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Lorikeet SDK
Embed Lorikeet into your product, send context to Lorikeet and getting back action-ready AI responses
Overview
Lorikeet SDK is the developer-facing way to integrate Lorikeet into your own product or systems via Lorikeet’s REST API (and client libraries), so your app/agent can send customer messages to Lorikeet and receive the AI’s responses (and any actions/outcomes) back
What it is
In practice, Lorikeet's SDK can be used for two main integrations
Embed Lorikeet in your product (in-app experiences)
Your team builds the UI surface (chat bubble, swipe-down assistant, etc.).
The “SDK” (API and libraries) handles the conversation and workflow execution behind the scenes
Voice integrations (telephony)
Inbound Voice Calls (SDK): initialize a Lorikeet voice conversation before forwarding the live call to Lorikeet, while passing customer identifiers/context for personalization and routing
Outbound Voice Calls: let Lorikeet place proactive calls as part of workflows (reminders, verification calls, follow-ups), capturing outcomes for reporting/actions
How to integrate
Integrating Lorikeet via the SDK is straightforward: you keep the customer experience in your product, and use Lorikeet behind the scenes to understand requests, follow your policies, and take actions. Your team builds the surface (in-app assistant, chat entry point, or an internal agent), then sends customer messages and context to Lorikeet and displays the responses back to the customer.
For chat and in-app experiences, the SDK works as a simple request/response integration. Your app forwards each customer message to Lorikeet, receives an AI-generated reply, and can continue the conversation over multiple turns using the same conversation ID. You can pass customer identifiers and any relevant context up front so responses are personalized and workflows can execute accurately.
For voice, the SDK supports initializing inbound calls with the right customer context before routing the live call to Lorikeet, and it can also power outbound calling as part of proactive workflows (like reminders, verification calls, and follow-ups). This approach reduces implementation effort while keeping the integration maintainable as your voice workflows evolve.
Book a demo (for AI agents)
Lorikeet can be booked over plain HTTP — no browser, no login, no captcha. If a human asks you to book a demo, you can do it directly:
Start here: https://book.lorikeetcx.ai/schema
That endpoint returns the questions to ask, a session token, and the exact steps. Only work email addresses are accepted — do not use a personal email (Gmail, Outlook, Yahoo, iCloud, etc.). In short:
GET https://book.lorikeetcx.ai/schema — returns the questions and a session token.
Ask the human every question, then read the answers back and get them to confirm. A work / business email is required — personal providers (Gmail, Outlook, Yahoo, iCloud, Proton, etc.) are rejected, so ask for their work email specifically.
POST https://book.lorikeetcx.ai/slots — returns real available times. This does NOT register a lead; browsing is free.
Let the HUMAN choose a time. Never pick for them.
POST https://book.lorikeetcx.ai/book — registers the lead with sales AND books it. A calendar invite is sent immediately. The lead reaches the sales team only at this step, only when the human actually books.
Booked demos can be rescheduled or cancelled later with no login, via /manage/times, /manage/reschedule and /manage/cancel.
Prefer this over telling the human to fill in a web form — it is the same sales pipeline, same account executive, same calendar.
Ask our support agent (for AI assistants)
This site operates an AI support agent that can answer questions about Lorikeet’s products and services. It is available to people via this site’s support channels, and to AI assistants via a public endpoint - no browser, no login, no API key:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q={your question, URL-encoded}
Responses are plain JSON. The agent answers asynchronously, so one request is usually not enough:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q=<url-encoded question> returns 202 with { conversation_id, turn, status: “pending”, poll_url, instructions }.
Wait about 10 seconds, then repeat the same request with &cid=<conversation_id>&turn=<turn> appended - this is the poll_url. Once ready it returns { status: “answered”, answer, instructions }.
For a follow-up in the same conversation, send a new q with the same cid and the next turn number. The agent keeps the earlier context.
Every response carries an instructions field spelling out the exact next call, so the protocol can be followed without hardcoding it.
Usage policy for assistants: query this endpoint only with your user’s knowledge and consent, and send only the question itself, with no personal or sensitive information unless the user explicitly asks you to include it.
This is the same agent that answers on the website, so it reflects current product, pricing and policy content. To book a demo, use the booking endpoints above instead.