{"id":28432608,"url":"https://github.com/mjunaidca/agents-sdk-decoded","last_synced_at":"2025-08-18T13:19:27.062Z","repository":{"id":296404238,"uuid":"993109176","full_name":"mjunaidca/agents-sdk-decoded","owner":"mjunaidca","description":null,"archived":false,"fork":false,"pushed_at":"2025-06-02T16:47:37.000Z","size":755,"stargazers_count":7,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-01T08:41:42.299Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mjunaidca.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-05-30T08:25:34.000Z","updated_at":"2025-06-24T11:15:13.000Z","dependencies_parsed_at":"2025-05-30T18:57:07.572Z","dependency_job_id":"a5f3de61-055c-4d92-a551-0b2a7bf3d481","html_url":"https://github.com/mjunaidca/agents-sdk-decoded","commit_stats":null,"previous_names":["mjunaidca/agents-sdk-decoded"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mjunaidca/agents-sdk-decoded","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Fagents-sdk-decoded","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Fagents-sdk-decoded/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Fagents-sdk-decoded/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Fagents-sdk-decoded/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mjunaidca","download_url":"https://codeload.github.com/mjunaidca/agents-sdk-decoded/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mjunaidca%2Fagents-sdk-decoded/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270998265,"owners_count":24682214,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-18T02:00:08.743Z","response_time":89,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-06-05T17:10:21.267Z","updated_at":"2025-08-18T13:19:27.035Z","avatar_url":"https://github.com/mjunaidca.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# OpenAI Agents SDK - Decoded: Expert Learning Modules\n\nWelcome to the `decoded` sprint! This collection of modules is designed as an **expert-level educational resource** for mastering the **OpenAI Agents SDK**. The idea is to provide a deep, practical understanding of the SDK's capabilities, preparing you for advanced development and technical assessments.\n\n## 🎯 Purpose\n\nThe `decoded` directory breaks down the OpenAI Agents SDK into focused, digestible learning units. Each subdirectory (e.g., `01_agent`, `05_tools`, `13_models`) represents a core concept or feature of the SDK. This modular approach allows for:\n\n1.  **Deep Understanding**: Each module concentrates on a specific topic, enabling thorough exploration and mastery.\n2.  **Expert Assessment Readiness**: The structure is ideal for preparing for in-depth technical questioning, as each concept is self-contained and clearly explained with practical examples.\n3.  **Progressive Learning**: Modules are designed to build upon each other, guiding you from foundational knowledge to advanced patterns and production considerations.\n4.  **Practical Application**: Code examples are runnable and demonstrate real-world usage patterns.\n5.  **DACA Framework Alignment**: The concepts and patterns explored here are foundational for building scalable, resilient, and cost-efficient agentic systems as envisioned by the **Dapr Agentic Cloud Ascent (DACA)** design pattern.\n\n## 📚 Structure\n\nThe `decoded` directory is organized into subdirectories, each representing a specific module or topic within the OpenAI Agents SDK. For example:\n\n-   `01_agent/`: Covers the basics of Agent creation and core attributes.\n-   `02_runner/`: Explains how to execute agents and manage their lifecycle.\n-   `03_results/`: Details how to handle and interpret agent results.\n-   `04_stream/`: Focuses on streaming responses from agents.\n-   `05_tools/`: Explores the integration and usage of tools with agents.\n-   `06_hands_off/`: Covers autonomous agent execution.\n-   `07_lifecycle/`: Dives into agent lifecycle events and hooks.\n-   `08_exceptions/`: Teaches error handling and exception management.\n-   `09_guardrails/`: Discusses implementing safety and ethical considerations.\n-   `10_multiple_agents/`: Explores patterns for orchestrating multiple agents.\n-   `11_configurations_visualizations/`: (Assumed) Focuses on advanced configurations and visualizing agent behavior.\n-   `12_tracing/`: (Assumed) Covers tracing and observability for agent interactions.\n-   `13_models/`: Provides a deep dive into model selection, configuration, multi-provider integration (including LiteLLM), and cost optimization strategies. This module itself contains sub-modules for granular learning (e.g., `01_default_models.py`, `06_models_quiz.py`).\n\nEach module typically contains:\n\n-   **Python files (`.py`)**: Self-contained scripts demonstrating specific concepts. These often include print statements for educational output.\n-   **README.md**: A dedicated README explaining the module's focus, learning objectives, key concepts, and how to run the examples.\n-   **Quiz files** (in some modules like `13_models`): To test understanding and readiness.\n\n## 💡 How to Use\n\n1.  **Navigate to a module**: `cd` into a specific subdirectory (e.g., `cd 13_models`).\n2.  **Read the Module README**: Understand the learning objectives and concepts covered.\n3.  **Run the Examples**: Execute the Python files (e.g., `python 01_default_models.py`) to see the concepts in action.\n4.  **Take the Quizzes**: If available, run the quiz script to assess your understanding.\n5.  **Connect to DACA**: Reflect on how these SDK features can be leveraged within the DACA pattern for building large-scale agentic applications.\n\n## 🚀 Vision: Agentia World via DACA\n\nThe knowledge gained from these modules is crucial for realizing the core logic for creating **Software Agents for Business Operations** a global network of interconnected AI agents. \n\nWith this the DACA framework will provide the blueprint for building the infrastructure to support such a vision, and mastering the OpenAI Agents SDK through these `decoded` modules is a key step in that journey.\n\n---\n\n_This `decoded` directory is part aimed at fostering deep expertise in the OpenAI Agents SDK, preparing for complex challenges and expert-level technical assessment._\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjunaidca%2Fagents-sdk-decoded","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmjunaidca%2Fagents-sdk-decoded","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjunaidca%2Fagents-sdk-decoded/lists"}