A Code Implementation for Designing Clever Multi-Agent Workflows with the BeeAI Framework

import subprocess
import sys
import asyncio
import json
from typing import Dict, Record, 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 package deal in packages:
        strive:
            subprocess.check_call([sys.executable, "-m", "pip", "install", package])
            print(f"✅ {package deal} put in efficiently")
        besides subprocess.CalledProcessError as e:
            print(f"❌ Failed to put in {package deal}: {e}")
    print("Set up full!")


install_packages()


strive:
    from beeai_framework import ChatModel
    from beeai_framework.brokers import Agent
    from beeai_framework.instruments import Device
    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: Record[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 progress in AI frameworks with 42% YoY enhance. Key rivals embody LangChain, CrewAI, and AutoGen."
        elif "code" in last_message.decrease():
            return "Code evaluation reveals good construction with async patterns. Take into account including extra error dealing with and documentation."
        elif "technique" in last_message.decrease():
            return "Strategic advice: Deal with 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, title: str, description: str):
        self.title = title
        self.description = description
   
    async def run(self, input_data: str) -> str:
        """Override this technique in subclasses"""
        elevate NotImplementedError

class MarketResearchTool(CustomTool):
    """Customized device for market analysis and competitor evaluation"""
   
    def __init__(self):
        tremendous().__init__(
            title="market_research",
            description="Analyzes market traits and competitor data"
        )
        self.market_data = {
            "AI_frameworks": {
                "rivals": ["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": {
                "fee": "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 based mostly on question"""
        query_lower = question.decrease()
       
        if "competitor" in query_lower or "competitors" in query_lower:
            information = self.market_data["AI_frameworks"]
            return f"""Market Evaluation Outcomes:
           
Key Opponents: {', '.be a part of(information['competitors'])}
Market Dimension: {information['market_size']}
Development Fee: {information['growth_rate']}
Key Developments: {', '.be a part of(information['key_trends'])}


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


Suggestion: Deal with reliability and value management considerations by means of higher monitoring and useful resource administration options."""
       
        else:
            return "Market analysis out there for: competitor evaluation, enterprise adoption, or particular pattern evaluation. Please specify your focus space."

class CodeAnalysisTool(CustomTool):
    """Customized device for analyzing code patterns and suggesting enhancements"""
   
    def __init__(self):
        tremendous().__init__(
            title="code_analysis",
            description="Analyzes code construction and suggests enhancements"
        )
   
    async def run(self, code_snippet: str) -> str:
        """Analyze code and supply insights"""
        evaluation = {
            "traces": len(code_snippet.break 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",
            "lessons": 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("Take into account breaking down into smaller features")
       
        return f"""Code Evaluation Report:
       
Construction:
- Traces of code: {evaluation['lines']}
- Complexity: {evaluation['complexity']}
- Courses: {evaluation['classes']}
- Capabilities: {evaluation['functions']}


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


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


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


class CustomAgent:
    """Customized agent implementation"""
   
    def __init__(self, title: str, function: str, directions: str, instruments: Record[CustomTool], llm=None):
        self.title = title
        self.function = function
        self.directions = directions
        self.instruments = instruments
        self.llm = llm or MockChatModel()
        self.reminiscence = []
   
    async def run(self, activity: str) -> Dict[str, Any]:
        """Execute agent activity"""
        print(f"🤖 {self.title} ({self.function}) processing activity...")
       
        self.reminiscence.append({"kind": "activity", "content material": activity, "timestamp": datetime.now()})
       
        task_lower = activity.decrease()
        tool_used = None
        tool_result = None
       
        for device in self.instruments:
            if device.title == "market_research" and ("market" in task_lower or "competitor" in task_lower):
                tool_result = await device.run(activity)
                tool_used = device.title
                break
            elif device.title == "code_analysis" and ("code" in task_lower or "analyze" in task_lower):
                tool_result = await device.run(activity)
                tool_used = device.title
                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"Device {tool_used} offered: {tool_result}"})
       
        response = await self.llm.generate(messages)
       
        self.reminiscence.append({"kind": "response", "content material": response, "timestamp": datetime.now()})
       
        return {
            "agent": self.title,
            "activity": activity,
            "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, information: 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,
            "information": information
        })
        print(f"[{timestamp.strftime('%H:%M:%S')}] {event_type}: {information.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, title: str, description: str):
        self.title = title
        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.title, "function": agent.function})
   
    async def run(self, duties: Record[str]) -> Dict[str, Any]:
        """Execute workflow with duties"""
        self.monitor.log_event("workflow_started", {"duties": len(duties)})
       
        outcomes = []
        context = {"shared_insights": []}
       
        for i, activity in enumerate(duties):
            agent = self.brokers[i % len(self.agents)]
           
            if context["shared_insights"]:
                enhanced_task = f"{activity}nnContext from earlier evaluation:n" + "n".be a part of(context["shared_insights"][-2:])
            else:
                enhanced_task = activity
           
            end result = await agent.run(enhanced_task)
            outcomes.append(end result)
           
            context["shared_insights"].append(f"{agent.title}: {end result['response'][:200]}...")
           
            self.monitor.log_event("task_completed", {
                "agent": agent.title,
                "task_index": i,
                "success": end result["success"]
            })
       
        self.monitor.log_event("workflow_completed", {"total_tasks": len(duties)})
       
        return {
            "workflow": self.title,
            "outcomes": outcomes,
            "context": context,
            "abstract": self._generate_summary(outcomes)
        }
   
    def _generate_summary(self, outcomes: Record[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.title}:


{chr(10).be a 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():
    """Reveal superior multi-agent workflow"""
   
    print("🚀 Superior Multi-Agent Workflow Demo")
    print("=" * 50)
   
    workflow = CustomWorkflow(
        title="Superior Enterprise Intelligence System",
        description="Multi-agent system for complete enterprise evaluation"
    )
   
    market_agent = CustomAgent(
        title="MarketAnalyst",
        function="Senior Market Analysis Analyst",
        directions="Analyze market traits, competitor panorama, and enterprise alternatives. Present data-driven insights with actionable suggestions.",
        instruments=[MarketResearchTool()],
        llm=MockChatModel()
    )
   
    tech_agent = CustomAgent(
        title="TechArchitect",
        function="Technical Structure Specialist",
        directions="Consider technical options, code high quality, and architectural choices. Deal with scalability, maintainability, and greatest practices.",
        instruments=[CodeAnalysisTool()],
        llm=MockChatModel()
    )
   
    strategy_agent = CustomAgent(
        title="StrategicPlanner",
        function="Strategic Enterprise Planner",
        directions="Synthesize market and technical insights into complete strategic suggestions. Deal with ROI, danger 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.information)
            else:
                context.add_error(end result.error)
        besides Exception as e:
            logger.error(f"Agent {agent.title} failed: {e}")
           
    return context.synthesize_recommendations()""",
        "Based mostly in the marketplace 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 Length: {abstract['total_duration']:.2f} seconds")
    print(f"Occasion Sorts: {', '.be a part of(abstract['event_types'])}")
   
    return workflow, end result


async def simple_tool_demo():
    """Reveal particular person device performance"""
   
    print("n🛠️ Particular person Device Demo")
    print("=" * 30)
   
    market_tool = MarketResearchTool()
    code_tool = CodeAnalysisTool()
   
    print("Obtainable Instruments:")
    print(f"• {market_tool.title}: {market_tool.description}")
    print(f"• {code_tool.title}: {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 Record, 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, activity: str) -> Record[Dict]:
        """Run activity on all brokers"""
        outcomes = []
        for agent in self.brokers:
            strive:
                end result = await agent.execute(activity)
                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 essential():
    """Predominant demo perform"""
   
    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 out there)")
   
    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 well-liked LLM (OpenAI, Anthropic, native fashions)")
    print("3. Discover the official BeeAI documentation")
    print("4. Construct customized brokers on 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 out there - might not work in some environments")
   
    asyncio.run(essential())