A genuinely useful starter pack for AI agents

If I were onboarding a new team to agents, I would hand them one runtime, one protocol doc, one graph-based orchestrator, and a short list of repos they can actually read over a weekend. The point is not to collect frameworks; it is to compare how each tool makes state, tools, and failure visible.

The kinds of materials worth saving in this space:
- framework docs that explain how orchestration actually works
- eval sets that resemble your real support or operations queue
- team writeups that include constraints, not just launch screenshots

Read:
- OpenAI Agents SDK for JavaScript: openai.github.io/openai-agents-js/
A clean look at agents, handoffs, guardrails, and tracing in one place.
- OpenAI Agents SDK for Python: openai.github.io/openai-agents-python/
Useful when your team wants the same concepts with more backend-heavy examples.
- Model Context Protocol introduction: modelcontextprotocol.io/introduction
Worth reading so tool access and context plumbing stop feeling hand-wavy.

Documents and downloadable guides:
- OpenAI agent guide: platform.openai.com/docs/guides/agents
A practical guide to agents, tools, handoffs, and traces from the product side.
- Model Context Protocol specification: modelcontextprotocol.io/specification/2025-06-18
Useful when readers need the actual protocol details instead of summaries.

Watch:
- OpenAI video archive: youtube.com/@OpenAI/videos
Talks and demos are a fast way to compare patterns before you commit to one runtime.

Build or inspect:
- OpenAI Agents JS source: github.com/openai/openai-agents-js
Readable source for tool calling, handoffs, tracing, and guardrails.
- LangGraph source: github.com/langchain-ai/langgraph
Helpful when you want explicit graph state, checkpoints, and resumable flows.

Image references:
- Model Context Protocol examples: modelcontextprotocol.io/examples
Reference implementations and diagrams that make the tool boundary more concrete.