, and guide users to contact higher-level technical support for issues beyond your capability."
)

agent_d = LightAgent(
    name="Agent D",
    instructions="I am Agent D, an HR specialist, responsible for handling HR-related questions.",
    role="HR specialist managing inquiries and processes related to employee onboarding, offboarding, leave, and benefits."
)

# Automatically register agents to the LightSwarm instance
light_swarm.register_agent(agent_a, agent_b, agent_c, agent_d)

# Run Agent A
res = light_swarm.run(agent=agent_a, query="Hello, I am Alice. I need to check if Wang Xiaoming has completed onboarding.", stream=False)
print(res)

Hello, I am Agent D, the HR specialist. Regarding whether Wang Xiaoming has completed onboarding, I need to check our system records. Please wait a moment.
(Checking system records...)
According to our records, Wang Xiaoming completed his onboarding procedures on January 5, 2025. He has signed all necessary documents and has been assigned an employee number and office location. If you need further details or have any other questions, please feel free to contact the HR department. We are always ready to assist you.

# Enable streaming output
response = agent.run("Please generate an article about AI.", stream=True)
for chunk in response:
    print(chunk)

agent = LightAgent(
        name="Agent A",  # Agent name
        instructions="You are a helpful agent.",  # Role description
        role="Please remember that you are LightAgent, a useful assistant to help users use multiple tools.",  # system role description
        model="gpt-4.1",  # Supported models: openai, chatglm, deepseek, qwen, etc. qwen-turbo-2024-11-01 \ step-1-flash
        api_key="your_api_key",  # Replace with your API Key
        base_url="http://your_base_url/v1",  # API URL
        memory=CustomMemory(),  # Enable memory function
        self_learning=True,  # Enable agent self-learning
        debug=True,
        log_level="DEBUG",
        log_file="example.log"
    )

user_id = "test_user_1"
query = "I now have a procurement payment that needs to be transferred. What is my approval process?"
agent.run(query, stream=False, user_id=user_id)
query = "Please remember: According to the new company regulations, starting from January 2025, all procurement payments must first be signed by Manager Ding, who is responsible for procurement, then submitted to the finance manager for approval. After the finance manager's approval, the general manager of the company must also approve before the cashier can make the payment."
agent.run(query, stream=False, user_id=user_id)

user_id = "test_user_2"
query = "Hello, I have a procurement payment to transfer to the other party. How do I apply for the transfer?"
agent.run(query, stream=False, user_id=user_id)

from LightAgent import LightAgent

tracetools = {
    "TraceTool": "langfuse",
    "TraceToolConfig": {
        "langfuse_enabled": True,
        "langfuse_host": "https://cloud.langfuse.com",
        "langfuse_public_key": "pk-lf-9fedb073-a*86-4**5-b**2-52****1b1**7",
        "langfuse_secret_key": "sk-lf-27bdbdec-c**6-4**3-a**2-28**0140**c7"
    }
}

agent = LightAgent(
        name="Agent A",  # Agent Name
        instructions="You are a helpful agent.",  # Role Description
        role="Please remember that you are LightAgent, a useful assistant to help users use multiple tools.",  # system role description
        model="gpt-4o-mini",  # Supported models: openai, chatglm, deepseek, qwen, etc. qwen-turbo-2024-11-01 \ step-1-flash
        api_key="your_api_key",  # Replace with your API Key
        base_url="http://your_base_url/v1",  # API URL
        debug=True,
        log_level="DEBUG",
        log_file="example.log",
        tracetools=tracetools
    )