$ show openai/openai-cookbook
The OpenAI Cookbook is a sprawling collection of example code, notebooks, and guides for accomplishing tasks with the OpenAI API. It is not a single application but a repository of patterns. Its dominant character is data research and example code: it contains hundreds of Jupyter notebooks and Python scripts demonstrating embeddings, fine-tuning, RAG, agents, evals, MCP integrations, and vector database usage. The repo also includes full-stack demo applications (React/Next.js/Flask) for real-time translation, deployment management, and supply-chain copilots, making it a hybrid. There is no unified data lineage; instead, each example subsection defines its own data flow from API calls to local CSV/JSONL inputs. The quality is uneven: some eval harnesses and agent demos are well-structured, while many notebooks are scratchy and rely on inaccessible private data. The repository is maintained (last commit June 2026, CI workflows active) but shows medium technical drift, with some examples pinned to older library versions and many notebooks requiring manual environment setup.
The LLM identified 5 files worth highlighting for reuse:
A Streamlit application for browsing and comparing realtime evaluation runs from crawl, walk, and run harnesses. It implements a comparison view with dynamic chart selection, a run viewer with drill-down into audio artifacts and event logs, and auto-discovery of scaffolded eval folders. The code is well-structured with clear separation of concerns and could be extracted as a generic eval results viewer with minimal modification.
A Flask-based local control plane for deploying and observing Agents SDK demo projects. It handles project import, Docker container lifecycle, trace ingestion, and serves a React frontend. The blueprint is modular and could be reused as a lightweight deployment manager for Python-based agent projects with minimal adaptation.
A self-contained vision eval harness with typed data classes, LLM-as-judge graders, and a grid sweep utility. It evaluates image generation and editing outputs against rubrics. The modular design (types, graders, runners, sweeps, storage) makes it reusable for custom image model evaluations beyond the specific cookbook examples.
A FastMCP server wrapping yfinance to expose stock prices, financial statements, holder info, and recommendations as MCP tools. It includes async timeouts, structured output with file paths and previews, and logging. This pattern is reusable as a template for building domain-specific MCP servers that need to serve tabular financial data to agents.
A FastAPI server that wraps an OpenAI Agents SDK agent connected to Databricks MCP vector search and Unity Catalog functions. It streams agent output, handles guardrail tripwires, and manages MCP server lifecycle. The architecture is a clean pattern for exposing agent-based copilots over HTTP with streaming and could be adapted to other MCP-backed agent systems.