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"""
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Single-File Hierarchical Structured Communication Framework Example
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This example demonstrates how to use the consolidated single-file implementation
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of the Talk Structurally, Act Hierarchically framework.
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All components are now in one file: hierarchical_structured_communication_framework.py
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"""
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import os
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import sys
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from typing import Dict, Any
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# Add the project root to the Python path
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project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..'))
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sys.path.insert(0, project_root)
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from dotenv import load_dotenv
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# Import everything from the single file
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from swarms.structs.hierarchical_structured_communication_framework import (
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HierarchicalStructuredCommunicationFramework,
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HierarchicalStructuredCommunicationGenerator,
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HierarchicalStructuredCommunicationEvaluator,
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HierarchicalStructuredCommunicationRefiner,
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HierarchicalStructuredCommunicationSupervisor,
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# Convenience aliases
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TalkHierarchicalGenerator,
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TalkHierarchicalEvaluator,
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TalkHierarchicalRefiner,
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TalkHierarchicalSupervisor,
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)
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# Load environment variables
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load_dotenv()
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def example_basic_usage():
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"""
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Basic usage example with default agents
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"""
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print("=" * 80)
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print("BASIC USAGE EXAMPLE")
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print("=" * 80)
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# Create framework with default configuration
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framework = HierarchicalStructuredCommunicationFramework(
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name="BasicFramework",
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max_loops=2,
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verbose=True
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)
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# Run a simple task
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task = "Explain the benefits of structured communication in multi-agent systems"
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print(f"Task: {task}")
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print("Running framework...")
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result = framework.run(task)
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print("\n" + "=" * 50)
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print("FINAL RESULT")
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print("=" * 50)
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print(result["final_result"])
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print(f"\nTotal loops: {result['total_loops']}")
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print(f"Conversation history entries: {len(result['conversation_history'])}")
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print(f"Evaluation results: {len(result['evaluation_results'])}")
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def example_custom_agents():
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"""
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Example using custom specialized agents
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"""
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print("\n" + "=" * 80)
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print("CUSTOM AGENTS EXAMPLE")
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print("=" * 80)
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# Create custom agents using the convenience aliases
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generator = TalkHierarchicalGenerator(
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agent_name="ContentCreator",
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model_name="gpt-4o-mini",
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verbose=True
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)
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evaluator1 = TalkHierarchicalEvaluator(
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agent_name="AccuracyChecker",
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evaluation_criteria=["accuracy", "technical_correctness"],
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model_name="gpt-4o-mini",
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verbose=True
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)
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evaluator2 = TalkHierarchicalEvaluator(
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agent_name="ClarityChecker",
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evaluation_criteria=["clarity", "readability", "coherence"],
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model_name="gpt-4o-mini",
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verbose=True
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)
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refiner = TalkHierarchicalRefiner(
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agent_name="ContentImprover",
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model_name="gpt-4o-mini",
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verbose=True
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)
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supervisor = TalkHierarchicalSupervisor(
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agent_name="WorkflowManager",
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model_name="gpt-4o-mini",
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verbose=True
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)
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# Create framework with custom agents
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framework = HierarchicalStructuredCommunicationFramework(
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name="CustomFramework",
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supervisor=supervisor,
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generators=[generator],
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evaluators=[evaluator1, evaluator2],
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refiners=[refiner],
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max_loops=3,
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verbose=True
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)
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# Run a complex task
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task = "Design a comprehensive machine learning pipeline for sentiment analysis"
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print(f"Task: {task}")
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print("Running framework with custom agents...")
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result = framework.run(task)
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print("\n" + "=" * 50)
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print("FINAL RESULT")
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print("=" * 50)
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print(result["final_result"])
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print(f"\nTotal loops: {result['total_loops']}")
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print(f"Conversation history entries: {len(result['conversation_history'])}")
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print(f"Evaluation results: {len(result['evaluation_results'])}")
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def example_ollama_integration():
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"""
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Example using Ollama for local inference
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"""
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print("\n" + "=" * 80)
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print("OLLAMA INTEGRATION EXAMPLE")
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print("=" * 80)
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# Create framework with Ollama configuration
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framework = HierarchicalStructuredCommunicationFramework(
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name="OllamaFramework",
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max_loops=2,
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verbose=True,
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model_name="llama3:latest",
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use_ollama=True,
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ollama_base_url="http://localhost:11434/v1",
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ollama_api_key="ollama"
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)
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# Run a task with local model
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task = "Explain the concept of structured communication protocols"
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print(f"Task: {task}")
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print("Running framework with Ollama...")
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try:
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result = framework.run(task)
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print("\n" + "=" * 50)
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print("FINAL RESULT")
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print("=" * 50)
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print(result["final_result"])
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print(f"\nTotal loops: {result['total_loops']}")
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print(f"Conversation history entries: {len(result['conversation_history'])}")
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print(f"Evaluation results: {len(result['evaluation_results'])}")
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except Exception as e:
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print(f"Error with Ollama: {e}")
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print("Make sure Ollama is running: ollama serve")
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def example_structured_communication():
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"""
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Example demonstrating structured communication protocol
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"""
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print("\n" + "=" * 80)
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print("STRUCTURED COMMUNICATION EXAMPLE")
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print("=" * 80)
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# Create framework
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framework = HierarchicalStructuredCommunicationFramework(
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name="CommunicationDemo",
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verbose=True
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)
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# Demonstrate structured message sending
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print("Sending structured message...")
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structured_msg = framework.send_structured_message(
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sender="Supervisor",
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recipient="Generator",
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message="Create a technical documentation outline",
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background="For a Python library focused on data processing",
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intermediate_output="Previous research on similar libraries"
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)
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print(f"Message sent: {structured_msg.message}")
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print(f"Background: {structured_msg.background}")
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print(f"Intermediate output: {structured_msg.intermediate_output}")
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print(f"From: {structured_msg.sender} -> To: {structured_msg.recipient}")
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def example_agent_interaction():
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"""
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Example showing direct agent interaction
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"""
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print("\n" + "=" * 80)
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print("AGENT INTERACTION EXAMPLE")
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print("=" * 80)
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# Create agents
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generator = TalkHierarchicalGenerator(
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agent_name="ContentGenerator",
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verbose=True
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)
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evaluator = TalkHierarchicalEvaluator(
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agent_name="QualityEvaluator",
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evaluation_criteria=["accuracy", "clarity"],
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verbose=True
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)
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refiner = TalkHierarchicalRefiner(
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agent_name="ContentRefiner",
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verbose=True
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)
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# Generate content
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print("1. Generating content...")
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gen_result = generator.generate_with_structure(
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message="Create a brief explanation of machine learning",
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background="For beginners with no technical background",
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intermediate_output=""
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)
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print(f"Generated content: {gen_result.content[:200]}...")
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# Evaluate content
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print("\n2. Evaluating content...")
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eval_result = evaluator.evaluate_with_criterion(
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content=gen_result.content,
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criterion="clarity"
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)
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print(f"Evaluation score: {eval_result.score}/10")
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print(f"Feedback: {eval_result.feedback[:200]}...")
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# Refine content
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print("\n3. Refining content...")
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refine_result = refiner.refine_with_feedback(
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original_content=gen_result.content,
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evaluation_results=[eval_result]
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)
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print(f"Refined content: {refine_result.refined_content[:200]}...")
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print(f"Changes made: {refine_result.changes_made}")
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def main():
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"""
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Main function to run all examples
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"""
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print("SINGLE-FILE HIERARCHICAL STRUCTURED COMMUNICATION FRAMEWORK")
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print("=" * 80)
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print("This demonstrates the consolidated single-file implementation")
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print("based on the research paper: arXiv:2502.11098")
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print("=" * 80)
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try:
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# Run examples
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example_basic_usage()
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example_custom_agents()
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example_ollama_integration()
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example_structured_communication()
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example_agent_interaction()
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print("\n" + "=" * 80)
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print("ALL EXAMPLES COMPLETED SUCCESSFULLY!")
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print("=" * 80)
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print("Framework Features Demonstrated:")
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print("✓ Single-file implementation")
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print("✓ Structured Communication Protocol (M_ij, B_ij, I_ij)")
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print("✓ Hierarchical Evaluation System")
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print("✓ Iterative Refinement Process")
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print("✓ Flexible Model Configuration (OpenAI/Ollama)")
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print("✓ Custom Agent Specialization")
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print("✓ Direct Agent Interaction")
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print("✓ Convenience Aliases")
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except KeyboardInterrupt:
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print("\nInterrupted by user")
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except Exception as e:
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print(f"Error during execution: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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