AI Agents

The OpenAI Agents SDK and LangGraph are valuable for different reasons: one is great for getting to a clean runtime with guardrails and tracing, and the other is excellent when the team needs graph-shaped control over state. I would choose the tool that makes debugging clearer, not the one with the loudest launch thread.

The stack categories worth comparing here:
- planner and router layers
- retrieval and memory systems
- evaluation and observability tooling

Open materials worth opening side by side:
- 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.
- OpenAI Agents SDK for JavaScript: openai.github.io/openai-agents-js/
A clean look at agents, handoffs, guardrails, and tracing in one place.

Working documents and 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.

Minimal handoff contract:

type Action = "lookup_account" | "draft_reply" | "escalate_to_human"

type Guardrail = {
  action: Action
  requiresApproval: boolean
  owner: "support_ops" | "engineering" | "human_reviewer"
}

const guardrails: Guardrail[] = [
  { action: "lookup_account", requiresApproval: false, owner: "support_ops" },
  { action: "draft_reply", requiresApproval: false, owner: "support_ops" },
  { action: "escalate_to_human", requiresApproval: true, owner: "human_reviewer" },
]