"""
Pattern 018: Cooperative Multi-Agent
Description:
The Cooperative Multi-Agent pattern features agents working together toward shared
goals through collaboration, communication, and coordination. Agents share information,
coordinate actions, and help each other to achieve collective objectives more
effectively than they could individually.
Components:
- Cooperative Agents: Agents designed to collaborate
- Shared Goal System: Common objectives all agents work toward
- Communication Protocol: Message passing and information sharing
- Coordination Mechanism: Synchronizing agent actions
- Resource Sharing: Pooling knowledge and capabilities
Use Cases:
- Complex collaborative tasks
- Distributed problem-solving
- Team-based projects
- Multi-perspective analysis
LangChain Implementation:
Uses multiple LLM agents with shared context, implements message passing
between agents, maintains shared knowledge base, and coordinates actions
through explicit communication protocols.
Key Features:
- Shared goal alignment
- Inter-agent communication
- Knowledge sharing
- Coordinated action planning
"""
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 MessageType(Enum):
"""Types of messages agents can exchange."""
INFORMATION = "information"
REQUEST = "request"
OFFER = "offer"
COORDINATION = "coordination"
RESULT = "result"
@dataclass
class Message:
"""Message exchanged between agents."""
from_agent: str
to_agent: Optional[str] # None means broadcast to all
message_type: MessageType
content: str
context: Optional[Dict[str, Any]] = None
@dataclass
class SharedKnowledge:
"""Knowledge base shared among cooperative agents."""
facts: List[str] = field(default_factory=list)
insights: List[str] = field(default_factory=list)
decisions: List[str] = field(default_factory=list)
@dataclass
class CooperativeTask:
"""Task for cooperative agents to work on."""
task_id: str
description: str
assigned_agents: List[str]
result: Optional[str] = None
completed: bool = False
class CooperativeAgent:
"""
An agent designed for cooperation and collaboration.
"""
def __init__(
self,
agent_id: str,
role: str,
expertise: str,
model: str = "gpt-3.5-turbo"
):
"""
Initialize a cooperative agent.
Args:
agent_id: Unique identifier
role: Agent's role in the team
expertise: Agent's area of expertise
model: LLM model to use
"""
self.agent_id = agent_id
self.role = role
self.expertise = expertise
self.llm = ChatOpenAI(model=model, temperature=0.6)
# Collaboration state
self.inbox: List[Message] = []
self.outbox: List[Message] = []
self.knowledge: List[str] = []
def get_persona(self) -> str:
"""Get agent's cooperative persona."""
return f"""You are {self.agent_id}, a cooperative team member.
Role: {self.role}
Expertise: {self.expertise}
You are collaborative, supportive, and focused on team success.
You actively share information, offer help, and coordinate with teammates."""
def analyze_task(
self,
task: CooperativeTask,
shared_knowledge: SharedKnowledge
) -> Dict[str, Any]:
"""
Analyze a task from the agent's perspective.
Args:
task: Task to analyze
shared_knowledge: Shared team knowledge
Returns:
Analysis with contributions and needs
"""
knowledge_context = "\n".join([
f"- {fact}" for fact in shared_knowledge.facts[:5]
]) if shared_knowledge.facts else "No shared knowledge yet"
prompt = ChatPromptTemplate.from_messages([
("system", self.get_persona()),
("user", """Task: {task_description}
Team's shared knowledge:
{knowledge_context}
Analyze this task and provide:
1. What you can contribute (based on your expertise)
2. What help you need from teammates
3. Key considerations
Be specific and collaborative.""")
])
chain = prompt | self.llm | StrOutputParser()
analysis = chain.invoke({
"task_description": task.description,
"knowledge_context": knowledge_context
})
return {"analysis": analysis.strip()}
def contribute_to_task(
self,
task: CooperativeTask,
team_messages: List[Message],
shared_knowledge: SharedKnowledge
) -> str:
"""
Contribute to solving the task.
Args:
task: Task to contribute to
team_messages: Recent messages from team
shared_knowledge: Shared knowledge base
Returns:
Agent's contribution
"""
# Build context from team communication
recent_messages = [
f"{msg.from_agent} ({msg.message_type.value}): {msg.content}"
for msg in team_messages[-5:] # Last 5 messages
]
message_context = "\n".join(recent_messages) if recent_messages else "No recent messages"
knowledge_context = "\n".join([
f"- {item}" for item in shared_knowledge.facts[:5]
]) if shared_knowledge.facts else "None yet"
prompt = ChatPromptTemplate.from_messages([
("system", self.get_persona()),
("user", """Task: {task_description}
Recent team communication:
{message_context}
Shared knowledge:
{knowledge_context}
Provide your contribution to solving this task.
Build upon teammates' input and share your expertise.
Be specific and actionable (2-3 sentences).""")
])
chain = prompt | self.llm | StrOutputParser()
contribution = chain.invoke({
"task_description": task.description,
"message_context": message_context,
"knowledge_context": knowledge_context
})
return contribution.strip()
def send_message(
self,
to_agent: Optional[str],
message_type: MessageType,
content: str
) -> Message:
"""
Send a message to another agent or broadcast.
Args:
to_agent: Recipient agent ID (None for broadcast)
message_type: Type of message
content: Message content
Returns:
Created message
"""
message = Message(
from_agent=self.agent_id,
to_agent=to_agent,
message_type=message_type,
content=content
)
self.outbox.append(message)
return message
def receive_message(self, message: Message):
"""Receive a message from another agent."""
self.inbox.append(message)
class CooperativeTeam:
"""
Manages a team of cooperative agents.
"""
def __init__(self, model: str = "gpt-3.5-turbo"):
"""Initialize cooperative team."""
self.model = model
self.agents: Dict[str, CooperativeAgent] = {}
self.shared_knowledge = SharedKnowledge()
self.message_log: List[Message] = []
self.completed_tasks: List[CooperativeTask] = []
def add_agent(
self,
agent_id: str,
role: str,
expertise: str
):
"""Add an agent to the team."""
agent = CooperativeAgent(agent_id, role, expertise, self.model)
self.agents[agent_id] = agent
def broadcast_message(self, message: Message):
"""Broadcast a message to all agents."""
for agent in self.agents.values():
if agent.agent_id != message.from_agent:
agent.receive_message(message)
self.message_log.append(message)
def direct_message(self, message: Message):
"""Send a message to a specific agent."""
if message.to_agent and message.to_agent in self.agents:
self.agents[message.to_agent].receive_message(message)
self.message_log.append(message)
def share_knowledge(self, knowledge_item: str, category: str = "facts"):
"""Add knowledge to shared knowledge base."""
if category == "facts":
self.shared_knowledge.facts.append(knowledge_item)
elif category == "insights":
self.shared_knowledge.insights.append(knowledge_item)
elif category == "decisions":
self.shared_knowledge.decisions.append(knowledge_item)
def collaborate_on_task(
self,
task_description: str,
assigned_agents: Optional[List[str]] = None
) -> Dict[str, Any]:
"""
Agents collaborate to complete a task.
Args:
task_description: Description of the task
assigned_agents: Agents to assign (None = all agents)
Returns:
Collaboration results
"""
if assigned_agents is None:
assigned_agents = list(self.agents.keys())
task = CooperativeTask(
task_id=f"task_{len(self.completed_tasks) + 1}",
description=task_description,
assigned_agents=assigned_agents
)
print(f"\n[Team] Starting collaborative task: {task_description}")
print(f"[Team] Assigned: {', '.join(assigned_agents)}\n")
# Phase 1: Individual analysis
print("=" * 80)
print("PHASE 1: Individual Analysis")
print("=" * 80 + "\n")
analyses = {}
for agent_id in assigned_agents:
agent = self.agents[agent_id]
analysis = agent.analyze_task(task, self.shared_knowledge)
analyses[agent_id] = analysis
print(f"[{agent_id}] Analysis:")
print(f" {analysis['analysis'][:150]}...\n")
# Share analysis with team
msg = agent.send_message(
None, # Broadcast
MessageType.INFORMATION,
f"My analysis: {analysis['analysis']}"
)
self.broadcast_message(msg)
# Phase 2: Coordination and planning
print("=" * 80)
print("PHASE 2: Coordination & Planning")
print("=" * 80 + "\n")
# First agent initiates coordination
coordinator_id = assigned_agents[0]
coordinator = self.agents[coordinator_id]
coord_msg = coordinator.send_message(
None,
MessageType.COORDINATION,
f"Let's coordinate our efforts. I'll focus on {coordinator.expertise}. Who can handle other aspects?"
)
self.broadcast_message(coord_msg)
print(f"[{coordinator_id}] Coordination: {coord_msg.content}\n")
# Other agents respond with offers
for agent_id in assigned_agents[1:]:
agent = self.agents[agent_id]
offer_msg = agent.send_message(
coordinator_id,
MessageType.OFFER,
f"I can contribute my {agent.expertise} expertise"
)
self.direct_message(offer_msg)
print(f"[{agent_id}] Offer: {offer_msg.content}\n")
# Phase 3: Collaborative execution
print("=" * 80)
print("PHASE 3: Collaborative Execution")
print("=" * 80 + "\n")
contributions = {}
for agent_id in assigned_agents:
agent = self.agents[agent_id]
contribution = agent.contribute_to_task(
task,
self.message_log,
self.shared_knowledge
)
contributions[agent_id] = contribution
print(f"[{agent_id}] Contribution:")
print(f" {contribution}\n")
# Share contribution
result_msg = agent.send_message(
None,
MessageType.RESULT,
contribution
)
self.broadcast_message(result_msg)
# Add to shared knowledge
self.share_knowledge(
f"{agent_id}: {contribution}",
"insights"
)
# Phase 4: Synthesis
print("=" * 80)
print("PHASE 4: Synthesis")
print("=" * 80 + "\n")
final_result = self._synthesize_contributions(
task_description,
contributions
)
task.result = final_result
task.completed = True
self.completed_tasks.append(task)
print(f"[Team] Task completed successfully!\n")
return {
"task": task,
"individual_analyses": analyses,
"contributions": contributions,
"final_result": final_result,
"messages_exchanged": len(self.message_log),
"shared_knowledge_items": len(self.shared_knowledge.facts) + len(self.shared_knowledge.insights)
}
def _synthesize_contributions(
self,
task_description: str,
contributions: Dict[str, str]
) -> str:
"""Synthesize individual contributions into final result."""
# Use a synthesis agent
synthesizer = ChatOpenAI(model=self.model, temperature=0.5)
contributions_text = "\n\n".join([
f"{agent_id} ({self.agents[agent_id].expertise}):\n{contrib}"
for agent_id, contrib in contributions.items()
])
prompt = ChatPromptTemplate.from_messages([
("system", "You are synthesizing collaborative team outputs into a cohesive result."),
("user", """Task: {task_description}
Team Contributions:
{contributions_text}
Synthesize these contributions into a unified, coherent solution that:
1. Integrates all team members' expertise
2. Addresses the task completely
3. Highlights collaborative synergies
4. Provides actionable results""")
])
chain = prompt | synthesizer | StrOutputParser()
synthesis = chain.invoke({
"task_description": task_description,
"contributions_text": contributions_text
})
return synthesis.strip()
def get_team_summary(self) -> Dict[str, Any]:
"""Get summary of team composition and performance."""
return {
"team_size": len(self.agents),
"agents": [
{
"id": agent.agent_id,
"role": agent.role,
"expertise": agent.expertise
}
for agent in self.agents.values()
],
"tasks_completed": len(self.completed_tasks),
"messages_exchanged": len(self.message_log),
"shared_knowledge_items": len(self.shared_knowledge.facts) + len(self.shared_knowledge.insights)
}
def demonstrate_cooperative_multi_agent():
"""Demonstrate the Cooperative Multi-Agent pattern."""
print("=" * 80)
print("COOPERATIVE MULTI-AGENT PATTERN DEMONSTRATION")
print("=" * 80)
# Test 1: Product development team
print("\n" + "=" * 80)
print("TEST 1: Product Development Team")
print("=" * 80)
team1 = CooperativeTeam()
# Build diverse team
team1.add_agent("Alice", "Product Manager", "Product Strategy")
team1.add_agent("Bob", "Engineer", "Technical Implementation")
team1.add_agent("Carol", "Designer", "User Experience")
team1.add_agent("Dave", "Analyst", "Data Analysis")
print("\nTeam Composition:")
for agent_id, agent in team1.agents.items():
print(f" - {agent_id}: {agent.role} ({agent.expertise})")
task1 = "Design and plan a new feature that helps users track their productivity goals"
result1 = team1.collaborate_on_task(task1)
print("=" * 80)
print("FINAL RESULT:")
print("=" * 80)
print(result1["final_result"])
print("\n" + "-" * 80)
print("COLLABORATION METRICS:")
print("-" * 80)
print(f" Messages exchanged: {result1['messages_exchanged']}")
print(f" Knowledge items shared: {result1['shared_knowledge_items']}")
print(f" Agents participated: {len(result1['contributions'])}")
# Test 2: Research team
print("\n" + "=" * 80)
print("TEST 2: Research Team Collaboration")
print("=" * 80)
team2 = CooperativeTeam()
team2.add_agent("ResearcherA", "Data Scientist", "Machine Learning")
team2.add_agent("ResearcherB", "Domain Expert", "Healthcare")
team2.add_agent("ResearcherC", "Statistician", "Statistical Analysis")
print("\nTeam Composition:")
for agent_id, agent in team2.agents.items():
print(f" - {agent_id}: {agent.role} ({agent.expertise})")
task2 = "Develop a methodology to predict patient readmission risk using ML"
result2 = team2.collaborate_on_task(task2)
print("=" * 80)
print("FINAL RESULT:")
print("=" * 80)
print(result2["final_result"])
# Summary
print("\n" + "=" * 80)
print("SUMMARY")
print("=" * 80)
print("""
The Cooperative Multi-Agent pattern demonstrates several key benefits:
1. **Synergy**: Combined expertise exceeds individual capabilities
2. **Knowledge Sharing**: Agents build on each other's contributions
3. **Coordinated Action**: Agents work together toward shared goals
4. **Communication**: Rich information exchange improves outcomes
Collaboration Phases:
1. **Individual Analysis**: Each agent analyzes from their perspective
2. **Coordination**: Agents plan how to work together
3. **Execution**: Agents contribute their expertise
4. **Synthesis**: Contributions integrated into final result
Communication Types:
- **Information**: Sharing facts and knowledge
- **Request**: Asking for help or information
- **Offer**: Offering assistance or expertise
- **Coordination**: Planning joint action
- **Result**: Sharing completed work
Use Cases:
- Complex multi-disciplinary projects
- Team-based problem-solving
- Distributed collaborative work
- Multi-perspective analysis
- Cross-functional initiatives
The pattern is particularly effective when:
- Tasks require diverse expertise
- Collaboration adds value over individual work
- Communication and coordination are feasible
- Shared goals align team members
- Synergies between specialties exist
""")
if __name__ == "__main__":
demonstrate_cooperative_multi_agent()
Agentic Design Pattern: Cooperative Multi Agent
Pattern 018: Cooperative Multi Agent – Line-by-Line Explanation
Overview
This document provides a detailed line-by-line explanation of the Cooperative Multi Agent pattern implementation.
Pattern Description
Pattern 018: Cooperative Multi-Agent
Description: The Cooperative Multi-Agent pattern features agents working together toward shared goals through collaboration, communication, and coordination. Agents share information, coordinate actions, and help each other to achieve collective objectives more effectively than they could individually.
Components: – Cooperative Agents: Agents designed to collaborate – Shared Goal System: Common objectives all agents work toward – Communication Protocol: Message passing and information sharing – Coordination Mechanism: Synchronizing agent actions – Resource Sharing: Pooling knowledge and capabilities
Use Cases: – Complex collaborative tasks – Distributed problem-solving – Team-based projects – Multi-perspective analysis
LangChain Implementation: Uses multiple LLM agents with shared context, implements message passing between agents, maintains shared knowledge base, and coordinates actions through explicit communication protocols.
Key Features: – Shared goal alignment – Inter-agent communication – Knowledge sharing – Coordinated action planning
File Statistics
- Total Lines: 594
- Classes: 6
- Functions: 1
- Constants: 0
Module Documentation
"""
Pattern 018: Cooperative Multi-Agent
Description:
The Cooperative Multi-Agent pattern features agents working together toward shared
goals through collaboration, communication, and coordination. Agents share information,
coordinate actions, and help each other to achieve collective objectives more
effectively than they could individually.
Components:
- Cooperative Agents: Agents designed to collaborate
- Shared Goal System: Common objectives all agents work toward
- Communication Protocol: Message passing and information sharing
- Coordination Mechanism: Synchronizing agent actions
- Resource Sharing: Pooling knowledge and capabilities
Use Cases:
- Complex collaborative tasks
- Distributed problem-solving
- Team-based projects
- Multi-perspective analysis
LangChain Implementation:
Uses multiple LLM agents with shared context, implements message passing
between agents, maintains shared knowledge base, and coordinates actions
through explicit communication protocols.
Key Features:
- Shared goal alignment
- Inter-agent communication
- Knowledge sharing
- Coordinated action planning
"""
Purpose: This module implements the Cooperative 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
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: MessageType
Lines 48-54
Inherits from: Enum
Purpose:
Types of messages agents can exchange.
Class: Message
Lines 58-64
Purpose:
Message exchanged between agents.
Class: SharedKnowledge
Lines 68-72
Purpose:
Knowledge base shared among cooperative agents.
Class: CooperativeTask
Lines 76-82
Purpose:
Task for cooperative agents to work on.
Class: CooperativeAgent
Lines 85-249
Purpose:
An agent designed for cooperation and collaboration.
Methods:
__init__(self, agent_id, role, expertise, model)
Lines 90-114
Purpose:
Initialize a cooperative agent.
Args:
agent_id: Unique identifier
role: Agent's role in the team
expertise: Agent's area of expertise
model: LLM model to use
Parameters:
– agent_id: Agent id
– role: Role
– expertise: Expertise
– model: Language model instance
get_persona(self)
Lines 116-123
Purpose:
Get agent's cooperative persona.
Parameters:
Returns: str
analyze_task(self, task, shared_knowledge)
Lines 125-166
Purpose:
Analyze a task from the agent's perspective.
Args:
task: Task to analyze
shared_knowledge: Shared team knowledge
Returns:
Analysis with contributions and needs
Parameters:
– task: Task
– shared_knowledge: Shared knowledge
Returns: <ast.Subscript object at 0x000001A06D692550>
contribute_to_task(self, task, team_messages, shared_knowledge)
Lines 168-219
Purpose:
Contribute to solving the task.
Args:
task: Task to contribute to
team_messages: Recent messages from team
shared_knowledge: Shared knowledge base
Returns:
Agent's contribution
Parameters:
– task: Task
– team_messages: Team messages
– shared_knowledge: Shared knowledge
Returns: str
send_message(self, to_agent, message_type, content)
Lines 221-245
Purpose:
Send a message to another agent or broadcast.
Args:
to_agent: Recipient agent ID (None for broadcast)
message_type: Type of message
content: Message content
Returns:
Created message
Parameters:
– to_agent: To agent
– message_type: Message type
– content: Content
Returns: Message
receive_message(self, message)
Lines 247-249
Purpose:
Receive a message from another agent.
Parameters:
– message: Message
Class: CooperativeTeam
Lines 252-484
Purpose:
Manages a team of cooperative agents.
Methods:
__init__(self, model)
Lines 257-263
Purpose:
Initialize cooperative team.
Parameters:
– model: Language model instance
add_agent(self, agent_id, role, expertise)
Lines 265-273
Purpose:
Add an agent to the team.
Parameters:
– agent_id: Agent id
– role: Role
– expertise: Expertise
broadcast_message(self, message)
Lines 275-280
Purpose:
Broadcast a message to all agents.
Parameters:
– message: Message
direct_message(self, message)
Lines 282-286
Purpose:
Send a message to a specific agent.
Parameters:
– message: Message
share_knowledge(self, knowledge_item, category)
Lines 288-295
Purpose:
Add knowledge to shared knowledge base.
Parameters:
– knowledge_item: Knowledge item
– category: Category
collaborate_on_task(self, task_description, assigned_agents)
Lines 297-430
Purpose:
Agents collaborate to complete a task.
Args:
task_description: Description of the task
assigned_agents: Agents to assign (None = all agents)
Returns:
Collaboration results
Parameters:
– task_description: Task description
– assigned_agents: Assigned agents
Returns: <ast.Subscript object at 0x000001A06D658050>
_synthesize_contributions(self, task_description, contributions)
Lines 432-467
Purpose:
Synthesize individual contributions into final result.
Parameters:
– task_description: Task description
– contributions: Contributions
Returns: str
get_team_summary(self)
Lines 469-484
Purpose:
Get summary of team composition and performance.
Parameters:
Returns: <ast.Subscript object at 0x000001A06D609690>
Functions
demonstrate_cooperative_multi_agent()
Lines 487-589
Purpose:
Demonstrate the Cooperative Multi-Agent pattern.
Key Concepts Summary
Pattern Overview
The Cooperative 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 017
- Pattern 019
- 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 Cooperative 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: 018_cooperative_multi_agent.py
Total Lines: 594
Pattern Category: AI Agent Design Pattern #018
Generated from actual implementation code
For educational and reference purposes
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