Code implementation for designing clever multi-agent workflows within the BEEAI framework

import subprocess
import sys
import asyncio
import json
from typing import Dict, Checklist, Any, Elective
from datetime import datetime
import os


def install_packages():
    packages = [
        "beeai-framework",
        "requests",
        "beautifulsoup4",
        "numpy",
        "pandas",
        "pydantic"
    ]
   
    print("Putting in required packages...")
    for bundle in packages:
        strive:
            subprocess.check_call([sys.executable, "-m", "pip", "install", package])
            print(f"✅ {bundle} put in efficiently")
        besides subprocess.CalledProcessError as e:
            print(f"❌ Failed to put in {bundle}: {e}")
    print("Set up full!")


install_packages()


strive:
    from beeai_framework import ChatModel
    from beeai_framework.brokers import Agent
    from beeai_framework.instruments import Software
    from beeai_framework.workflows import Workflow
    BEEAI_AVAILABLE = True
    print("✅ BeeAI Framework imported efficiently")
besides ImportError as e:
    print(f"⚠️ BeeAI Framework import failed: {e}")
    print("Falling again to customized implementation...")
    BEEAI_AVAILABLE = False

class MockChatModel:
    """Mock LLM for demonstration functions"""
    def __init__(self, model_name: str = "mock-llm"):
        self.model_name = model_name
   
    async def generate(self, messages: Checklist[Dict[str, str]]) -> str:
        """Generate a mock response"""
        last_message = messages[-1]['content'] if messages else ""
       
        if "market" in last_message.decrease():
            return "Market evaluation exhibits sturdy development in AI frameworks with 42% YoY enhance. Key opponents embody LangChain, CrewAI, and AutoGen."
        elif "code" in last_message.decrease():
            return "Code evaluation reveals good construction with async patterns. Think about including extra error dealing with and documentation."
        elif "technique" in last_message.decrease():
            return "Strategic advice: Concentrate on ease of use, sturdy documentation, and enterprise options to compete successfully."
        else:
            return f"Analyzed: {last_message[:100]}... Suggestion: Implement greatest practices for scalability and maintainability."


class CustomTool:
    """Base class for customized instruments"""
    def __init__(self, identify: str, description: str):
        self.identify = identify
        self.description = description
   
    async def run(self, input_data: str) -> str:
        """Override this methodology in subclasses"""
        increase NotImplementedError

class MarketResearchTool(CustomTool):
    """Customized device for market analysis and competitor evaluation"""
   
    def __init__(self):
        tremendous().__init__(
            identify="market_research",
            description="Analyzes market developments and competitor info"
        )
        self.market_data = {
            "AI_frameworks": {
                "opponents": ["LangChain", "CrewAI", "AutoGen", "Haystack", "Semantic Kernel"],
                "market_size": "$2.8B",
                "growth_rate": "42% YoY",
                "key_trends": ["Multi-agent systems", "Production deployment", "Tool integration", "Enterprise adoption"]
            },
            "enterprise_adoption": {
                "charge": "78%",
                "top_use_cases": ["Customer support", "Data analysis", "Code generation", "Document processing"],
                "challenges": ["Reliability", "Cost control", "Integration complexity", "Governance"]
            }
        }
   
    async def run(self, question: str) -> str:
        """Simulate market analysis primarily based on question"""
        query_lower = question.decrease()
       
        if "competitor" in query_lower or "competitors" in query_lower:
            knowledge = self.market_data["AI_frameworks"]
            return f"""Market Evaluation Outcomes:
           
Key Opponents: {', '.be part of(knowledge['competitors'])}
Market Dimension: {knowledge['market_size']}
Progress Charge: {knowledge['growth_rate']}
Key Tendencies: {', '.be part of(knowledge['key_trends'])}


Suggestion: Concentrate on differentiating options like simplified deployment, higher debugging instruments, and enterprise-grade safety."""
       
        elif "adoption" in query_lower or "enterprise" in query_lower:
            knowledge = self.market_data["enterprise_adoption"]
            return f"""Enterprise Adoption Evaluation:
           
Adoption Charge: {knowledge['rate']}
Prime Use Circumstances: {', '.be part of(knowledge['top_use_cases'])}
Foremost Challenges: {', '.be part of(knowledge['challenges'])}


Suggestion: Handle reliability and price management considerations by higher monitoring and useful resource administration options."""
       
        else:
            return "Market analysis obtainable for: competitor evaluation, enterprise adoption, or particular development evaluation. Please specify your focus space."

class CodeAnalysisTool(CustomTool):
    """Customized device for analyzing code patterns and suggesting enhancements"""
   
    def __init__(self):
        tremendous().__init__(
            identify="code_analysis",
            description="Analyzes code construction and suggests enhancements"
        )
   
    async def run(self, code_snippet: str) -> str:
        """Analyze code and supply insights"""
        evaluation = {
            "strains": len(code_snippet.cut up('n')),
            "complexity": "Excessive" if len(code_snippet) > 500 else "Medium" if len(code_snippet) > 200 else "Low",
            "async_usage": "Sure" if "async" in code_snippet or "await" in code_snippet else "No",
            "error_handling": "Current" if "strive:" in code_snippet or "besides:" in code_snippet else "Lacking",
            "documentation": "Good" if '"""' in code_snippet or "'''" in code_snippet else "Wants enchancment",
            "imports": "Current" if "import " in code_snippet else "None detected",
            "courses": len([line for line in code_snippet.split('n') if line.strip().startswith('class ')]),
            "features": len([line for line in code_snippet.split('n') if line.strip().startswith('def ') or line.strip().startswith('async def ')])
        }
       
        strategies = []
        if evaluation["error_handling"] == "Lacking":
            strategies.append("Add try-except blocks for error dealing with")
        if evaluation["documentation"] == "Wants enchancment":
            strategies.append("Add docstrings and feedback")
        if "print(" in code_snippet:
            strategies.append("Think about using correct logging as a substitute of print statements")
        if evaluation["async_usage"] == "Sure" and "await" not in code_snippet:
            strategies.append("Guarantee correct await utilization with async features")
        if evaluation["complexity"] == "Excessive":
            strategies.append("Think about breaking down into smaller features")
       
        return f"""Code Evaluation Report:
       
Construction:
- Traces of code: {evaluation['lines']}
- Complexity: {evaluation['complexity']}
- Lessons: {evaluation['classes']}
- Capabilities: {evaluation['functions']}


High quality Metrics:
- Async utilization: {evaluation['async_usage']}
- Error dealing with: {evaluation['error_handling']}
- Documentation: {evaluation['documentation']}


Strategies:
{chr(10).be part of(f"• {suggestion}" for suggestion in strategies) if strategies else "• Code seems to be good! Following greatest practices."}


General Rating: {10 - len(strategies) * 2}/10"""


class CustomAgent:
    """Customized agent implementation"""
   
    def __init__(self, identify: str, function: str, directions: str, instruments: Checklist[CustomTool], llm=None):
        self.identify = identify
        self.function = function
        self.directions = directions
        self.instruments = instruments
        self.llm = llm or MockChatModel()
        self.reminiscence = []
   
    async def run(self, process: str) -> Dict[str, Any]:
        """Execute agent process"""
        print(f"🤖 {self.identify} ({self.function}) processing process...")
       
        self.reminiscence.append({"sort": "process", "content material": process, "timestamp": datetime.now()})
       
        task_lower = process.decrease()
        tool_used = None
        tool_result = None
       
        for device in self.instruments:
            if device.identify == "market_research" and ("market" in task_lower or "competitor" in task_lower):
                tool_result = await device.run(process)
                tool_used = device.identify
                break
            elif device.identify == "code_analysis" and ("code" in task_lower or "analyze" in task_lower):
                tool_result = await device.run(process)
                tool_used = device.identify
                break
       
        messages = [
            {"role": "system", "content": f"You are {self.role}. {self.instructions}"},
            {"role": "user", "content": task}
        ]
       
        if tool_result:
            messages.append({"function": "system", "content material": f"Software {tool_used} offered: {tool_result}"})
       
        response = await self.llm.generate(messages)
       
        self.reminiscence.append({"sort": "response", "content material": response, "timestamp": datetime.now()})
       
        return {
            "agent": self.identify,
            "process": process,
            "tool_used": tool_used,
            "tool_result": tool_result,
            "response": response,
            "success": True
        }

class WorkflowMonitor:
    """Monitor and log workflow occasions"""
   
    def __init__(self):
        self.occasions = []
        self.start_time = datetime.now()
   
    def log_event(self, event_type: str, knowledge: Dict[str, Any]):
        """Log workflow occasions"""
        timestamp = datetime.now()
        self.occasions.append({
            "timestamp": timestamp,
            "period": (timestamp - self.start_time).total_seconds(),
            "event_type": event_type,
            "knowledge": knowledge
        })
        print(f"[{timestamp.strftime('%H:%M:%S')}] {event_type}: {knowledge.get('agent', 'System')}")
   
    def get_summary(self):
        """Get monitoring abstract"""
        return {
            "total_events": len(self.occasions),
            "total_duration": (datetime.now() - self.start_time).total_seconds(),
            "event_types": listing(set([e["event_type"] for e in self.occasions])),
            "occasions": self.occasions
        }


class CustomWorkflow:
    """Customized workflow implementation"""
   
    def __init__(self, identify: str, description: str):
        self.identify = identify
        self.description = description
        self.brokers = []
        self.monitor = WorkflowMonitor()
   
    def add_agent(self, agent: CustomAgent):
        """Add agent to workflow"""
        self.brokers.append(agent)
        self.monitor.log_event("agent_added", {"agent": agent.identify, "function": agent.function})
   
    async def run(self, duties: Checklist[str]) -> Dict[str, Any]:
        """Execute workflow with duties"""
        self.monitor.log_event("workflow_started", {"duties": len(duties)})
       
        outcomes = []
        context = {"shared_insights": []}
       
        for i, process in enumerate(duties):
            agent = self.brokers[i % len(self.agents)]
           
            if context["shared_insights"]:
                enhanced_task = f"{process}nnContext from earlier evaluation:n" + "n".be part of(context["shared_insights"][-2:])
            else:
                enhanced_task = process
           
            end result = await agent.run(enhanced_task)
            outcomes.append(end result)
           
            context["shared_insights"].append(f"{agent.identify}: {end result['response'][:200]}...")
           
            self.monitor.log_event("task_completed", {
                "agent": agent.identify,
                "task_index": i,
                "success": end result["success"]
            })
       
        self.monitor.log_event("workflow_completed", {"total_tasks": len(duties)})
       
        return {
            "workflow": self.identify,
            "outcomes": outcomes,
            "context": context,
            "abstract": self._generate_summary(outcomes)
        }
   
    def _generate_summary(self, outcomes: Checklist[Dict[str, Any]]) -> str:
        """Generate workflow abstract"""
        summary_parts = []
       
        for end in outcomes:
            summary_parts.append(f"• {end result['agent']}: {end result['response'][:150]}...")
       
        return f"""Workflow Abstract for {self.identify}:


{chr(10).be part of(summary_parts)}


Key Insights:
• Market alternatives recognized in AI framework house
• Technical structure suggestions offered
• Strategic implementation plan outlined
• Multi-agent collaboration demonstrated efficiently"""

async def advanced_workflow_demo():
    """Display superior multi-agent workflow"""
   
    print("🚀 Superior Multi-Agent Workflow Demo")
    print("=" * 50)
   
    workflow = CustomWorkflow(
        identify="Superior Enterprise Intelligence System",
        description="Multi-agent system for complete enterprise evaluation"
    )
   
    market_agent = CustomAgent(
        identify="MarketAnalyst",
        function="Senior Market Analysis Analyst",
        directions="Analyze market developments, competitor panorama, and enterprise alternatives. Present data-driven insights with actionable suggestions.",
        instruments=[MarketResearchTool()],
        llm=MockChatModel()
    )
   
    tech_agent = CustomAgent(
        identify="TechArchitect",
        function="Technical Structure Specialist",
        directions="Consider technical options, code high quality, and architectural selections. Concentrate on scalability, maintainability, and greatest practices.",
        instruments=[CodeAnalysisTool()],
        llm=MockChatModel()
    )
   
    strategy_agent = CustomAgent(
        identify="StrategicPlanner",
        function="Strategic Enterprise Planner",
        directions="Synthesize market and technical insights into complete strategic suggestions. Concentrate on ROI, threat evaluation, and implementation roadmaps.",
        instruments=[],
        llm=MockChatModel()
    )
   
    workflow.add_agent(market_agent)
    workflow.add_agent(tech_agent)
    workflow.add_agent(strategy_agent)
   
    duties = [
        "Analyze the current AI framework market landscape and identify key opportunities for a new multi-agent framework targeting enterprise users.",
        """Analyze this code architecture pattern and provide technical assessment:


async def multi_agent_workflow():
    agents = [ResearchAgent(), AnalysisAgent(), SynthesisAgent()]
    context = SharedContext()
   
    for agent in brokers:
        strive:
            end result = await agent.run(context.get_task())
            if end result.success:
                context.add_insight(end result.knowledge)
            else:
                context.add_error(end result.error)
        besides Exception as e:
            logger.error(f"Agent {agent.identify} failed: {e}")
           
    return context.synthesize_recommendations()""",
        "Primarily based available on the market evaluation and technical evaluation, create a complete strategic plan for launching a aggressive AI framework with give attention to multi-agent capabilities and enterprise adoption."
    ]
   
    print("n🔄 Executing Superior Workflow...")
    end result = await workflow.run(duties)
   
    print("n✅ Workflow Accomplished Efficiently!")
    print("=" * 50)
    print("📊 COMPREHENSIVE ANALYSIS RESULTS")
    print("=" * 50)
    print(end result["summary"])
   
    print("n📈 WORKFLOW MONITORING SUMMARY")
    print("=" * 30)
    abstract = workflow.monitor.get_summary()
    print(f"Complete Occasions: {abstract['total_events']}")
    print(f"Complete Period: {abstract['total_duration']:.2f} seconds")
    print(f"Occasion Sorts: {', '.be part of(abstract['event_types'])}")
   
    return workflow, end result


async def simple_tool_demo():
    """Display particular person device performance"""
   
    print("n🛠️ Particular person Software Demo")
    print("=" * 30)
   
    market_tool = MarketResearchTool()
    code_tool = CodeAnalysisTool()
   
    print("Out there Instruments:")
    print(f"• {market_tool.identify}: {market_tool.description}")
    print(f"• {code_tool.identify}: {code_tool.description}")
   
    print("n🔍 Market Analysis Evaluation:")
    market_result = await market_tool.run("competitor evaluation in AI frameworks")
    print(market_result)
   
    print("n🔍 Code Evaluation:")
    sample_code=""'
import asyncio
from typing import Checklist, Dict


class AgentManager:
    """Manages a number of AI brokers"""
   
    def __init__(self):
        self.brokers = []
        self.outcomes = []
   
    async def add_agent(self, agent):
        """Add agent to supervisor"""
        self.brokers.append(agent)
   
    async def run_all(self, process: str) -> Checklist[Dict]:
        """Run process on all brokers"""
        outcomes = []
        for agent in self.brokers:
            strive:
                end result = await agent.execute(process)
                outcomes.append(end result)
            besides Exception as e:
                print(f"Agent failed: {e}")
                outcomes.append({"error": str(e)})
        return outcomes
'''
   
    code_result = await code_tool.run(sample_code)
    print(code_result)

async def important():
    """Foremost demo operate"""
   
    print("🐝 Superior BeeAI Framework Tutorial")
    print("=" * 40)
    print("This tutorial demonstrates:")
    print("• Multi-agent workflows")
    print("• Customized device growth")
    print("• Reminiscence administration")
    print("• Occasion monitoring")
    print("• Manufacturing-ready patterns")
   
    if BEEAI_AVAILABLE:
        print("• Utilizing actual BeeAI Framework")
    else:
        print("• Utilizing customized implementation (BeeAI not obtainable)")
   
    print("=" * 40)
   
    await simple_tool_demo()
   
    print("n" + "="*50)
    await advanced_workflow_demo()
   
    print("n🎉 Tutorial Full!")
    print("nNext Steps:")
    print("1. Set up BeeAI Framework correctly: pip set up beeai-framework")
    print("2. Configure your most popular LLM (OpenAI, Anthropic, native fashions)")
    print("3. Discover the official BeeAI documentation")
    print("4. Construct customized brokers in your particular use case")
    print("5. Deploy to manufacturing with correct monitoring")


if __name__ == "__main__":
    strive:
        import nest_asyncio
        nest_asyncio.apply()
        print("✅ Utilized nest_asyncio for Colab compatibility")
    besides ImportError:
        print("⚠️ nest_asyncio not obtainable - might not work in some environments")
   
    asyncio.run(important())