openai

Official command-line client for the OpenAI API, with documented installation, environment variables, resource-based commands, file passing, and admin API support.

A vocabulary-driven graph memory library for AI agents, with storage backends, indexing pipelines, and MCP servers for building persistent structured memory into agent workflows.

Intencion is a product analytics and observability platform for AI agents. It tracks user intent, tool calls, run outcomes, and resolution trends, then turns failures or unmet requests into Linear or GitHub issues.

OpenAI’s SDK docs for programmatically controlling local Codex agents, with coverage of setup, core concepts, tools, orchestration, evaluation, and deployment paths.

An open-source CLI for testing LLM prompts across consistency, latency, cost, and behavior, with HTML reports and support for OpenAI and Anthropic.

Python package for recording real LLM API responses and replaying them in tests so LLM-driven code can run deterministically without live API calls.

A macOS companion overlay for Claude Code that shows an animated pet/sprite based on Claude Code activity. It includes a CLI, hook integration, a daemon, and a web-based atlas validator/renderer for Codex-pet-compatible pets.

TypeScript middleware that trims or compresses LLM chat history to fit a token budget, with support for OpenAI, Anthropic, and AI SDK integrations.

Self-hosted AI observability for tracking LLM requests, costs, latency, prompt traces, reasoning tokens, and PII masking across many model providers.

A tutorial and live demo showing how to build an AI SQL analyst agent that inspects schema, writes queries, runs them, and explains results using Node.js, OpenAI, Vercel AI SDK, and SQLite.

Python library for managing LLM conversation history with cache-friendly message ordering, dynamic context, and compaction.

A curated learning roadmap for building production AI agents, covering OpenAI, Anthropic, MCP, harnesses, evals, memory, and safety. The README organizes the material into phases with exercises and a prioritized reading table.

A GitHub research project that measures how gpt-4.1 responds when asked to pick a random number between 1 and 100, using 10,000 API calls and comparing the results to a uniform baseline.