openai/openai-cookbook
Apr 2025 – Mar 2026
3 Months active
Languages Used
JSONMarkdownPythonJupyter Notebook
Technical Skills
AI IntegrationAPI IntegrationData HandlingDebuggingDocumentationLLM Development
Tom Pakeman
PROFILE
Tom Pakeman
Contributed to the openai/openai-cookbook repository by developing advanced function calling examples with reasoning models, integrating external data sources, and enhancing developer onboarding through refined documentation and hands-on Jupyter notebooks. Built a real-time evaluation framework for OpenAI API workflows, featuring robust error handling, persistent result storage, and a Streamlit-based results viewer for interactive metric comparison and per-run inspection. Leveraged Python, data visualization, and API integration to streamline prototyping, validation, and data-driven tuning of reasoning-powered applications. The work emphasized clarity, maintainability, and collaborative tooling, reducing feedback cycles and improving the reliability of both demonstration notebooks and evaluation pipelines.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
3
Lines of code
13,058
Activity Months3
Your Network
481 peopleSame Organization
@openai.com359
Shared Repositories
122
Work History
March 2026
2 Commits • 1 Features
Mar 1, 2026March 2026: Delivered a real-time evaluation framework for OpenAI API evaluations, including a Streamlit-based results viewer to compare metrics and inspect individual runs in real time. Implemented robust error handling, persistent result storage, and plotting capabilities for evaluation metrics. This work, supported by realtime evals types tooling and a dedicated results viewer (both co-authored), reduces feedback cycles, improves reliability, and enables data-driven API tuning. Key technologies include Python, Streamlit, data visualization, and collaborative tooling.
2 Commits • 1 Features
Mar 1, 2026March 2026: Delivered a real-time evaluation framework for OpenAI API evaluations, including a Streamlit-based results viewer to compare metrics and inspect individual runs in real time. Implemented robust error handling, persistent result storage, and plotting capabilities for evaluation metrics. This work, supported by realtime evals types tooling and a dedicated results viewer (both co-authored), reduces feedback cycles, improves reliability, and enables data-driven API tuning. Key technologies include Python, Streamlit, data visualization, and collaborative tooling.
March 2026
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary: Delivered a focused feature enhancement in the openai-cookbook that strengthens advanced function calling with reasoning models and external data sources. The work consolidates updates to the Reasoning Function Call cookbook and its example notebook, introduces a new web search example, and provides guidance on sequential and parallel tool calls, along with improvements to output correctness and demonstration notebook clarity. These deliverables enhance developer workflows, improve the accuracy of data-driven outputs, and reduce time-to-value when integrating external data sources.
May 2025
2 Commits • 1 Features
May 1, 2025May 2025 monthly summary: Delivered a focused feature enhancement in the openai-cookbook that strengthens advanced function calling with reasoning models and external data sources. The work consolidates updates to the Reasoning Function Call cookbook and its example notebook, introduces a new web search example, and provides guidance on sequential and parallel tool calls, along with improvements to output correctness and demonstration notebook clarity. These deliverables enhance developer workflows, improve the accuracy of data-driven outputs, and reduce time-to-value when integrating external data sources.
April 2025
4 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for openai/openai-cookbook: Delivered hands-on capability for function calls with reasoning models through new example notebooks, improved documentation for reasoning models and function calling, and fixed a bug in the reasoning-function-call notebook related to error handling and logging. The work provides a clear, production-leaning reference for prototyping and validating reasoning-powered function calls, accelerating onboarding and adoption.
4 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for openai/openai-cookbook: Delivered hands-on capability for function calls with reasoning models through new example notebooks, improved documentation for reasoning models and function calling, and fixed a bug in the reasoning-function-call notebook related to error handling and logging. The work provides a clear, production-leaning reference for prototyping and validating reasoning-powered function calls, accelerating onboarding and adoption.
April 2025
Activity
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Quality Metrics
Correctness93.8%
Maintainability90.0%
Architecture90.0%
Performance82.6%
AI Usage62.6%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPython
Technical Skills
AI Function CallingAI IntegrationAPI IntegrationAPI integrationData HandlingData RefinementData analysisDebuggingDocumentationFunction CallingJupyter NotebooksLLM DevelopmentLLM UsageMachine learningNatural Language Processing
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline