"""
Pattern 014: Leader-Follower
Description:
The Leader-Follower pattern implements hierarchical agent coordination where one
leader/coordinator agent manages and delegates tasks to multiple follower/specialist
agents. The leader is responsible for task decomposition, delegation, coordination,
and result synthesis.
Components:
- Leader Agent: Coordinates, delegates, and synthesizes results
- Follower Agents: Specialized agents that execute specific tasks
- Communication Protocol: Request-response between leader and followers
- Task Assignment Strategy: How tasks are allocated to followers
Use Cases:
- Complex workflows requiring coordination
- Tasks benefiting from specialization
- Multi-step processes with dependencies
- Projects requiring project management
LangChain Implementation:
Uses a coordinator LLM for task planning and delegation, specialized LLMs
with different system prompts for specific domains, and implements task
assignment, execution tracking, and result synthesis.
Key Features:
- Dynamic task decomposition
- Specialist agent assignment
- Progress tracking and monitoring
- Hierarchical result synthesis
"""
import os
from typing import List, Dict, Any, Optional
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 TaskStatus(Enum):
"""Status of a task in the workflow."""
PENDING = "pending"
IN_PROGRESS = "in_progress"
COMPLETED = "completed"
FAILED = "failed"
class SpecialistType(Enum):
"""Types of specialist agents available."""
RESEARCH = "research"
ANALYSIS = "analysis"
WRITING = "writing"
CODING = "coding"
MATH = "math"
CREATIVE = "creative"
@dataclass
class Task:
"""Represents a task to be completed."""
task_id: str
description: str
specialist_type: SpecialistType
status: TaskStatus = TaskStatus.PENDING
result: Optional[str] = None
dependencies: List[str] = field(default_factory=list)
@dataclass
class WorkflowResult:
"""Result from complete workflow execution."""
final_output: str
tasks: List[Task]
execution_summary: str
class SpecialistAgent:
"""
A specialist agent with expertise in a specific domain.
"""
def __init__(
self,
specialist_type: SpecialistType,
model: str = "gpt-3.5-turbo",
temperature: float = 0.7
):
"""
Initialize a specialist agent.
Args:
specialist_type: Type of specialist
model: LLM model to use
temperature: Creativity level
"""
self.specialist_type = specialist_type
self.llm = ChatOpenAI(model=model, temperature=temperature)
# Define specialist system prompts
self.system_prompts = {
SpecialistType.RESEARCH: """You are a research specialist. Your expertise is in:
- Finding and summarizing information
- Conducting literature reviews
- Fact-checking and verification
- Identifying credible sources
Provide thorough, well-researched responses with citations when possible.""",
SpecialistType.ANALYSIS: """You are an analysis specialist. Your expertise is in:
- Data analysis and interpretation
- Pattern recognition
- Critical thinking and evaluation
- Drawing insights from information
Provide detailed analytical insights and evidence-based conclusions.""",
SpecialistType.WRITING: """You are a writing specialist. Your expertise is in:
- Clear and engaging writing
- Content structuring and organization
- Grammar and style
- Adapting tone for different audiences
Provide well-written, polished content.""",
SpecialistType.CODING: """You are a coding specialist. Your expertise is in:
- Writing clean, efficient code
- Debugging and problem-solving
- Best practices and design patterns
- Multiple programming languages
Provide production-ready code with explanations.""",
SpecialistType.MATH: """You are a mathematics specialist. Your expertise is in:
- Mathematical problem-solving
- Statistical analysis
- Logical reasoning
- Step-by-step calculations
Provide accurate solutions with clear explanations.""",
SpecialistType.CREATIVE: """You are a creative specialist. Your expertise is in:
- Brainstorming and ideation
- Creative problem-solving
- Innovative thinking
- Generating unique perspectives
Provide creative, out-of-the-box ideas and solutions."""
}
def execute_task(self, task: Task, context: Optional[str] = None) -> str:
"""
Execute a task using specialist expertise.
Args:
task: Task to execute
context: Optional context from previous tasks
Returns:
Task result as string
"""
system_prompt = self.system_prompts[self.specialist_type]
user_message = f"Task: {task.description}"
if context:
user_message += f"\n\nContext from previous tasks:\n{context}"
prompt = ChatPromptTemplate.from_messages([
("system", system_prompt),
("user", user_message)
])
chain = prompt | self.llm | StrOutputParser()
result = chain.invoke({})
return result
class LeaderAgent:
"""
Leader agent that coordinates and manages specialist agents.
Responsibilities:
- Decompose complex tasks into subtasks
- Assign tasks to appropriate specialists
- Monitor progress and handle dependencies
- Synthesize results into final output
"""
def __init__(self, model: str = "gpt-3.5-turbo"):
"""Initialize the leader agent."""
self.llm = ChatOpenAI(model=model, temperature=0.3)
# Initialize specialist agents
self.specialists: Dict[SpecialistType, SpecialistAgent] = {
specialist_type: SpecialistAgent(specialist_type)
for specialist_type in SpecialistType
}
def decompose_task(self, overall_task: str) -> List[Task]:
"""
Decompose an overall task into subtasks for specialists.
Args:
overall_task: The main task to decompose
Returns:
List of subtasks with assigned specialists
"""
specialist_descriptions = "\n".join([
f"- {s.value}: {self.specialists[s].system_prompts[s].split('Your expertise is in:')[1].split('Provide')[0].strip()}"
for s in SpecialistType
])
decomposition_prompt = ChatPromptTemplate.from_messages([
("system", """You are a project coordinator. Decompose tasks into subtasks and assign
to appropriate specialists. Be specific and ensure tasks are actionable."""),
("user", """Decompose this task into 3-5 subtasks:
Task: {overall_task}
Available specialists:
{specialist_descriptions}
Provide your decomposition in this exact format:
TASK 1: [specialist_type] - [clear task description]
TASK 2: [specialist_type] - [clear task description]
...
Use these specialist types: {specialist_types}""")
])
chain = decomposition_prompt | self.llm | StrOutputParser()
decomposition = chain.invoke({
"overall_task": overall_task,
"specialist_descriptions": specialist_descriptions,
"specialist_types": ", ".join([s.value for s in SpecialistType])
})
# Parse the decomposition
tasks = []
for i, line in enumerate(decomposition.strip().split("\n")):
if line.strip().startswith("TASK"):
try:
# Extract specialist type and description
parts = line.split(":", 1)[1].strip()
specialist_str, description = parts.split("-", 1)
specialist_str = specialist_str.strip().lower()
# Map to specialist type
specialist_type = None
for s in SpecialistType:
if s.value in specialist_str:
specialist_type = s
break
if specialist_type:
task = Task(
task_id=f"task_{i+1}",
description=description.strip(),
specialist_type=specialist_type
)
tasks.append(task)
except Exception as e:
print(f"Error parsing task line: {line} - {e}")
continue
return tasks
def assign_dependencies(self, tasks: List[Task]) -> List[Task]:
"""
Determine task dependencies for proper execution order.
Args:
tasks: List of tasks to analyze
Returns:
Tasks with dependencies populated
"""
# Simple heuristic: research -> analysis -> others
for i, task in enumerate(tasks):
if task.specialist_type == SpecialistType.ANALYSIS:
# Analysis depends on research
for prev_task in tasks[:i]:
if prev_task.specialist_type == SpecialistType.RESEARCH:
task.dependencies.append(prev_task.task_id)
elif task.specialist_type in [SpecialistType.WRITING, SpecialistType.CREATIVE]:
# Writing/creative depends on research and analysis
for prev_task in tasks[:i]:
if prev_task.specialist_type in [SpecialistType.RESEARCH, SpecialistType.ANALYSIS]:
task.dependencies.append(prev_task.task_id)
return tasks
def execute_workflow(self, tasks: List[Task]) -> List[Task]:
"""
Execute tasks in proper order respecting dependencies.
Args:
tasks: List of tasks to execute
Returns:
Completed tasks with results
"""
completed_tasks: Dict[str, Task] = {}
# Build context from completed tasks
def get_context_for_task(task: Task) -> str:
context_parts = []
for dep_id in task.dependencies:
if dep_id in completed_tasks:
dep_task = completed_tasks[dep_id]
context_parts.append(
f"From {dep_id} ({dep_task.specialist_type.value}):\n{dep_task.result}\n"
)
return "\n".join(context_parts)
# Execute tasks
for task in tasks:
print(f"\n[Leader] Assigning {task.task_id} to {task.specialist_type.value} specialist...")
task.status = TaskStatus.IN_PROGRESS
try:
# Get specialist agent
specialist = self.specialists[task.specialist_type]
# Get context from dependencies
context = get_context_for_task(task)
# Execute task
result = specialist.execute_task(task, context)
task.result = result
task.status = TaskStatus.COMPLETED
completed_tasks[task.task_id] = task
print(f"[Leader] ✓ {task.task_id} completed by {task.specialist_type.value}")
except Exception as e:
print(f"[Leader] ✗ {task.task_id} failed: {e}")
task.status = TaskStatus.FAILED
task.result = f"Error: {str(e)}"
return tasks
def synthesize_results(
self,
overall_task: str,
tasks: List[Task]
) -> str:
"""
Synthesize results from all tasks into final output.
Args:
overall_task: Original task description
tasks: Completed tasks with results
Returns:
Final synthesized result
"""
# Compile all task results
task_results = "\n\n".join([
f"**{task.task_id}** ({task.specialist_type.value}):\n{task.result}"
for task in tasks
if task.status == TaskStatus.COMPLETED
])
synthesis_prompt = ChatPromptTemplate.from_messages([
("system", """You are a project coordinator synthesizing results from multiple specialists.
Create a cohesive, comprehensive final output that integrates all specialist contributions."""),
("user", """Original Task: {overall_task}
Specialist Results:
{task_results}
Synthesize these results into a coherent final output that addresses the original task.
Integrate insights from all specialists into a unified response.""")
])
chain = synthesis_prompt | self.llm | StrOutputParser()
final_output = chain.invoke({
"overall_task": overall_task,
"task_results": task_results
})
return final_output
def coordinate(self, overall_task: str) -> WorkflowResult:
"""
Coordinate the complete workflow from task to final output.
Args:
overall_task: The main task to complete
Returns:
WorkflowResult with final output and execution details
"""
print(f"\n[Leader] Coordinating task: {overall_task}\n")
# Step 1: Decompose task
print("[Leader] Step 1: Decomposing task...")
tasks = self.decompose_task(overall_task)
print(f"[Leader] Created {len(tasks)} subtasks")
for task in tasks:
print(f" - {task.task_id}: {task.description[:60]}... [{task.specialist_type.value}]")
# Step 2: Assign dependencies
print("\n[Leader] Step 2: Determining task dependencies...")
tasks = self.assign_dependencies(tasks)
# Step 3: Execute workflow
print("\n[Leader] Step 3: Executing workflow...")
tasks = self.execute_workflow(tasks)
# Step 4: Synthesize results
print("\n[Leader] Step 4: Synthesizing results...")
final_output = self.synthesize_results(overall_task, tasks)
# Create execution summary
completed = sum(1 for t in tasks if t.status == TaskStatus.COMPLETED)
execution_summary = f"Completed {completed}/{len(tasks)} tasks using {len(set(t.specialist_type for t in tasks))} specialist types"
print(f"\n[Leader] Workflow complete! {execution_summary}")
return WorkflowResult(
final_output=final_output,
tasks=tasks,
execution_summary=execution_summary
)
def demonstrate_leader_follower():
"""Demonstrate the Leader-Follower pattern with various examples."""
print("=" * 80)
print("LEADER-FOLLOWER PATTERN DEMONSTRATION")
print("=" * 80)
leader = LeaderAgent()
# Test 1: Research and analysis task
print("\n" + "=" * 80)
print("TEST 1: Complex Research and Analysis Task")
print("=" * 80)
task1 = "Analyze the impact of artificial intelligence on the job market and write a summary report"
result = leader.coordinate(task1)
print("\n" + "-" * 80)
print("FINAL OUTPUT:")
print("-" * 80)
print(result.final_output)
print("\n" + "-" * 80)
print("TASK BREAKDOWN:")
print("-" * 80)
for task in result.tasks:
status_symbol = "✓" if task.status == TaskStatus.COMPLETED else "✗"
print(f"{status_symbol} {task.task_id} [{task.specialist_type.value}]: {task.description[:60]}...")
# Test 2: Creative and technical task
print("\n" + "=" * 80)
print("TEST 2: Creative + Technical Task")
print("=" * 80)
task2 = "Design a simple Python game concept and create a basic implementation plan"
result = leader.coordinate(task2)
print("\n" + "-" * 80)
print("FINAL OUTPUT:")
print("-" * 80)
print(result.final_output[:500] + "...")
print("\n" + "-" * 80)
print("SPECIALIST UTILIZATION:")
print("-" * 80)
specialist_usage = {}
for task in result.tasks:
specialist_type = task.specialist_type.value
specialist_usage[specialist_type] = specialist_usage.get(specialist_type, 0) + 1
for specialist, count in specialist_usage.items():
print(f" {specialist}: {count} task(s)")
# Test 3: Multi-domain problem
print("\n" + "=" * 80)
print("TEST 3: Multi-Domain Problem Solving")
print("=" * 80)
task3 = "Calculate the ROI of implementing renewable energy in a small business and provide recommendations"
result = leader.coordinate(task3)
print("\n" + "-" * 80)
print("FINAL OUTPUT:")
print("-" * 80)
print(result.final_output)
# Summary
print("\n" + "=" * 80)
print("SUMMARY")
print("=" * 80)
print("""
The Leader-Follower pattern demonstrates several key benefits:
1. **Specialization**: Each agent focuses on what it does best
2. **Coordination**: Leader manages workflow and dependencies
3. **Scalability**: Easy to add new specialist types
4. **Quality**: Specialist expertise improves output quality
Architecture:
- **Leader Agent**: Plans, delegates, coordinates, synthesizes
- **Follower Agents**: Execute specialized tasks with expertise
- **Task Management**: Handles dependencies and execution order
- **Result Synthesis**: Combines specialist outputs into coherent result
Use Cases:
- Complex workflows requiring multiple skills
- Projects benefiting from domain specialization
- Multi-step processes with dependencies
- Tasks requiring project management
The pattern is particularly effective when:
- Tasks can be decomposed into specialized subtasks
- Different subtasks require different expertise
- Coordination overhead is justified by quality improvement
- Clear task dependencies exist
""")
if __name__ == "__main__":
demonstrate_leader_follower()
Agentic Design Pattern: Leader Follower
Pattern 014: Leader Follower – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Leader Follower pattern implementation.
Pattern Description
Pattern 014: Leader-Follower
Description: The Leader-Follower pattern implements hierarchical agent coordination where one leader/coordinator agent manages and delegates tasks to multiple follower/specialist agents. The leader is responsible for task decomposition, delegation, coordination, and result synthesis.
Components: – Leader Agent: Coordinates, delegates, and synthesizes results – Follower Agents: Specialized agents that execute specific tasks – Communication Protocol: Request-response between leader and followers – Task Assignment Strategy: How tasks are allocated to followers
Use Cases: – Complex workflows requiring coordination – Tasks benefiting from specialization – Multi-step processes with dependencies – Projects requiring project management
LangChain Implementation: Uses a coordinator LLM for task planning and delegation, specialized LLMs with different system prompts for specific domains, and implements task assignment, execution tracking, and result synthesis.
Key Features: – Dynamic task decomposition – Specialist agent assignment – Progress tracking and monitoring – Hierarchical result synthesis
File Statistics
- Total Lines: 540
- Classes: 6
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 014: Leader-Follower
Description:
The Leader-Follower pattern implements hierarchical agent coordination where one
leader/coordinator agent manages and delegates tasks to multiple follower/specialist
agents. The leader is responsible for task decomposition, delegation, coordination,
and result synthesis.
Components:
- Leader Agent: Coordinates, delegates, and synthesizes results
- Follower Agents: Specialized agents that execute specific tasks
- Communication Protocol: Request-response between leader and followers
- Task Assignment Strategy: How tasks are allocated to followers
Use Cases:
- Complex workflows requiring coordination
- Tasks benefiting from specialization
- Multi-step processes with dependencies
- Projects requiring project management
LangChain Implementation:
Uses a coordinator LLM for task planning and delegation, specialized LLMs
with different system prompts for specific domains, and implements task
assignment, execution tracking, and result synthesis.
Key Features:
- Dynamic task decomposition
- Specialist agent assignment
- Progress tracking and monitoring
- Hierarchical result synthesis
"""
Purpose: This module implements the Leader Follower 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
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)
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: TaskStatus
Lines 47-52
Inherits from: Enum
Purpose:
Status of a task in the workflow.
Class: SpecialistType
Lines 55-62
Inherits from: Enum
Purpose:
Types of specialist agents available.
Class: Task
Lines 66-73
Purpose:
Represents a task to be completed.
Class: WorkflowResult
Lines 77-81
Purpose:
Result from complete workflow execution.
Class: SpecialistAgent
Lines 84-176
Purpose:
A specialist agent with expertise in a specific domain.
Methods:
__init__(self, specialist_type, model, temperature)
Lines 89-149
Purpose:
Initialize a specialist agent.
Args:
specialist_type: Type of specialist
model: LLM model to use
temperature: Creativity level
Parameters:
– specialist_type: Specialist type
– model: Language model instance
– temperature: Sampling temperature (0-1)
execute_task(self, task, context)
Lines 151-176
Purpose:
Execute a task using specialist expertise.
Args:
task: Task to execute
context: Optional context from previous tasks
Returns:
Task result as string
Parameters:
– task: Task
– context: Context
Returns: str
Class: LeaderAgent
Lines 179-434
Purpose:
Leader agent that coordinates and manages specialist agents.
Responsibilities:
- Decompose complex tasks into subtasks
- Assign tasks to appropriate specialists
- Monitor progress and handle dependencies
- Synthesize results into final output
Methods:
__init__(self, model)
Lines 190-198
Purpose:
Initialize the leader agent.
Parameters:
– model: Language model instance
decompose_task(self, overall_task)
Lines 200-269
Purpose:
Decompose an overall task into subtasks for specialists.
Args:
overall_task: The main task to decompose
Returns:
List of subtasks with assigned specialists
Parameters:
– overall_task: Overall task
Returns: <ast.Subscript object at 0x000001A06D633A50>
assign_dependencies(self, tasks)
Lines 271-295
Purpose:
Determine task dependencies for proper execution order.
Args:
tasks: List of tasks to analyze
Returns:
Tasks with dependencies populated
Parameters:
– tasks: Tasks
Returns: <ast.Subscript object at 0x000001A06D633810>
execute_workflow(self, tasks)
Lines 297-347
Purpose:
Execute tasks in proper order respecting dependencies.
Args:
tasks: List of tasks to execute
Returns:
Completed tasks with results
Parameters:
– tasks: Tasks
Returns: <ast.Subscript object at 0x000001A06D609D90>
synthesize_results(self, overall_task, tasks)
Lines 349-390
Purpose:
Synthesize results from all tasks into final output.
Args:
overall_task: Original task description
tasks: Completed tasks with results
Returns:
Final synthesized result
Parameters:
– overall_task: Overall task
– tasks: Tasks
Returns: str
coordinate(self, overall_task)
Lines 392-434
Purpose:
Coordinate the complete workflow from task to final output.
Args:
overall_task: The main task to complete
Returns:
WorkflowResult with final output and execution details
Parameters:
– overall_task: Overall task
Returns: WorkflowResult
Functions
demonstrate_leader_follower()
Lines 437-535
Purpose:
Demonstrate the Leader-Follower pattern with various examples.
Key Concepts Summary
Pattern Overview
The Leader Follower 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
↓
3. Prepare prompt with input data
↓
4. Invoke LLM through LangChain
↓
5. Parse and validate response
↓
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 013
- Pattern 015
- 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 Leader Follower 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: 014_leader_follower.py
Total Lines: 540
Pattern Category: AI Agent Design Pattern #014
Generated from actual implementation code
For educational and reference purposes
Leave a Reply