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:
        attempt:
            subprocess.check_call((sys.executable, "-m", "pip", "set up", package deal))
            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()


attempt:
    from beeai_framework import ChatModel
    from beeai_framework.brokers import Agent
    from beeai_framework.instruments import Instrument
    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 material') if messages else ""
       
        if "market" in last_message.decrease():
            return "Market evaluation reveals robust progress in AI frameworks with 42% YoY improve. 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 suggestion: Deal with ease of use, robust 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 software for market analysis and competitor evaluation"""
   
    def __init__(self):
        tremendous().__init__(
            title="market_research",
            description="Analyzes market tendencies 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 techniques", "Manufacturing deployment", "Instrument integration", "Enterprise adoption")
            },
            "enterprise_adoption": {
                "price": "78%",
                "top_use_cases": ("Buyer help", "Knowledge evaluation", "Code era", "Doc processing"),
                "challenges": ("Reliability", "Price management", "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:
            information = self.market_data("AI_frameworks")
            return f"""Market Evaluation Outcomes:
           
Key Opponents: {', '.be part of(information('opponents'))}
Market Measurement: {information('market_size')}
Development Fee: {information('growth_rate')}
Key Tendencies: {', '.be 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('price')}
High Use Circumstances: {', '.be part of(information('top_use_cases'))}
Important Challenges: {', '.be part of(information('challenges'))}


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

class CodeAnalysisTool(CustomTool):
    """Customized software 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.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 "attempt:" 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.cut up('n') if line.strip().startswith('class '))),
            "features": len((line for line in code_snippet.cut up('n') if line.strip().startswith('def ') or line.strip().startswith('async def ')))
        }
       
        options = ()
        if evaluation("error_handling") == "Lacking":
            options.append("Add try-except blocks for error dealing with")
        if evaluation("documentation") == "Wants enchancment":
            options.append("Add docstrings and feedback")
        if "print(" in code_snippet:
            options.append("Think about using correct logging as an alternative of print statements")
        if evaluation("async_usage") == "Sure" and "await" not in code_snippet:
            options.append("Guarantee correct await utilization with async features")
        if evaluation("complexity") == "Excessive":
            options.append("Think about breaking down into smaller features")
       
        return f"""Code Evaluation Report:
       
Construction:
- Traces of code: {evaluation('traces')}
- Complexity: {evaluation('complexity')}
- Courses: {evaluation('courses')}
- Features: {evaluation('features')}


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


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


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


class CustomAgent:
    """Customized agent implementation"""
   
    def __init__(self, title: str, position: str, directions: str, instruments: Record(CustomTool), llm=None):
        self.title = title
        self.position = position
        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.position}) processing activity...")
       
        self.reminiscence.append({"sort": "activity", "content material": activity, "timestamp": datetime.now()})
       
        task_lower = activity.decrease()
        tool_used = None
        tool_result = None
       
        for software in self.instruments:
            if software.title == "market_research" and ("market" in task_lower or "competitor" in task_lower):
                tool_result = await software.run(activity)
                tool_used = software.title
                break
            elif software.title == "code_analysis" and ("code" in task_lower or "analyze" in task_lower):
                tool_result = await software.run(activity)
                tool_used = software.title
                break
       
        messages = (
            {"position": "system", "content material": f"You might be {self.position}. {self.directions}"},
            {"position": "consumer", "content material": activity}
        )
       
        if tool_result:
            messages.append({"position": "system", "content material": f"Instrument {tool_used} supplied: {tool_result}"})
       
        response = await self.llm.generate(messages)
       
        self.reminiscence.append({"sort": "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, "position": agent.position})
   
    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.brokers))
           
            if context("shared_insights"):
                enhanced_task = f"{activity}nnContext from earlier evaluation:n" + "n".be 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 part of(summary_parts)}


Key Insights:
• Market alternatives recognized in AI framework area
• Technical structure suggestions supplied
• 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",
        position="Senior Market Analysis Analyst",
        directions="Analyze market tendencies, competitor panorama, and enterprise alternatives. Present data-driven insights with actionable suggestions.",
        instruments=(MarketResearchTool()),
        llm=MockChatModel()
    )
   
    tech_agent = CustomAgent(
        title="TechArchitect",
        position="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",
        position="Strategic Enterprise Planner",
        directions="Synthesize market and technical insights into complete strategic suggestions. Deal with 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 present AI framework market panorama and establish key alternatives for a brand new multi-agent framework focusing on enterprise customers.",
        """Analyze this code structure sample and supply technical evaluation:


async def multi_agent_workflow():
    brokers = (ResearchAgent(), AnalysisAgent(), SynthesisAgent())
    context = SharedContext()
   
    for agent in brokers:
        attempt:
            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 available on the market evaluation and technical evaluation, create a complete strategic plan for launching a aggressive AI framework with concentrate on 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("abstract"))
   
    print("n📈 WORKFLOW MONITORING SUMMARY")
    print("=" * 30)
    abstract = workflow.monitor.get_summary()
    print(f"Whole Occasions: {abstract('total_events')}")
    print(f"Whole 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():
    """Reveal particular person software performance"""
   
    print("n🛠️ Particular person Instrument 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:
            attempt:
                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 predominant():
    """Important demo perform"""
   
    print("🐝 Superior BeeAI Framework Tutorial")
    print("=" * 40)
    print("This tutorial demonstrates:")
    print("• Multi-agent workflows")
    print("• Customized software improvement")
    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 accessible)")
   
    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 to your particular use case")
    print("5. Deploy to manufacturing with correct monitoring")


if __name__ == "__main__":
    attempt:
        import nest_asyncio
        nest_asyncio.apply()
        print("✅ Utilized nest_asyncio for Colab compatibility")
    besides ImportError:
        print("⚠️ nest_asyncio not accessible - could not work in some environments")
   
    asyncio.run(predominant())