What was changed
This PR adds comprehensive documentation for the OpenAI Agents SDK integration with Temporal, providing developers with detailed implementation guides, architectural patterns, and best practices for building AI agent workflows.
New Documentation Structure
docs/openai_agents/README.md - High-level overview and architecture guidedocs/openai_agents/BASIC.md - Fundamental agent patterns and tool integrationdocs/openai_agents/AGENT_PATTERNS.md - Advanced multi-agent architectures and coordinationdocs/openai_agents/TOOLS.md - Comprehensive tool usage (code interpreter, file search, image generation)docs/openai_agents/HANDOFFS.md - Agent collaboration and message filtering patternsdocs/openai_agents/HOSTED_MCP.md - Model Context Protocol integrationdocs/openai_agents/MODEL_PROVIDERS.md - Custom LLM provider integration (LiteLLM, Ollama, GPT-OSS)docs/openai_agents/REASONING_CONTENT.md - Accessing model reasoning and thought processesdocs/openai_agents/CUSTOMER_SERVICE.md - Conversational workflows with escalation capabilitiesdocs/openai_agents/FINANCIAL_RESEARCH_AGENT.md - Complex multi-agent financial analysis systemdocs/openai_agents/RESEARCH_BOT.md - Multi-agent research system with planning, search, and synthesis
Documentation Features
- Consistent Structure: Each service follows the same documentation template for easy navigation
- Code Examples: Real implementation code with file paths and key benefits
- Architecture Diagrams: Mermaid diagrams showing system flows and component relationships
- Developer Onboarding: Progressive disclosure from high-level concepts to implementation details
- Best Practices: Common patterns, error handling, and development guidelines
- File Organization: Clear directory structures and file purposes
Why?
The OpenAI Agents SDK integration with Temporal represents a powerful combination of AI capabilities and durable execution, but developers need comprehensive guidance to leverage this integration effectively. This documentation addresses several critical needs:
Developer Experience
- Reduced Learning Curve: New developers can quickly understand integration patterns and best practices
- Implementation Guidance: Real code examples show how to structure workflows, activities, and agents
- Architecture Understanding: Clear explanations of how Temporal and OpenAI Agents work together
Production Readiness
- Best Practices: Established patterns for error handling, state management, and scalability
- Common Pitfalls: Identified and documented issues like task queue mismatches and timeout configurations
- Performance Optimization: Guidelines for parallel execution, agent coordination, and resource management
Ecosystem Growth
- Standardization: Consistent documentation structure across all services
- Knowledge Sharing: Comprehensive coverage of advanced patterns like multi-agent systems and tool integration
- Community Enablement: Developers can build upon these examples to create their own AI agent workflows
Integration Value
- Durability: Shows how Temporal ensures AI workflows survive interruptions and failures
- Observability: Demonstrates tracing, monitoring, and debugging capabilities
- Scalability: Examples of handling complex multi-agent interactions and long-running conversations
Checklist
Closes
- Note: This PR addresses the need for comprehensive OpenAI Agents integration documentation
How was this tested:
- Code Verification: Each documentation file was systematically verified against its corresponding source code
- File-by-File Review: All 10 service subfolders were analyzed with their associated Python files
- Content Validation: Ensured no duplication between overview and detailed service documentation
- Link Verification: All internal documentation links were tested and verified
- Structure Consistency: Confirmed all documentation follows the established template format
- Code Accuracy: Verified all code examples match actual implementation files
Any docs updates needed?
- README Updates: The main
openai_agents/README.md has been updated to serve as a high-level overview - Documentation Structure: New comprehensive documentation suite in
docs/openai_agents/ - External Documentation: Consider updating
docs.temporal.io to reference this new documentation suite - Repository Navigation: The new documentation provides clear entry points for developers exploring the OpenAI Agents integration
Documentation Impact
This PR significantly enhances the Temporal repository's documentation by providing:
- 10 comprehensive service guides covering all major integration patterns
- Consistent documentation structure that can serve as a template for other integrations
- Developer-friendly onboarding that reduces the barrier to entry for AI agent workflows
- Production-ready examples that demonstrate real-world usage patterns
What was changed
This PR adds comprehensive documentation for the OpenAI Agents SDK integration with Temporal, providing developers with detailed implementation guides, architectural patterns, and best practices for building AI agent workflows.
New Documentation Structure
docs/openai_agents/README.md- High-level overview and architecture guidedocs/openai_agents/BASIC.md- Fundamental agent patterns and tool integrationdocs/openai_agents/AGENT_PATTERNS.md- Advanced multi-agent architectures and coordinationdocs/openai_agents/TOOLS.md- Comprehensive tool usage (code interpreter, file search, image generation)docs/openai_agents/HANDOFFS.md- Agent collaboration and message filtering patternsdocs/openai_agents/HOSTED_MCP.md- Model Context Protocol integrationdocs/openai_agents/MODEL_PROVIDERS.md- Custom LLM provider integration (LiteLLM, Ollama, GPT-OSS)docs/openai_agents/REASONING_CONTENT.md- Accessing model reasoning and thought processesdocs/openai_agents/CUSTOMER_SERVICE.md- Conversational workflows with escalation capabilitiesdocs/openai_agents/FINANCIAL_RESEARCH_AGENT.md- Complex multi-agent financial analysis systemdocs/openai_agents/RESEARCH_BOT.md- Multi-agent research system with planning, search, and synthesisDocumentation Features
Why?
The OpenAI Agents SDK integration with Temporal represents a powerful combination of AI capabilities and durable execution, but developers need comprehensive guidance to leverage this integration effectively. This documentation addresses several critical needs:
Developer Experience
Production Readiness
Ecosystem Growth
Integration Value
Checklist
Closes
How was this tested:
Any docs updates needed?
openai_agents/README.mdhas been updated to serve as a high-level overviewdocs/openai_agents/docs.temporal.ioto reference this new documentation suiteDocumentation Impact
This PR significantly enhances the Temporal repository's documentation by providing: