# Example: Using OpenAI for reasoning
from openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are an AI agent that can use tools"},
        {"role": "user", "content": "Book a flight to NYC"}
    ],
    tools=[flight_search_tool, booking_tool]
)

# Vector memory example
from chromadb import Client

client = Client()
collection = client.create_collection("agent_memory")

# Store interaction
collection.add(
    documents=["User prefers morning flights"],
    metadatas=[{"type": "preference"}],
    ids=["pref_1"]
)

# Function calling schema
tools = [
    {
        "type": "function",
        "function": {
            "name": "search_flights",
            "description": "Search for available flights",
            "parameters": {
                "type": "object",
                "properties": {
                    "origin": {"type": "string"},
                    "destination": {"type": "string"},
                    "date": {"type": "string"}
                },
                "required": ["origin", "destination", "date"]
            }
        }
    }
]

class AgentExecutor:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = tools

    def execute(self, task):
        # 1. Plan
        plan = self.llm.create_plan(task)

        # 2. Execute steps
        for step in plan:
            tool = self.select_tool(step)
            result = tool.execute(step.params)

            # 3. Reflect
            if not self.validate(result):
                self.replan(step, result)

        return final_result

from langchain.agents import initialize_agent, Tool
from langchain.llms import OpenAI

tools = [
    Tool(
        name="Calculator",
        func=calculator.run,
        description="Useful for math calculations"
    )
]

agent = initialize_agent(
    tools, 
    OpenAI(temperature=0),
    agent="zero-shot-react-description"
)

agent.run("What is 25 * 4 + 10?")

class AutoGPTAgent:
    def run(self, goal):
        while not self.goal_achieved(goal):
            # 1. Think
            thoughts = self.llm.generate_thoughts(goal, self.memory)

            # 2. Reason
            action = self.llm.decide_action(thoughts)

            # 3. Act
            result = self.execute_action(action)

            # 4. Criticize
            self.memory.add(result)
            self.evaluate_progress(goal)

Thought: I need to find the weather in NYC
Action: search_weather(location="New York")
Observation: Temperature is 72°F, sunny
Thought: Now I should check if user needs umbrella
Action: analyze_weather(temp=72, condition="sunny")
Observation: No umbrella needed
Answer: It's 72°F and sunny in NYC. No umbrella needed!

from langchain.agents import AgentExecutor
from langchain.tools import Tool

class SupportAgent:
    def __init__(self):
        self.tools = [
            Tool(
                name="search_knowledge_base",
                func=self.search_kb,
                description="Search company knowledge base"
            ),
            Tool(
                name="create_ticket",
                func=self.create_ticket,
                description="Create support ticket"
            ),
            Tool(
                name="check_order_status",
                func=self.check_order,
                description="Check order status by ID"
            )
        ]

    def search_kb(self, query):
        # Vector search in knowledge base
        results = vector_db.similarity_search(query, k=3)
        return results

    def create_ticket(self, issue):
        # Create ticket in system
        ticket_id = ticketing_system.create(issue)
        return f"Ticket {ticket_id} created"

class MultiAgentSystem:
    def __init__(self):
        self.researcher = ResearchAgent()
        self.writer = WriterAgent()
        self.critic = CriticAgent()

    def collaborate(self, task):
        # Research phase
        research = self.researcher.gather_info(task)

        # Writing phase
        draft = self.writer.create_content(research)

        # Review phase
        feedback = self.critic.review(draft)

        # Iterate until approved
        while not feedback.approved:
            draft = self.writer.revise(draft, feedback)
            feedback = self.critic.review(draft)

        return draft

def create_tool_from_api(api_spec):
    """Dynamically create tools from OpenAPI specs"""

    @tool
    def dynamic_tool(params: dict):
        """Auto-generated tool from API"""
        response = requests.post(
            api_spec.endpoint,
            json=params,
            headers=api_spec.headers
        )
        return response.json()

    return dynamic_tool

from functools import lru_cache

@lru_cache(maxsize=1000)
def cached_llm_call(prompt, model):
    return llm.generate(prompt, model)

import asyncio

async def execute_tools_parallel(tools, params):
    tasks = [tool.execute_async(param) 
             for tool, param in zip(tools, params)]
    results = await asyncio.gather(*tasks)
    return results

# Install dependencies
pip install langchain openai chromadb

# Basic agent setup
from langchain.agents import create_openai_functions_agent

agent = create_openai_functions_agent(
    llm=ChatOpenAI(model="gpt-4"),
    tools=your_tools,
    prompt=your_prompt
)

executor = AgentExecutor(agent=agent, tools=your_tools)
result = executor.invoke({"input": "Your task here"})