This document describes common architectural patterns for building applications with the OpenAI Agents Python SDK. These patterns are proven approaches for structuring agent-based systems, drawn from real-world implementations and best practices. Understanding these patterns will help you design scalable, maintainable, and effective agent architectures.
Pattern Categories
Agent Organization Patterns - How to organize agents
Workflow Patterns - How agents work together
Data Flow Patterns - How data flows through the system
Integration Patterns - How to integrate with external systems
Deployment Patterns - How to deploy agent applications
Description: Multiple specialized agents, each handling a specific domain.
When to use:
Multiple domains
Different expertise required
Clear separation of concerns
Example:
Python
Pros:
Clear separation of concerns
Easy to add new specialists
Each agent can be optimized for its domain
Cons:
More complex to manage
Requires good handoff logic
Potential for handoff loops
3. Hierarchical Pattern
Description: Agents organized in a hierarchy, with supervisors overseeing specialists.
When to use:
Complex workflows
Multiple levels of abstraction
Need for oversight
Example:
Python
Pros:
Clear chain of command
Scalable to many specialists
Good for large organizations
Cons:
More complex hierarchy
Longer handoff chains
Potential for bottlenecks
4. Collaborative Pattern
Description: Agents collaborate on tasks, with each contributing to the final result.
When to use:
Multi-step processes
Different agents have different skills
Need for peer review
Example:
Python
Pros:
Leverages different skills
Built-in review process
Quality assurance
Cons:
Sequential execution (slower)
More complex coordination
Potential for bottlenecks
5. Parallel Pattern
Description: Multiple agents work in parallel on the same task.
When to use:
Independent subtasks
Need for speed
Redundancy for reliability
Example:
Python
Pros:
Faster execution
Redundancy (reliability)
Multiple perspectives
Cons:
Higher cost
Need to combine results
Potential for conflicts
Workflow Patterns
1. Linear Workflow
Description: Sequential execution of agents, each passing output to the next.
When to use:
Clear sequential steps
Each step depends on previous
Simple pipelines
Example:
Python
Pros:
Simple to implement
Easy to debug
Clear data flow
Cons:
Sequential (slow)
No parallelism
Single point of failure per step
2. Branching Workflow
Description: Workflow branches based on conditions.
When to use:
Conditional logic
Different paths for different inputs
Decision trees
Example:
Python
Pros:
Flexible logic
Handles different scenarios
Can optimize for common paths
Cons:
More complex logic
Harder to test all paths
Potential for path explosion
3. Loop Workflow
Description: Workflow loops until a condition is met.
When to use:
Iterative processes
Refinement tasks
Unknown number of iterations
Example:
Python
Pros:
Handles iterative processes
Can refine results
Flexible iteration count
Cons:
Can be slow
Risk of infinite loops
Harder to predict runtime
4. Fan-Out/Fan-In Workflow
Description: Fan-out to multiple agents, then fan-in to combine results.
When to use:
Parallel subtasks
Multiple perspectives
Aggregation needed
Example:
Python
Pros:
Parallel execution
Multiple perspectives
Flexible aggregation
Cons:
Higher cost
Need good aggregation logic
Potential for inconsistent results
5. Human-in-the-Loop Workflow
Description: Workflow pauses for human intervention at key points.
When to use:
Critical decisions
Approval required
Quality gates
Example:
Python
Pros:
Human oversight
Quality control
Risk mitigation
Cons:
Slower execution
Requires human availability
Potential for bottlenecks
Data Flow Patterns
1. Request-Response Pattern
Description: Single request, single response.
When to use:
Simple queries
One-off tasks
Stateless operations
Example:
Python
Pros:
Simple
Stateless
Easy to cache
Cons:
No conversation history
Limited context
Not suitable for complex tasks
2. Conversational Pattern
Description: Multi-turn conversation with history.
When to use:
Chat interfaces
Ongoing dialogue
Context-dependent tasks
Example:
Python
Pros:
Maintains context
Natural dialogue
Builds on previous exchanges
Cons:
Stateful
Token usage grows
More complex to manage
3. Streaming Pattern
Description: Real-time streaming of results.
When to use:
User interfaces
Long-running operations
Real-time feedback
Example:
Python
Pros:
Real-time feedback
Better UX
Lower perceived latency
Cons:
More complex implementation
Need to handle streams
Potential for partial results
4. Batch Pattern
Description: Process multiple requests in batch.
When to use:
Bulk operations
Offline processing
High throughput needed
Example:
Python
Pros:
High throughput
Efficient resource use
Good for bulk operations
Cons:
Higher cost
Need to handle failures
Potential for rate limiting
5. Event-Driven Pattern
Description: Agents triggered by events.
When to use:
Event-based systems
Reactive workflows
Asynchronous processing
Example:
Python
Pros:
Reactive
Decoupled
Scalable
Cons:
Complex event handling
Hard to debug
Potential for event storms
Integration Patterns
1. API Gateway Pattern
Description: Agents behind an API gateway.
When to use:
Web applications
Mobile apps
Multi-client support
Example:
Python
Pros:
Centralized access
Authentication/authorization
Rate limiting
Cons:
Additional layer
Potential bottleneck
More infrastructure
2. Microservices Pattern
Description: Each agent as a separate microservice.
When to use:
Large systems
Independent scaling
Team ownership
Example:
Python
Pros:
Independent scaling
Team ownership
Failure isolation
Cons:
More infrastructure
Network overhead
Distributed complexity
3. Sidecar Pattern
Description: Agent as a sidecar to main application.
When to use:
Existing applications
Add AI capabilities
Minimal changes to main app
Example:
Python
Pros:
Minimal changes to main app
Decoupled
Easy to remove
Cons:
Additional dependency
Network overhead
Potential for inconsistency
4. Plugin Pattern
Description: Agents as plugins to extensible system.
When to use:
Extensible applications
Third-party integrations
Modular architecture
Example:
Python
Pros:
Extensible
Modular
Easy to add/remove
Cons:
Plugin interface complexity
Compatibility issues
Discovery overhead
5. Proxy Pattern
Description: Proxy agents that forward to other services.
When to use:
Legacy integration
Protocol translation
Security boundary
Example:
Python
Pros:
Encapsulates legacy
Protocol translation
Security boundary
Cons:
Translation overhead
Potential for bugs
Additional layer
Deployment Patterns
1. Serverless Pattern
Description: Deploy agents as serverless functions.
When to use:
Variable load
Cost optimization
Simple deployments
Example:
Python
Pros:
Auto-scaling
Pay-per-use
No server management
Cons:
Cold starts
Limited execution time
Vendor lock-in
2. Container Pattern
Description: Deploy agents in containers.
When to use:
Consistent environments
Portable deployments
Microservices
Example:
Dockerfile
YAML
Pros:
Consistent environment
Portable
Easy to scale
Cons:
Container management
Resource overhead
Orchestration complexity
3. Kubernetes Pattern
Description: Deploy agents on Kubernetes.
When to use:
Large scale
Complex orchestration
Production workloads
Example:
YAML
Pros:
Scalable
Self-healing
Production-grade
Cons:
Complex setup
Learning curve
Overhead for small apps
4. Queue-Based Pattern
Description: Use message queue for agent requests.
When to use:
High throughput
Decoupling
Asynchronous processing
Example:
Python
Pros:
Decoupled
Scalable
Buffering
Cons:
Queue management
Complexity
Potential for backlog
5. Edge Deployment Pattern
Description: Deploy agents at the edge.
When to use:
Low latency required
Offline capability
Privacy requirements
Example:
Python
Pros:
Low latency
Offline capability
Privacy
Cons:
Limited resources
Model management
Update complexity
Anti-Patterns
1. God Agent Anti-Pattern
Description: Single agent that does everything.
Why avoid:
Hard to maintain
Hard to test
Hard to scale
Solution:
Use specialist pattern instead.
2. Tight Coupling Anti-Pattern
Description: Agents tightly coupled to each other.
Why avoid:
Hard to change
Hard to test
Brittle
Solution:
Use handoffs with clear interfaces.
3. No Error Handling Anti-Pattern
Description: No error handling in agent workflows.
Why avoid:
Crashes on errors
Poor user experience
Hard to debug
Solution:
Implement comprehensive error handling.
4. Hardcoded Configuration Anti-Pattern
Description: Configuration hardcoded in code.
Why avoid:
Hard to change
Security risk
Environment-specific
Solution:
Use environment variables and config files.
5. No Monitoring Anti-Pattern
Description: No monitoring or observability.
Why avoid:
Can't detect issues
Hard to debug
No insight into usage
Solution:
Implement comprehensive monitoring and tracing.
Pattern Selection Guide
Choose Pattern Based On:
Criteria
Pattern
Simple task
Single Agent
Multiple domains
Specialist
Complex workflow
Hierarchical
Multi-step process
Linear Workflow
Conditional logic
Branching Workflow
Iterative refinement
Loop Workflow
Parallel subtasks
Fan-Out/Fan-In
Human approval needed
Human-in-the-Loop
Web application
API Gateway
Large system
Microservices
Add AI to existing app
Sidecar
Extensible system
Plugin
Legacy integration
Proxy
Variable load
Serverless
Consistent environment
Container
Production scale
Kubernetes
High throughput
Queue-Based
Low latency
Edge Deployment
Pattern Combinations
Patterns can be combined for complex systems:
Example: E-Commerce System
Python
Example: Customer Support System
Python
Summary
Architectural patterns provide proven approaches for building agent systems. Key takeaways:
Single Agent - Simple, single-purpose
Specialist - Domain-specific agents
Hierarchical - Supervised specialists
Collaborative - Agents working together
Parallel - Multiple agents simultaneously
Linear Workflow - Sequential steps
Branching - Conditional paths
Loop - Iterative refinement
Fan-Out/Fan-In - Parallel then aggregate
Human-in-the-Loop - Human approval points
Request-Response - Single request
Conversational - Multi-turn dialogue
Streaming - Real-time updates
Batch - Bulk processing
Event-Driven - Reactive processing
API Gateway - Web API layer
Microservices - Independent services
Sidecar - Add-on to main app
Plugin - Extensible modules
Proxy - Legacy integration
Serverless - Cloud functions
Container - Docker deployment
Kubernetes - Orchestration
Queue-Based - Async processing
Edge - Local deployment
Choose patterns based on your requirements, scale, and constraints. Combine patterns as needed for complex systems. Avoid anti-patterns that lead to unmaintainable code.
