"""
Pattern 017: Competitive Multi-Agent
Description:
The Competitive Multi-Agent pattern uses multiple agents that compete to produce
the best solution. Agents work independently, their solutions are evaluated against
each other, and the winning solution is selected. This competition drives quality
improvement and innovation.
Components:
- Competitor Agents: Multiple agents with different strategies
- Evaluation System: Judges solutions across multiple criteria
- Competition Framework: Manages rounds and determines winners
- Selection Mechanism: Chooses best solution(s)
Use Cases:
- Creative tasks (writing, design, ideation)
- Optimization problems with multiple local optima
- Generating diverse solution approaches
- Quality improvement through competition
LangChain Implementation:
Uses multiple LLM agents with different configurations competing on tasks,
implements multi-criteria evaluation, tournament-style competition, and
quality-driven selection mechanisms.
Key Features:
- Tournament-style competitions
- Multi-criteria evaluation
- Diverse agent strategies
- Winner selection and ranking
"""
import os
from typing import List, Dict, Any, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import random
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 CompetitionMode(Enum):
"""Modes of competition."""
TOURNAMENT = "tournament" # Bracket-style elimination
ROUND_ROBIN = "round_robin" # All agents compete in every round
SURVIVAL = "survival" # Lowest scorers eliminated each round
@dataclass
class Solution:
"""Represents a solution from a competitor."""
agent_id: str
content: str
round_number: int
scores: Dict[str, float] = None # Criterion -> Score
total_score: float = 0.0
rank: Optional[int] = None
@dataclass
class CompetitionResult:
"""Results from a competition."""
winner: Solution
all_solutions: List[Solution]
rounds: int
mode: str
class CompetitorAgent:
"""
An agent that competes to produce the best solution.
"""
def __init__(
self,
agent_id: str,
strategy: str,
model: str = "gpt-3.5-turbo",
temperature: float = 0.7
):
"""
Initialize a competitor agent.
Args:
agent_id: Unique identifier
strategy: Competitive strategy description
model: LLM model to use
temperature: Creativity level
"""
self.agent_id = agent_id
self.strategy = strategy
self.llm = ChatOpenAI(model=model, temperature=temperature)
self.wins = 0
self.total_score = 0.0
def generate_solution(
self,
problem: str,
round_number: int,
previous_best: Optional[Solution] = None
) -> Solution:
"""
Generate a solution for the problem.
Args:
problem: Problem to solve
round_number: Current round number
previous_best: Previous best solution (for improvement)
Returns:
Generated solution
"""
strategy_context = f"\nYour strategy: {self.strategy}"
if previous_best and round_number > 1:
improvement_context = f"\n\nPrevious best solution to beat:\n{previous_best.content}\n\nImprove upon this!"
else:
improvement_context = ""
prompt = ChatPromptTemplate.from_messages([
("system", f"""You are a competitor in a solution competition.{strategy_context}
Generate your BEST solution. Be creative, thorough, and competitive!"""),
("user", """Problem: {problem}{improvement_context}
Your solution (aim to win the competition!):""")
])
chain = prompt | self.llm | StrOutputParser()
content = chain.invoke({
"problem": problem,
"improvement_context": improvement_context
})
return Solution(
agent_id=self.agent_id,
content=content.strip(),
round_number=round_number
)
class CompetitionJudge:
"""
Evaluates and ranks solutions in competitions.
"""
def __init__(
self,
criteria: List[str],
model: str = "gpt-3.5-turbo"
):
"""
Initialize the judge.
Args:
criteria: List of evaluation criteria
model: LLM model for judging
"""
self.criteria = criteria
self.llm = ChatOpenAI(model=model, temperature=0.2)
def evaluate_solution(
self,
problem: str,
solution: Solution
) -> Solution:
"""
Evaluate a solution across all criteria.
Args:
problem: Original problem
solution: Solution to evaluate
Returns:
Solution with scores populated
"""
scores = {}
for criterion in self.criteria:
score = self._score_criterion(problem, solution.content, criterion)
scores[criterion] = score
solution.scores = scores
solution.total_score = sum(scores.values()) / len(scores)
return solution
def _score_criterion(
self,
problem: str,
solution: str,
criterion: str
) -> float:
"""Score a solution on a specific criterion."""
prompt = ChatPromptTemplate.from_messages([
("system", f"You are an expert judge evaluating solutions based on: {criterion}"),
("user", """Problem: {problem}
Solution: {solution}
Rate this solution on {criterion} from 0.0 (worst) to 10.0 (best).
Consider only this specific criterion.
Provide only a number between 0.0 and 10.0:""")
])
chain = prompt | self.llm | StrOutputParser()
try:
score_str = chain.invoke({
"problem": problem,
"solution": solution,
"criterion": criterion
})
score = float(score_str.strip())
return max(0.0, min(10.0, score))
except:
return 5.0 # Default moderate score
def rank_solutions(
self,
solutions: List[Solution]
) -> List[Solution]:
"""
Rank solutions by total score.
Args:
solutions: List of solutions to rank
Returns:
Ranked solutions (highest to lowest)
"""
sorted_solutions = sorted(
solutions,
key=lambda s: s.total_score,
reverse=True
)
for rank, solution in enumerate(sorted_solutions, 1):
solution.rank = rank
return sorted_solutions
class CompetitiveSystem:
"""
Manages competitive multi-agent system.
"""
def __init__(
self,
evaluation_criteria: Optional[List[str]] = None,
model: str = "gpt-3.5-turbo"
):
"""
Initialize competitive system.
Args:
evaluation_criteria: Criteria for evaluating solutions
model: LLM model for agents
"""
if evaluation_criteria is None:
evaluation_criteria = [
"Creativity and Innovation",
"Practicality and Feasibility",
"Completeness and Detail",
"Clarity and Communication"
]
self.criteria = evaluation_criteria
self.judge = CompetitionJudge(evaluation_criteria, model)
self.agents: List[CompetitorAgent] = []
self.model = model
def add_competitor(
self,
agent_id: str,
strategy: str,
temperature: float = 0.7
):
"""Add a competitor to the system."""
agent = CompetitorAgent(agent_id, strategy, self.model, temperature)
self.agents.append(agent)
def run_competition(
self,
problem: str,
mode: CompetitionMode = CompetitionMode.ROUND_ROBIN,
rounds: int = 2
) -> CompetitionResult:
"""
Run a competition among agents.
Args:
problem: Problem for agents to solve
mode: Competition mode
rounds: Number of competition rounds
Returns:
Competition results
"""
print(f"\n[Competition] Starting {mode.value} with {len(self.agents)} competitors")
print(f"[Competition] {rounds} rounds, criteria: {', '.join(self.criteria)}\n")
all_solutions: List[Solution] = []
current_best: Optional[Solution] = None
for round_num in range(1, rounds + 1):
print(f"{'=' * 80}")
print(f"ROUND {round_num}/{rounds}")
print(f"{'=' * 80}\n")
round_solutions = []
# Each agent generates a solution
for agent in self.agents:
print(f"[{agent.agent_id}] Generating solution (strategy: {agent.strategy})...")
solution = agent.generate_solution(
problem,
round_num,
current_best
)
# Evaluate solution
solution = self.judge.evaluate_solution(problem, solution)
print(f"[{agent.agent_id}] Score: {solution.total_score:.2f}/10.0")
print(f"[{agent.agent_id}] Solution: {solution.content[:80]}...\n")
round_solutions.append(solution)
all_solutions.append(solution)
# Rank this round's solutions
ranked = self.judge.rank_solutions(round_solutions)
print(f"\n[Judge] Round {round_num} Rankings:")
for solution in ranked[:3]: # Top 3
print(f" #{solution.rank} {solution.agent_id}: {solution.total_score:.2f}/10.0")
# Update current best
if ranked[0].total_score > (current_best.total_score if current_best else 0):
current_best = ranked[0]
print(f"\n[Judge] New leader: {current_best.agent_id}!")
# Handle elimination for survival mode
if mode == CompetitionMode.SURVIVAL and round_num List[Tuple[str, int, float]]:
"""
Get agent leaderboard.
Returns:
List of (agent_id, wins, avg_score) tuples
"""
return sorted(
[(a.agent_id, a.wins, a.total_score) for a in self.agents],
key=lambda x: (x[1], x[2]),
reverse=True
)
def demonstrate_competitive_multi_agent():
"""Demonstrate the Competitive Multi-Agent pattern."""
print("=" * 80)
print("COMPETITIVE MULTI-AGENT PATTERN DEMONSTRATION")
print("=" * 80)
# Test 1: Creative competition
print("\n" + "=" * 80)
print("TEST 1: Creative Problem-Solving Competition")
print("=" * 80)
system1 = CompetitiveSystem()
# Add competitors with different strategies
system1.add_competitor(
"Innovator",
"Focus on novel, cutting-edge, unconventional ideas",
temperature=0.9
)
system1.add_competitor(
"Pragmatist",
"Focus on practical, proven, implementable solutions",
temperature=0.5
)
system1.add_competitor(
"Optimizer",
"Focus on efficiency, cost-effectiveness, and ROI",
temperature=0.6
)
system1.add_competitor(
"User-Centric",
"Focus on user experience, accessibility, and satisfaction",
temperature=0.7
)
problem1 = "Design a feature to increase user engagement in a productivity app"
result1 = system1.run_competition(
problem1,
mode=CompetitionMode.ROUND_ROBIN,
rounds=2
)
print(f"\n{'*' * 80}")
print("WINNER:")
print(f"{'*' * 80}")
print(f"Agent: {result1.winner.agent_id}")
print(f"Score: {result1.winner.total_score:.2f}/10.0")
print(f"Round: {result1.winner.round_number}")
print(f"\nWinning Solution:\n{result1.winner.content}")
print(f"\n{'-' * 80}")
print("SCORE BREAKDOWN:")
print(f"{'-' * 80}")
for criterion, score in result1.winner.scores.items():
print(f" {criterion}: {score:.2f}/10.0")
# Test 2: Survival mode competition
print("\n" + "=" * 80)
print("TEST 2: Survival Mode Competition")
print("=" * 80)
system2 = CompetitiveSystem(
evaluation_criteria=[
"Innovation",
"Feasibility",
"Impact"
]
)
system2.add_competitor("Alpha", "Aggressive innovation", 0.9)
system2.add_competitor("Beta", "Balanced approach", 0.7)
system2.add_competitor("Gamma", "Conservative and safe", 0.5)
problem2 = "Propose a strategy to reduce customer churn by 30%"
result2 = system2.run_competition(
problem2,
mode=CompetitionMode.SURVIVAL,
rounds=2
)
print(f"\n{'*' * 80}")
print("SURVIVOR & WINNER:")
print(f"{'*' * 80}")
print(f"Agent: {result2.winner.agent_id}")
print(f"Score: {result2.winner.total_score:.2f}/10.0")
print(f"\nWinning Solution:\n{result2.winner.content}")
# Test 3: Tournament with diverse strategies
print("\n" + "=" * 80)
print("TEST 3: Multi-Round Tournament")
print("=" * 80)
system3 = CompetitiveSystem()
system3.add_competitor("Creative", "Maximum creativity and originality", 1.0)
system3.add_competitor("Analytical", "Data-driven and analytical", 0.4)
system3.add_competitor("Hybrid", "Balance creativity with analysis", 0.7)
problem3 = "Develop a marketing campaign for an eco-friendly product"
result3 = system3.run_competition(
problem3,
mode=CompetitionMode.ROUND_ROBIN,
rounds=3
)
print(f"\n{'*' * 80}")
print("TOURNAMENT WINNER:")
print(f"{'*' * 80}")
print(f"Agent: {result3.winner.agent_id}")
print(f"Average Score: {result3.winner.total_score:.2f}/10.0")
# Show score progression
print(f"\n{'-' * 80}")
print("SCORE PROGRESSION:")
print(f"{'-' * 80}")
for agent_id in ["Creative", "Analytical", "Hybrid"]:
agent_solutions = [s for s in result3.all_solutions if s.agent_id == agent_id]
scores = [s.total_score for s in agent_solutions]
print(f"{agent_id}: {' -> '.join([f'{s:.2f}' for s in scores])}")
# Summary
print("\n" + "=" * 80)
print("SUMMARY")
print("=" * 80)
print("""
The Competitive Multi-Agent pattern demonstrates several key benefits:
1. **Quality Through Competition**: Competition drives agents to produce better solutions
2. **Diverse Approaches**: Different strategies explore solution space differently
3. **Iterative Improvement**: Agents can improve based on previous best solutions
4. **Objective Selection**: Multi-criteria evaluation ensures fair judging
Competition Modes:
- **Round Robin**: All agents compete in every round (fair, comprehensive)
- **Tournament**: Bracket-style elimination (exciting, efficient)
- **Survival**: Lowest scorers eliminated each round (competitive pressure)
Evaluation Approach:
- **Multi-Criteria**: Solutions judged on multiple dimensions
- **Objective Scoring**: Numerical scores (0-10) for each criterion
- **Weighted Total**: Final score from criterion average
- **Ranking**: Clear winner determination
Use Cases:
- Creative tasks (writing, design, ideation)
- Optimization with multiple local optima
- Generating diverse solution approaches
- Quality improvement through competition
- A/B testing alternative approaches
The pattern is particularly effective when:
- Solution quality is critical
- Multiple valid approaches exist
- Diversity of perspectives adds value
- Competition motivates better outputs
- Objective evaluation is possible
""")
if __name__ == "__main__":
demonstrate_competitive_multi_agent()
Agentic Design Pattern: Competitive Multi Agent
Pattern 017: Competitive Multi Agent – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Competitive Multi Agent pattern implementation.
Pattern Description
Pattern 017: Competitive Multi-Agent
Description: The Competitive Multi-Agent pattern uses multiple agents that compete to produce the best solution. Agents work independently, their solutions are evaluated against each other, and the winning solution is selected. This competition drives quality improvement and innovation.
Components: – Competitor Agents: Multiple agents with different strategies – Evaluation System: Judges solutions across multiple criteria – Competition Framework: Manages rounds and determines winners – Selection Mechanism: Chooses best solution(s)
Use Cases: – Creative tasks (writing, design, ideation) – Optimization problems with multiple local optima – Generating diverse solution approaches – Quality improvement through competition
LangChain Implementation: Uses multiple LLM agents with different configurations competing on tasks, implements multi-criteria evaluation, tournament-style competition, and quality-driven selection mechanisms.
Key Features: – Tournament-style competitions – Multi-criteria evaluation – Diverse agent strategies – Winner selection and ranking
File Statistics
- Total Lines: 559
- Classes: 6
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 017: Competitive Multi-Agent
Description:
The Competitive Multi-Agent pattern uses multiple agents that compete to produce
the best solution. Agents work independently, their solutions are evaluated against
each other, and the winning solution is selected. This competition drives quality
improvement and innovation.
Components:
- Competitor Agents: Multiple agents with different strategies
- Evaluation System: Judges solutions across multiple criteria
- Competition Framework: Manages rounds and determines winners
- Selection Mechanism: Chooses best solution(s)
Use Cases:
- Creative tasks (writing, design, ideation)
- Optimization problems with multiple local optima
- Generating diverse solution approaches
- Quality improvement through competition
LangChain Implementation:
Uses multiple LLM agents with different configurations competing on tasks,
implements multi-criteria evaluation, tournament-style competition, and
quality-driven selection mechanisms.
Key Features:
- Tournament-style competitions
- Multi-criteria evaluation
- Diverse agent strategies
- Winner selection and ranking
"""
Purpose: This module implements the Competitive Multi Agent pattern, providing a structured approach to agent design and implementation.
Import Statements
Standard Library Imports
import dataclasses.dataclass
import enum.Enum
import langchain_core.output_parsers.StrOutputParser
import langchain_core.prompts.ChatPromptTemplate
import os
import random
import typing.Any
import typing.Dict
import typing.List
import typing.Optional
import typing.Tuple
Purpose: Core Python functionality:
– dataclasses.dataclass: 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
– random: Random number generation
– 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.Tuple: 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: CompetitionMode
Lines 48-52
Inherits from: Enum
Purpose:
Modes of competition.
Class: Solution
Lines 56-63
Purpose:
Represents a solution from a competitor.
Class: CompetitionResult
Lines 67-72
Purpose:
Results from a competition.
Class: CompetitorAgent
Lines 75-146
Purpose:
An agent that competes to produce the best solution.
Methods:
__init__(self, agent_id, strategy, model, temperature)
Lines 80-100
Purpose:
Initialize a competitor agent.
Args:
agent_id: Unique identifier
strategy: Competitive strategy description
model: LLM model to use
temperature: Creativity level
Parameters:
– agent_id: Agent id
– strategy: Strategy
– model: Language model instance
– temperature: Sampling temperature (0-1)
generate_solution(self, problem, round_number, previous_best)
Lines 102-146
Purpose:
Generate a solution for the problem.
Args:
problem: Problem to solve
round_number: Current round number
previous_best: Previous best solution (for improvement)
Returns:
Generated solution
Parameters:
– problem: Problem
– round_number: Round number
– previous_best: Previous best
Returns: Solution
Class: CompetitionJudge
Lines 149-249
Purpose:
Evaluates and ranks solutions in competitions.
Methods:
__init__(self, criteria, model)
Lines 154-167
Purpose:
Initialize the judge.
Args:
criteria: List of evaluation criteria
model: LLM model for judging
Parameters:
– criteria: Criteria
– model: Language model instance
evaluate_solution(self, problem, solution)
Lines 169-193
Purpose:
Evaluate a solution across all criteria.
Args:
problem: Original problem
solution: Solution to evaluate
Returns:
Solution with scores populated
Parameters:
– problem: Problem
– solution: Solution
Returns: Solution
_score_criterion(self, problem, solution, criterion)
Lines 195-225
Purpose:
Score a solution on a specific criterion.
Parameters:
– problem: Problem
– solution: Solution
– criterion: Criterion
Returns: float
rank_solutions(self, solutions)
Lines 227-249
Purpose:
Rank solutions by total score.
Args:
solutions: List of solutions to rank
Returns:
Ranked solutions (highest to lowest)
Parameters:
– solutions: Solutions
Returns: <ast.Subscript object at 0x000001A06D64EC90>
Class: CompetitiveSystem
Lines 252-390
Purpose:
Manages competitive multi-agent system.
Methods:
__init__(self, evaluation_criteria, model)
Lines 257-280
Purpose:
Initialize competitive system.
Args:
evaluation_criteria: Criteria for evaluating solutions
model: LLM model for agents
Parameters:
– evaluation_criteria: Evaluation criteria
– model: Language model instance
add_competitor(self, agent_id, strategy, temperature)
Lines 282-290
Purpose:
Add a competitor to the system.
Parameters:
– agent_id: Agent id
– strategy: Strategy
– temperature: Sampling temperature (0-1)
run_competition(self, problem, mode, rounds)
Lines 292-377
Purpose:
Run a competition among agents.
Args:
problem: Problem for agents to solve
mode: Competition mode
rounds: Number of competition rounds
Returns:
Competition results
Parameters:
– problem: Problem
– mode: Mode
– rounds: Rounds
Returns: CompetitionResult
get_leaderboard(self)
Lines 379-390
Purpose:
Get agent leaderboard.
Returns:
List of (agent_id, wins, avg_score) tuples
Parameters:
Returns: <ast.Subscript object at 0x000001A06D63CC50>
Functions
demonstrate_competitive_multi_agent()
Lines 393-554
Purpose:
Demonstrate the Competitive Multi-Agent pattern.
Key Concepts Summary
Pattern Overview
The Competitive 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
↓
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 016
- Pattern 018
- 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 Competitive 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: 017_competitive_multi_agent.py
Total Lines: 559
Pattern Category: AI Agent Design Pattern #017
Generated from actual implementation code
For educational and reference purposes
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