Chat

The same published agent that answers the phone also answers over text. There are two ways to drive a chat from your backend: the session API (durable conversations you create, message, and end) and an OpenAI-compatible completions endpoint (a drop-in for any client that already speaks the OpenAI chat shape).

Sessions

A session is a server-side conversation tied to one agent. Create it, post messages, and end it when done. History is reconstructed for you, so you can fetch the full transcript any time. Posting a message with stream: true returns Server-Sent Events (SSE) — token deltas, tool calls, and knowledge retrievals arrive as they happen, terminating with an event: done (or event: error) frame. With stream: false you get the assembled messages in one JSON response.

# Create a session, then send a streaming message
curl -X POST https://builder.flowyte.com/api/v1/chat/sessions \
  -H "Authorization: Bearer flowyte_sk_…" \
  -H "Content-Type: application/json" \
  -d '{"agentId": "AGENT_ID"}'

curl -N -X POST https://builder.flowyte.com/api/v1/chat/sessions/SESSION_ID/messages \
  -H "Authorization: Bearer flowyte_sk_…" \
  -H "Content-Type: application/json" \
  -d '{"content": "Do you deliver to 80202?", "stream": true}'

OpenAI-compatible completions

POST /chat/completions accepts the OpenAI request shape — set model to the agent’s id. Point an existing OpenAI SDK at the base URL https://builder.flowyte.com/api/v1 and it works with only a base-URL swap.

curl -X POST https://builder.flowyte.com/api/v1/chat/completions \
  -H "Authorization: Bearer flowyte_sk_…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "AGENT_ID",
    "messages": [{"role": "user", "content": "What are your hours?"}],
    "stream": true
  }'

from openai import OpenAI

client = OpenAI(
    base_url="https://builder.flowyte.com/api/v1",
    api_key="flowyte_sk_…",
)

stream = client.chat.completions.create(
    model="AGENT_ID",          # the agent id is the model
    messages=[{"role": "user", "content": "What are your hours?"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://builder.flowyte.com/api/v1",
  apiKey: "flowyte_sk_…",
});

const stream = await client.chat.completions.create({
  model: "AGENT_ID",           // the agent id is the model
  messages: [{ role: "user", content: "What are your hours?" }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

When stream: true, this endpoint emits OpenAI-style SSE chunks (data: {choices:[{delta:…}]}) ending with data: [DONE].

In the API

Action	Endpoint	Scope
Create a session	`POST /chat/sessions`	`chat:write`
Get a session	`GET /chat/sessions/{id}`	`chat:read`
List messages	`GET /chat/sessions/{id}/messages`	`chat:read`
Post a message (SSE when `stream`)	`POST /chat/sessions/{id}/messages`	`chat:write`
End a session	`POST /chat/sessions/{id}/end`	`chat:write`
OpenAI-compatible completion	`POST /chat/completions`	`chat:write`

These endpoints use your secret key. To put chat in a browser without exposing a secret, use a publishable key and the Embeddable Widget.

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Sessions

OpenAI-compatible completions

In the API

​Sessions

​OpenAI-compatible completions

​In the API

Sessions

OpenAI-compatible completions

In the API