from openclaw import Agent, Memory

# Create agent
agent = Agent(
    name="ChatBot",
    model="llama-3-70b",
    personality="friendly, helpful, knowledgeable",
    memory=Memory(type="short-term")
)

# Start agent
response = agent.chat("Hello! How are you?")
print(response)

from openclaw import Agent, Tool
from openclaw.tools import (
    CalculatorTool,
    SearchTool,
    WeatherTool,
    FileTool
)

# Define custom tool
class DatabaseTool(Tool):
    def __init__(self):
        super().__init__(
            name="database",
            description="Query the product database"
        )
    
    def execute(self, query):
        # Connect to your database
        results = db.query(query)
        return str(results)

# Create agent with tools
agent = Agent(
    name="Assistant",
    model="llama-3-70b",
    tools=[
        CalculatorTool(),
        SearchTool(),
        WeatherTool(),
        FileTool(),
        DatabaseTool()
    ]
)

# Agent will automatically choose appropriate tools
response = agent.chat("What's the weather in Tokyo and add 27 degrees to it?")
# Agent will: 1. Call WeatherTool, 2. Call CalculatorTool, 3. Return answer

from openclaw import Agent, Orchestrator

# Create specialized agents
researcher = Agent(
    name="Researcher",
    model="llama-3-70b",
    tools=[SearchTool(), DatabaseTool()],
    personality="thorough, analytical"
)

coder = Agent(
    name="Coder",
    model="code-llama-34b",
    tools=[FileTool(), CalculatorTool()],
    personality="precise, technical"
)

writer = Agent(
    name="Writer",
    model="mistral-7b",
    tools=[FileTool()],
    personality="creative, articulate"
)

# Create orchestrator to coordinate agents
orchestrator = Orchestrator(
    agents=[researcher, coder, writer],
    model="llama-3-70b"
)

# Orchestrator will delegate tasks to appropriate agents
result = orchestrator.execute(
    "Research renewable energy trends, write a Python script to analyze data, and create a report"
)

from openclaw import Memory

# Vector store for long-term memory
memory = Memory(
    type="vector",
    storage="chromadb",
    embedding_model="all-MiniLM-L6-v2"
)

agent = Agent(
    name="PersonalAssistant",
    model="llama-3-70b",
    memory=memory
)

# Agent remembers past conversations
agent.chat("Remember that I prefer morning meetings")
# Later...
agent.chat("When's my next meeting?")
# Agent: "Your next meeting is tomorrow at 10 AM (morning, as you prefer)"

agent = Agent(
    name="TechSupport",
    model="llama-3-70b",
    system_prompt="""
    You are a technical support specialist.
    - Be patient and empathetic
    - Ask clarifying questions
    - Provide step-by-step solutions
    - Never guess - if unsure, say so
    - Follow up to ensure resolution
    """
)

# Enable streaming for real-time responses
for chunk in agent.chat_stream("Tell me about AI agents"):
    print(chunk, end="", flush=True)

agent = Agent(
    name="DataAnalyst",
    model="llama-3-70b",
    parallel_tools=True,  # Execute multiple tools in parallel
    tools=[DatabaseTool(), APITool(), FileTool()]
)

class ResearchAgent(Agent):
    def __init__(self):
        super().__init__(
            name="Researcher",
            model="llama-3-70b",
            tools=[SearchTool(), DatabaseTool(), SummaryTool()]
        )
    
    def research(self, topic):
        # 1. Search for information
        results = self.tools["search"].execute(topic)
        
        # 2. Summarize findings
        summary = self.tools["summary"].execute(results)
        
        # 3. Save to database
        self.tools["database"].save(topic, summary)
        
        return summary

class WorkflowAgent(Agent):
    def __init__(self):
        super().__init__(
            name="Workflow",
            model="llama-3-70b",
            tools=[EmailTool(), CalendarTool(), TaskTool()]
        )
    
    def handle_request(self, request):
        # Parse intent
        intent = self.analyze_intent(request)
        
        # Execute workflow
        if intent == "schedule_meeting":
            return self.schedule_meeting(request)
        elif intent == "send_report":
            return self.send_report(request)
        elif intent == "create_task":
            return self.create_task(request)

class AutonomousAgent(Agent):
    def __init__(self, goal):
        super().__init__(
            name="Autonomous",
            model="llama-3-70b",
            memory=Memory(type="vector")
        )
        self.goal = goal
    
    def run(self):
        while not self.is_goal_achieved():
            # Plan next action
            plan = self.plan()
            
            # Execute action
            result = self.execute(plan)
            
            # Learn from result
            self.learn(result)
            
            # Repeat until goal achieved

from openclaw import metrics

# Enable metrics
metrics.enable()

# Track response time
@metrics.time("agent_response")
def handle_request(request):
    return agent.chat(request)

# Track token usage
metrics.track("tokens_used", agent.get_token_count())

import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("openclaw")

# Agent will automatically log:
# - User requests
# - Tool calls
# - Response times
# - Errors

# Enable debug mode
agent = Agent(
    name="DebugAgent",
    model="llama-3-70b",
    debug=True  # Logs reasoning process
)

# Use smaller model for simple queries
if complexity_score(request) < 0.3:
    model = "phi-3-mini"
else:
    model = "llama-3-70b"

# Enable quantization
agent = Agent(
    model="llama-3-70b",
    quantization="4-bit"  # Reduces memory by 75%
)

# Solutions:
1. Reduce model size
2. Enable quantization (--quantize 4-bit)
3. Reduce context window
4. Clear memory cache
5. Increase system RAM

# Solutions:
1. Check GPU utilization: nvidia-smi
2. Enable streaming responses
3. Use smaller model
4. Enable caching
5. Reduce context length

# Debug tool execution
agent.debug_tools = True
agent.chat("test command")
# Check logs for tool call details