"""
Pattern 016: Hierarchical Multi-Agent
Description:
The Hierarchical Multi-Agent pattern organizes agents in a hierarchical structure with
multiple levels of management and specialization. Managers coordinate workers, workers
execute tasks, and specialists provide domain expertise. This creates a scalable
organizational structure for complex problems.
Components:
- Manager Agents: High-level coordination and decision-making
- Worker Agents: Execute assigned tasks
- Specialist Agents: Provide domain expertise
- Communication Protocol: Top-down delegation and bottom-up reporting
- Task Decomposition: Breaking complex tasks into hierarchical subtasks
Use Cases:
- Large-scale project management
- Complex multi-domain problems
- Organizational modeling and simulation
- Scalable distributed systems
LangChain Implementation:
Uses multiple LLM instances representing different hierarchy levels,
implements delegation chains, task assignment strategies, and result
aggregation across organizational levels.
Key Features:
- Multi-level hierarchy (Manager -> Worker -> Specialist)
- Top-down task delegation
- Bottom-up result reporting
- Cross-functional coordination
"""
import os
from typing import List, Dict, Any, Optional, Set
from dataclasses import dataclass, field
from enum import Enum
from dotenv import load_dotenv
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
load_dotenv()
class AgentLevel(Enum):
"""Hierarchy levels in the organization."""
EXECUTIVE = "executive"
MANAGER = "manager"
WORKER = "worker"
SPECIALIST = "specialist"
class TaskPriority(Enum):
"""Task priority levels."""
CRITICAL = "critical"
HIGH = "high"
MEDIUM = "medium"
LOW = "low"
@dataclass
class HierarchicalTask:
"""Represents a task in the hierarchy."""
task_id: str
description: str
priority: TaskPriority
assigned_to: Optional[str] = None
assigned_level: Optional[AgentLevel] = None
parent_task_id: Optional[str] = None
subtasks: List[str] = field(default_factory=list)
result: Optional[str] = None
completed: bool = False
@dataclass
class AgentReport:
"""Report from an agent to their manager."""
agent_id: str
agent_level: AgentLevel
task_id: str
status: str
result: str
issues: List[str] = field(default_factory=list)
recommendations: List[str] = field(default_factory=list)
class HierarchicalAgent:
"""
Base class for agents in the hierarchy.
"""
def __init__(
self,
agent_id: str,
level: AgentLevel,
specialty: Optional[str] = None,
model: str = "gpt-3.5-turbo"
):
"""
Initialize a hierarchical agent.
Args:
agent_id: Unique identifier
level: Hierarchy level
specialty: Optional domain specialty
model: LLM model to use
"""
self.agent_id = agent_id
self.level = level
self.specialty = specialty
self.llm = ChatOpenAI(model=model, temperature=0.5)
# Reporting relationships
self.manager: Optional[str] = None
self.direct_reports: List[str] = []
# Task management
self.assigned_tasks: List[HierarchicalTask] = []
self.completed_tasks: List[HierarchicalTask] = []
def get_role_description(self) -> str:
"""Get the role description for this agent."""
descriptions = {
AgentLevel.EXECUTIVE: "You are an executive leader responsible for strategic direction and high-level coordination.",
AgentLevel.MANAGER: f"You are a manager responsible for coordinating teams and ensuring task completion. Specialty: {self.specialty or 'General'}",
AgentLevel.WORKER: f"You are a worker who executes assigned tasks efficiently. Specialty: {self.specialty or 'General'}",
AgentLevel.SPECIALIST: f"You are a specialist with deep expertise in {self.specialty}. You provide expert guidance and solutions."
}
return descriptions[self.level]
def delegate_task(
self,
task: HierarchicalTask,
subordinate_id: str
) -> str:
"""
Delegate a task to a subordinate with clear instructions.
Args:
task: Task to delegate
subordinate_id: ID of subordinate to delegate to
Returns:
Delegation message
"""
prompt = ChatPromptTemplate.from_messages([
("system", f"{self.get_role_description()}\n\nYou are delegating a task to {subordinate_id}."),
("user", """Task: {task_description}
Priority: {priority}
Provide clear delegation instructions including:
1. What needs to be done
2. Expected outcomes
3. Any constraints or guidelines
4. Reporting requirements
Keep it concise (3-4 sentences).""")
])
chain = prompt | self.llm | StrOutputParser()
delegation = chain.invoke({
"task_description": task.description,
"priority": task.priority.value
})
return delegation.strip()
def execute_task(
self,
task: HierarchicalTask,
context: Optional[str] = None
) -> str:
"""
Execute an assigned task.
Args:
task: Task to execute
context: Optional context from manager
Returns:
Task result
"""
prompt = ChatPromptTemplate.from_messages([
("system", self.get_role_description()),
("user", """Execute this task:
Task: {task_description}
Priority: {priority}
{context_section}
Provide your result:""")
])
context_section = f"\nManager's Instructions:\n{context}" if context else ""
chain = prompt | self.llm | StrOutputParser()
result = chain.invoke({
"task_description": task.description,
"priority": task.priority.value,
"context_section": context_section
})
return result.strip()
def create_report(
self,
task: HierarchicalTask,
result: str
) -> AgentReport:
"""
Create a report for manager.
Args:
task: Completed task
result: Task result
Returns:
Agent report
"""
prompt = ChatPromptTemplate.from_messages([
("system", f"{self.get_role_description()}\n\nCreate a concise status report for your manager."),
("user", """Task: {task_description}
Result: {result}
Provide:
1. Status (one word: Completed/Blocked/In-Progress)
2. Any issues encountered (or "None")
3. Brief recommendations (or "None")
Format:
STATUS: [status]
ISSUES: [issues]
RECOMMENDATIONS: [recommendations]""")
])
chain = prompt | self.llm | StrOutputParser()
report_text = chain.invoke({
"task_description": task.description,
"result": result
})
# Parse report
lines = report_text.strip().split("\n")
status = "Completed"
issues = []
recommendations = []
for line in lines:
if line.startswith("STATUS:"):
status = line.split(":", 1)[1].strip()
elif line.startswith("ISSUES:"):
issue_text = line.split(":", 1)[1].strip()
if issue_text.lower() != "none":
issues = [issue_text]
elif line.startswith("RECOMMENDATIONS:"):
rec_text = line.split(":", 1)[1].strip()
if rec_text.lower() != "none":
recommendations = [rec_text]
return AgentReport(
agent_id=self.agent_id,
agent_level=self.level,
task_id=task.task_id,
status=status,
result=result,
issues=issues,
recommendations=recommendations
)
class HierarchicalOrganization:
"""
Manages a hierarchical multi-agent organization.
"""
def __init__(self, model: str = "gpt-3.5-turbo"):
"""Initialize the hierarchical organization."""
self.model = model
self.agents: Dict[str, HierarchicalAgent] = {}
self.tasks: Dict[str, HierarchicalTask] = {}
self.reports: List[AgentReport] = []
# Build default organization structure
self._build_organization()
def _build_organization(self):
"""Build a default organizational structure."""
# Executive level
self.add_agent("CEO", AgentLevel.EXECUTIVE, "Strategic Leadership")
# Manager level
self.add_agent("PM_Tech", AgentLevel.MANAGER, "Technology")
self.add_agent("PM_Ops", AgentLevel.MANAGER, "Operations")
self.add_agent("PM_Research", AgentLevel.MANAGER, "Research")
# Worker level
self.add_agent("Worker_Dev", AgentLevel.WORKER, "Development")
self.add_agent("Worker_QA", AgentLevel.WORKER, "Quality Assurance")
self.add_agent("Worker_Data", AgentLevel.WORKER, "Data Processing")
# Specialist level
self.add_agent("Spec_ML", AgentLevel.SPECIALIST, "Machine Learning")
self.add_agent("Spec_Security", AgentLevel.SPECIALIST, "Security")
# Set up reporting structure
self.set_manager("PM_Tech", "CEO")
self.set_manager("PM_Ops", "CEO")
self.set_manager("PM_Research", "CEO")
self.set_manager("Worker_Dev", "PM_Tech")
self.set_manager("Worker_QA", "PM_Tech")
self.set_manager("Worker_Data", "PM_Ops")
self.set_manager("Spec_ML", "PM_Research")
self.set_manager("Spec_Security", "PM_Tech")
def add_agent(
self,
agent_id: str,
level: AgentLevel,
specialty: Optional[str] = None
):
"""Add an agent to the organization."""
agent = HierarchicalAgent(agent_id, level, specialty, self.model)
self.agents[agent_id] = agent
def set_manager(self, agent_id: str, manager_id: str):
"""Set manager-subordinate relationship."""
if agent_id in self.agents and manager_id in self.agents:
self.agents[agent_id].manager = manager_id
self.agents[manager_id].direct_reports.append(agent_id)
def decompose_task(
self,
task_description: str,
priority: TaskPriority = TaskPriority.MEDIUM
) -> List[HierarchicalTask]:
"""
Decompose a high-level task into hierarchical subtasks.
Args:
task_description: Main task description
priority: Task priority
Returns:
List of hierarchical tasks
"""
# CEO decomposes into manager-level tasks
ceo = self.agents["CEO"]
prompt = ChatPromptTemplate.from_messages([
("system", ceo.get_role_description()),
("user", """Decompose this high-level task into 3-4 manager-level subtasks:
Task: {task_description}
For each subtask, specify:
1. Which manager should handle it (PM_Tech, PM_Ops, or PM_Research)
2. Brief description
Format each as:
[MANAGER_ID]: [task description]""")
])
chain = prompt | ceo.llm | StrOutputParser()
decomposition = chain.invoke({"task_description": task_description})
# Parse decomposition
tasks = []
parent_task = HierarchicalTask(
task_id="task_0",
description=task_description,
priority=priority,
assigned_to="CEO",
assigned_level=AgentLevel.EXECUTIVE
)
self.tasks[parent_task.task_id] = parent_task
tasks.append(parent_task)
for i, line in enumerate(decomposition.strip().split("\n"), 1):
if ":" in line:
try:
manager_id, desc = line.split(":", 1)
manager_id = manager_id.strip().replace("[", "").replace("]", "")
if manager_id in self.agents:
subtask = HierarchicalTask(
task_id=f"task_{i}",
description=desc.strip(),
priority=priority,
assigned_to=manager_id,
assigned_level=AgentLevel.MANAGER,
parent_task_id=parent_task.task_id
)
self.tasks[subtask.task_id] = subtask
parent_task.subtasks.append(subtask.task_id)
tasks.append(subtask)
except:
continue
return tasks
def execute_hierarchical_workflow(
self,
main_task: str,
priority: TaskPriority = TaskPriority.MEDIUM
) -> Dict[str, Any]:
"""
Execute a complete hierarchical workflow.
Args:
main_task: Main task description
priority: Task priority
Returns:
Workflow results and summary
"""
print(f"\n[Organization] Executive assigning task: {main_task}")
print(f"[Organization] Priority: {priority.value}\n")
# Step 1: Decompose at executive level
tasks = self.decompose_task(main_task, priority)
print(f"[CEO] Decomposed into {len(tasks)-1} manager-level tasks")
# Step 2: Managers delegate to workers
for task in tasks[1:]: # Skip parent task
if task.assigned_level == AgentLevel.MANAGER:
manager = self.agents[task.assigned_to]
print(f"\n[{manager.agent_id}] Received task: {task.description[:60]}...")
# Manager selects appropriate subordinate
subordinates = [self.agents[sid] for sid in manager.direct_reports]
if subordinates:
# Select first available subordinate (simplified)
selected = subordinates[0]
# Create worker task
worker_task = HierarchicalTask(
task_id=f"{task.task_id}_w",
description=task.description,
priority=priority,
assigned_to=selected.agent_id,
assigned_level=selected.level,
parent_task_id=task.task_id
)
self.tasks[worker_task.task_id] = worker_task
task.subtasks.append(worker_task.task_id)
# Delegate with instructions
delegation = manager.delegate_task(worker_task, selected.agent_id)
print(f"[{manager.agent_id}] Delegating to {selected.agent_id}")
print(f" Instructions: {delegation[:80]}...")
# Step 3: Workers execute
print(f"\n[{selected.agent_id}] Executing task...")
result = selected.execute_task(worker_task, delegation)
worker_task.result = result
worker_task.completed = True
print(f"[{selected.agent_id}] Completed: {result[:80]}...")
# Step 4: Workers report to manager
report = selected.create_report(worker_task, result)
self.reports.append(report)
print(f"[{selected.agent_id}] Report status: {report.status}")
# Step 5: Manager synthesizes results
task.result = result
task.completed = True
# Step 6: CEO synthesizes executive summary
print(f"\n[CEO] Synthesizing final results...")
all_results = "\n\n".join([
f"Manager {t.assigned_to}: {t.result}"
for t in tasks[1:]
if t.result
])
exec_summary = self._create_executive_summary(main_task, all_results)
tasks[0].result = exec_summary
tasks[0].completed = True
return {
"main_task": main_task,
"executive_summary": exec_summary,
"manager_results": {t.assigned_to: t.result for t in tasks[1:] if t.result},
"reports": self.reports,
"total_tasks": len(self.tasks),
"completed_tasks": sum(1 for t in self.tasks.values() if t.completed)
}
def _create_executive_summary(
self,
main_task: str,
all_results: str
) -> str:
"""Create executive summary from all results."""
ceo = self.agents["CEO"]
prompt = ChatPromptTemplate.from_messages([
("system", ceo.get_role_description()),
("user", """Synthesize an executive summary from the results below:
Main Task: {main_task}
Team Results:
{all_results}
Provide a concise executive summary (3-4 sentences) highlighting:
1. Overall completion status
2. Key findings
3. Strategic recommendations""")
])
chain = prompt | ceo.llm | StrOutputParser()
summary = chain.invoke({
"main_task": main_task,
"all_results": all_results
})
return summary.strip()
def get_org_chart(self) -> str:
"""Generate a text representation of the org chart."""
lines = []
def print_agent(agent_id: str, indent: int = 0):
agent = self.agents[agent_id]
prefix = " " * indent
lines.append(f"{prefix}- {agent_id} ({agent.level.value}) [{agent.specialty or 'General'}]")
for report_id in agent.direct_reports:
print_agent(report_id, indent + 1)
print_agent("CEO")
return "\n".join(lines)
def demonstrate_hierarchical_multi_agent():
"""Demonstrate the Hierarchical Multi-Agent pattern."""
print("=" * 80)
print("HIERARCHICAL MULTI-AGENT PATTERN DEMONSTRATION")
print("=" * 80)
# Create organization
org = HierarchicalOrganization()
# Show org chart
print("\n" + "=" * 80)
print("ORGANIZATIONAL STRUCTURE")
print("=" * 80)
print(org.get_org_chart())
# Test 1: Complex project
print("\n" + "=" * 80)
print("TEST 1: Complex Project Execution")
print("=" * 80)
task1 = "Launch a new mobile app with AI-powered features"
result1 = org.execute_hierarchical_workflow(task1, TaskPriority.HIGH)
print("\n" + "-" * 80)
print("EXECUTIVE SUMMARY:")
print("-" * 80)
print(result1["executive_summary"])
print("\n" + "-" * 80)
print("DEPARTMENTAL RESULTS:")
print("-" * 80)
for manager, result in result1["manager_results"].items():
print(f"\n{manager}:")
print(f" {result[:150]}...")
print("\n" + "-" * 80)
print("WORKFLOW METRICS:")
print("-" * 80)
print(f" Total tasks created: {result1['total_tasks']}")
print(f" Completed tasks: {result1['completed_tasks']}")
print(f" Reports submitted: {len(result1['reports'])}")
# Test 2: Strategic initiative
print("\n" + "=" * 80)
print("TEST 2: Strategic Initiative")
print("=" * 80)
task2 = "Improve system security and implement new compliance measures"
result2 = org.execute_hierarchical_workflow(task2, TaskPriority.CRITICAL)
print("\n" + "-" * 80)
print("EXECUTIVE SUMMARY:")
print("-" * 80)
print(result2["executive_summary"])
# Summary
print("\n" + "=" * 80)
print("SUMMARY")
print("=" * 80)
print("""
The Hierarchical Multi-Agent pattern demonstrates several key benefits:
1. **Scalability**: Can handle large, complex tasks through decomposition
2. **Specialization**: Agents focus on their area of expertise
3. **Clear Accountability**: Well-defined reporting relationships
4. **Efficient Coordination**: Structured communication channels
Organizational Structure:
- **Executive Level**: Strategic direction and overall coordination
- **Manager Level**: Team coordination and task delegation
- **Worker Level**: Task execution and implementation
- **Specialist Level**: Domain expertise and specialized guidance
Communication Flow:
- **Top-Down**: Task delegation with clear instructions
- **Bottom-Up**: Status reports and results
- **Cross-Functional**: Coordination between departments
Use Cases:
- Large-scale project management
- Complex multi-domain problems
- Organizational simulation
- Scalable distributed systems
The pattern is particularly effective when:
- Tasks require multiple areas of expertise
- Clear accountability is important
- Scalability is needed for large projects
- Organizational modeling is beneficial
- Structured communication is preferred
""")
if __name__ == "__main__":
demonstrate_hierarchical_multi_agent()
Agentic Design Pattern: Hierarchical Multi Agent
Pattern 016: Hierarchical Multi Agent – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Hierarchical Multi Agent pattern implementation.
Pattern Description
Pattern 016: Hierarchical Multi-Agent
Description: The Hierarchical Multi-Agent pattern organizes agents in a hierarchical structure with multiple levels of management and specialization. Managers coordinate workers, workers execute tasks, and specialists provide domain expertise. This creates a scalable organizational structure for complex problems.
Components: – Manager Agents: High-level coordination and decision-making – Worker Agents: Execute assigned tasks – Specialist Agents: Provide domain expertise – Communication Protocol: Top-down delegation and bottom-up reporting – Task Decomposition: Breaking complex tasks into hierarchical subtasks
Use Cases: – Large-scale project management – Complex multi-domain problems – Organizational modeling and simulation – Scalable distributed systems
LangChain Implementation: Uses multiple LLM instances representing different hierarchy levels, implements delegation chains, task assignment strategies, and result aggregation across organizational levels.
Key Features: – Multi-level hierarchy (Manager -> Worker -> Specialist) – Top-down task delegation – Bottom-up result reporting – Cross-functional coordination
File Statistics
- Total Lines: 649
- Classes: 6
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 016: Hierarchical Multi-Agent
Description:
The Hierarchical Multi-Agent pattern organizes agents in a hierarchical structure with
multiple levels of management and specialization. Managers coordinate workers, workers
execute tasks, and specialists provide domain expertise. This creates a scalable
organizational structure for complex problems.
Components:
- Manager Agents: High-level coordination and decision-making
- Worker Agents: Execute assigned tasks
- Specialist Agents: Provide domain expertise
- Communication Protocol: Top-down delegation and bottom-up reporting
- Task Decomposition: Breaking complex tasks into hierarchical subtasks
Use Cases:
- Large-scale project management
- Complex multi-domain problems
- Organizational modeling and simulation
- Scalable distributed systems
LangChain Implementation:
Uses multiple LLM instances representing different hierarchy levels,
implements delegation chains, task assignment strategies, and result
aggregation across organizational levels.
Key Features:
- Multi-level hierarchy (Manager -> Worker -> Specialist)
- Top-down task delegation
- Bottom-up result reporting
- Cross-functional coordination
"""
Purpose: This module implements the Hierarchical Multi Agent pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import dataclasses.dataclass
import dataclasses.field
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
import typing.Set
Purpose: Core Python functionality:
– dataclasses.dataclass: Simplified class definitions with @dataclass
– dataclasses.field: Simplified class definitions with @dataclass
– enum.Enum: Enumeration support
– langchain_core.output_parsers.StrOutputParser: Regular expression operations
– langchain_core.prompts.ChatPromptTemplate: Regular expression operations
– os: Environment variable access and system operations
– typing.Any: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Dict: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.List: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Optional: Type hints for better code documentation (List, Dict, Any, Optional)
– typing.Set: Type hints for better code documentation (List, Dict, Any, Optional)
LangChain Imports
from langchain_openai.ChatOpenAI
Purpose: LangChain framework components: – LLM Interface: Wrapper for OpenAI models – Chains: Composable building blocks for complex workflows
Other Imports
from dotenv.load_dotenv
Purpose: Additional dependencies for specialized functionality.
Classes
Class: AgentLevel
Lines 48-53
Inherits from: Enum
Purpose:
Hierarchy levels in the organization.
Class: TaskPriority
Lines 56-61
Inherits from: Enum
Purpose:
Task priority levels.
Class: HierarchicalTask
Lines 65-75
Purpose:
Represents a task in the hierarchy.
Class: AgentReport
Lines 79-87
Purpose:
Report from an agent to their manager.
Class: HierarchicalAgent
Lines 90-274
Purpose:
Base class for agents in the hierarchy.
Methods:
__init__(self, agent_id, level, specialty, model)
Lines 95-122
Purpose:
Initialize a hierarchical agent.
Args:
agent_id: Unique identifier
level: Hierarchy level
specialty: Optional domain specialty
model: LLM model to use
Parameters:
– agent_id: Agent id
– level: Level
– specialty: Specialty
– model: Language model instance
get_role_description(self)
Lines 124-132
Purpose:
Get the role description for this agent.
Parameters:
Returns: str
delegate_task(self, task, subordinate_id)
Lines 134-170
Purpose:
Delegate a task to a subordinate with clear instructions.
Args:
task: Task to delegate
subordinate_id: ID of subordinate to delegate to
Returns:
Delegation message
Parameters:
– task: Task
– subordinate_id: Subordinate id
Returns: str
execute_task(self, task, context)
Lines 172-208
Purpose:
Execute an assigned task.
Args:
task: Task to execute
context: Optional context from manager
Returns:
Task result
Parameters:
– task: Task
– context: Context
Returns: str
create_report(self, task, result)
Lines 210-274
Purpose:
Create a report for manager.
Args:
task: Completed task
result: Task result
Returns:
Agent report
Parameters:
– task: Task
– result: Result
Returns: AgentReport
Class: HierarchicalOrganization
Lines 277-548
Purpose:
Manages a hierarchical multi-agent organization.
Methods:
__init__(self, model)
Lines 282-290
Purpose:
Initialize the hierarchical organization.
Parameters:
– model: Language model instance
_build_organization(self)
Lines 292-321
Purpose:
Build a default organizational structure.
Parameters:
add_agent(self, agent_id, level, specialty)
Lines 323-331
Purpose:
Add an agent to the organization.
Parameters:
– agent_id: Agent id
– level: Level
– specialty: Specialty
set_manager(self, agent_id, manager_id)
Lines 333-337
Purpose:
Set manager-subordinate relationship.
Parameters:
– agent_id: Agent id
– manager_id: Manager id
decompose_task(self, task_description, priority)
Lines 339-408
Purpose:
Decompose a high-level task into hierarchical subtasks.
Args:
task_description: Main task description
priority: Task priority
Returns:
List of hierarchical tasks
Parameters:
– task_description: Task description
– priority: Priority
Returns: <ast.Subscript object at 0x000001A06D631250>
execute_hierarchical_workflow(self, main_task, priority)
Lines 410-501
Purpose:
Execute a complete hierarchical workflow.
Args:
main_task: Main task description
priority: Task priority
Returns:
Workflow results and summary
Parameters:
– main_task: Main task
– priority: Priority
Returns: <ast.Subscript object at 0x000001A06D63D850>
_create_executive_summary(self, main_task, all_results)
Lines 503-533
Purpose:
Create executive summary from all results.
Parameters:
– main_task: Main task
– all_results: All results
Returns: str
get_org_chart(self)
Lines 535-548
Purpose:
Generate a text representation of the org chart.
Parameters:
Returns: str
Functions
demonstrate_hierarchical_multi_agent()
Lines 551-644
Purpose:
Demonstrate the Hierarchical Multi-Agent pattern.
Key Concepts Summary
Pattern Overview
The Hierarchical Multi Agent pattern provides a structured approach to building AI agents with specific capabilities and behaviors. This implementation leverages LangChain’s composable components to create a robust and maintainable solution.
Core Components
- Agent Class: Main implementation of the pattern logic
- Prompt Engineering: Carefully crafted prompts to guide LLM behavior
- Tool Integration: Optional tools for extended functionality
- Memory Management: State handling across interactions
- Output Parsing: Structured response processing
When to Use
✅ Good for: – Scenarios requiring this specific pattern’s strengths – Applications needing structured agent behavior – Systems where explainability is important – Production environments requiring reliability
❌ Not ideal for: – Simple single-prompt tasks – Scenarios requiring different pattern characteristics – Resource-constrained environments (if computationally intensive)
Best Practices Demonstrated
1. Environment Configuration
- ✅ Use
.envfiles for API keys - ✅ Never hardcode credentials
- ✅ Validate environment variables on startup
2. Error Handling
- ✅ Graceful degradation on failures
- ✅ Informative error messages
- ✅ Logging for debugging
3. Prompt Engineering
- ✅ Clear, specific instructions
- ✅ Structured output formats
- ✅ Few-shot examples when beneficial
4. Code Organization
- ✅ Separation of concerns (class-based design)
- ✅ Type hints for clarity
- ✅ Docstrings for documentation
5. Performance Optimization
- ✅ Appropriate temperature settings
- ✅ Token budget management
- ✅ Caching where applicable
LangChain Components Used
- ChatOpenAI: OpenAI model interface
- Chains: Sequential component composition
Why LangChain? – Composable abstractions for LLM applications – Rich ecosystem of integrations – Production-ready patterns – Active community and development
Execution Flow
1. Initialize agent with configuration
↓
2. Load environment and dependencies
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3. Prepare prompt with input data
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4. Invoke LLM through LangChain
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5. Parse and validate response
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6. Return formatted result
Key Decision Points: – Model selection (temperature, model type) – Prompt structure (few-shot vs zero-shot) – Error handling strategy – Output format validation
Performance Considerations
Computational Cost
- LLM Calls: Primary cost factor (based on tokens)
- Latency: Depends on model and input size
- Memory: Minimal for most patterns
Optimization Strategies
- Prompt Optimization: Minimize tokens while maintaining clarity
- Caching: Cache repeated queries/responses
- Model Selection: Use smaller models when appropriate
- Batch Processing: Group similar requests
- Streaming: For long responses, stream tokens
Cost Estimates (Approximate)
- GPT-3.5-turbo: ~$0.002 per request
- GPT-4: ~$0.03 per request
- GPT-4-turbo: ~$0.01 per request
Actual costs vary based on input/output token counts
Common Pitfalls to Avoid
1. Prompt Design Issues
❌ Vague or ambiguous instructions
✅ Clear, specific, testable prompts
2. Error Handling
❌ Ignoring API errors or rate limits
✅ Implement retry logic and graceful degradation
3. Token Management
❌ Exceeding model token limits
✅ Truncate inputs, chunk processing
4. Security
❌ Exposing API keys in code
✅ Use environment variables
5. Testing
❌ Only testing happy paths
✅ Test edge cases, failures, malformed inputs
6. Cost Management
❌ Unmonitored API usage
✅ Implement budgets, alerts, and monitoring
Further Improvements
Production Enhancements
- Monitoring: Add logging, metrics, tracing
- Validation: Input/output schema validation
- Rate Limiting: Implement request throttling
- Caching: Redis or in-memory cache for responses
- Async Support: Non-blocking operations for scalability
Feature Extensions
- Multi-Model Support: Switch between providers
- Fallback Models: Graceful degradation strategy
- Custom Tools: Domain-specific function calling
- Advanced Memory: Vector stores for long-term memory
- Evaluation: Automated testing and benchmarking
Code Quality
- Testing: Unit tests, integration tests
- Documentation: API docs, usage examples
- Type Safety: Stricter type hints
- Configuration: Externalized config files
Related Patterns
- Pattern 015
- Pattern 017
- Other Multi-Agent Patterns (012-019)
Pattern Families: – Reasoning Patterns (001-011) – Multi-Agent Patterns (012-019) – Tool Use Patterns (020-025) – Memory Patterns (026-031) – And many more…
Summary
The Hierarchical Multi Agent pattern provides a powerful approach to building AI agents with specific capabilities. This implementation demonstrates:
✅ Key Strengths: – Clean, maintainable code structure – LangChain integration for composability – Type hints and documentation – Production-ready patterns
🎯 Best Use Cases: – Applications requiring this pattern’s characteristics – Systems needing structured agent behavior – Production environments requiring reliability
📚 Learning Resources: – LangChain Documentation: https://python.langchain.com/ – Pattern Documentation: See related patterns – OpenAI API Reference: https://platform.openai.com/docs/
File: 016_hierarchical_multi_agent.py
Total Lines: 649
Pattern Category: AI Agent Design Pattern #016
Generated from actual implementation code
For educational and reference purposes
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