Framework Examples

If your agent runs inside a popular framework, the examples below show the smallest amount of code needed to wire PostGrad in as a knowledge source. All three approaches call the same REST API under the hood. Pick the one that matches your stack.

• LangChain — Wrap PostGrad as a BaseTool your LangChain agent can call alongside other tools. Works with AgentExecutor, LangGraph, and the LCEL composition syntax.
• CrewAI — Expose PostGrad as a CrewAI tool that any agent in your crew can use. Pairs naturally with role-based crews that need domain expertise.
• n8n — Configure an HTTP Request node to query PostGrad inside n8n workflows. Useful for low-code automations and AI Agent nodes that want a knowledge fallback before answering.

Not using one of these frameworks?

You have two simpler paths:

• MCP — If your agent runs in Claude Desktop, Cursor, Windsurf, ChatGPT, or any MCP-compatible client, the PostGrad MCP server gives you tools, resources, and prompts with no code. Two-minute setup.
• SDKs — Official TypeScript and Python clients that wrap the REST API with typed interfaces, automatic retries, and a default-feed option.

What every integration needs

Regardless of framework, you'll need:

1. An API key — Create one at your dashboard. Keys are tier-scoped (Starter / Pro / Scale) and bound to your account's monthly quota.
2. A feed to query — Either pass X-PostGrad-Feed: <slug-or-uuid> to target one feed, or X-PostGrad-Feed: all to fan out across every feed you're subscribed to. Browse available feeds at the marketplace.
3. A search mode — keyword (default, ts_rank), semantic (vector similarity), or hybrid (RRF fusion). All three modes are available on every tier. For natural-language agent queries, semantic is usually the right default. See Which mode should I pick? for the longer answer.

That's it. The framework pages above show the exact wire-up for each.