Chat Completions

An OpenAI-compatible chat completions endpoint. If you’re already using the OpenAI SDK, simply change base_url and api_key for a seamless migration.

POST https://api.profy.cn/v1/chat/completions

Authentication

Two authentication methods are supported:

API Key: Authorization: Bearer sk-pro-xxxx
OAuth Bearer: Authorization: Bearer <access_token>

Request Body

model

string

required

Model identifier. Use GET /v1/models to query available models.

messages

array

required

Array of conversation messages. Each message includes role and content fields.

stream

boolean

default:"false"

Whether to use SSE streaming response.

temperature

number

Sampling temperature, range 0-2. Higher values produce more random output. Some models do not support this parameter (see model capabilities).

max_tokens

integer

Maximum number of tokens to generate.

top_p

number

Nucleus sampling parameter.

frequency_penalty

number

Frequency penalty parameter, range -2.0 to 2.0.

presence_penalty

number

Presence penalty parameter, range -2.0 to 2.0.

stop

string | string[]

Stop sequences for generation.

tools

array

Function Calling tool definition array (same format as OpenAI).

tool_choice

string | object

Tool selection strategy: "auto", "none", or a specific tool.

Non-Streaming Request Examples

from profy import Profy

async with Profy(api_key="sk-pro-your-key") as client:
    resp = await client.chat.completions.create(
        model="deepseek-v3",
        messages=[
            {"role": "system", "content": "You are a professional translation assistant"},
            {"role": "user", "content": "Translate this to English: 今天天气真好"},
        ],
        temperature=0.3,
    )
    print(resp.text)

import { Profy } from "@profy-ai/sdk";

const client = new Profy({ apiKey: "sk-pro-your-key" });

const resp = await client.chat.completions.create({
  model: "deepseek-v3",
  messages: [
    { role: "system", content: "You are a professional translation assistant" },
    { role: "user", content: "Translate this to English: 今天天气真好" },
  ],
  temperature: 0.3,
});

console.log(resp.text);

curl -X POST https://api.profy.cn/v1/chat/completions \
  -H "Authorization: Bearer sk-pro-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v3",
    "messages": [
      {"role": "system", "content": "You are a professional translation assistant"},
      {"role": "user", "content": "Translate this to English: 今天天气真好"}
    ],
    "temperature": 0.3
  }'

Non-Streaming Response

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "model": "deepseek-v3",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The weather is really nice today."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 28,
    "completion_tokens": 8,
    "total_tokens": 36
  },
  "balance_remaining": 9500
}

The response includes an additional balance_remaining field showing the remaining credit balance after the call. This is one of the key differences from OpenAI.

Streaming Request Examples

from profy import Profy

async with Profy(api_key="sk-pro-your-key") as client:
    stream = await client.chat.completions.create(
        model="deepseek-v3",
        messages=[{"role": "user", "content": "Tell me a joke"}],
        stream=True,
    )
    async for chunk in stream:
        if chunk.delta:
            print(chunk.delta, end="", flush=True)

import { Profy } from "@profy-ai/sdk";

const client = new Profy({ apiKey: "sk-pro-your-key" });

const stream = await client.chat.completions.create({
  model: "deepseek-v3",
  messages: [{ role: "user", content: "Tell me a joke" }],
  stream: true,
});

for await (const chunk of stream) {
  if (chunk.delta) {
    process.stdout.write(chunk.delta);
  }
}

curl -N -X POST https://api.profy.cn/v1/chat/completions \
  -H "Authorization: Bearer sk-pro-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v3",
    "messages": [{"role": "user", "content": "Tell me a joke"}],
    "stream": true
  }'

As an OpenAI Drop-In Replacement

If you’re using OpenAI’s official SDK, simply change base_url and api_key:

from openai import OpenAI

client = OpenAI(
    api_key="sk-pro-your-key",
    base_url="https://api.profy.cn/v1",
)

response = client.chat.completions.create(
    model="deepseek-v3",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-pro-your-key",
  baseURL: "https://api.profy.cn/v1",
});

const response = await client.chat.completions.create({
  model: "deepseek-v3",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(response.choices[0].message.content);

Call a Profy Expert (Expert-as-Model)

In addition to calling raw LLM models, you can invoke any published Profy Expert (AI Agent) using the profy-<identifier> model name format. Behind the scenes, the Expert runs a full Agent stack: persona, tool calls, memory, and skills — but you interact with it just like any other model.

Model Naming Convention

Format	Meaning	Example
`profy-<identifier>`	Invoke the specified Profy Expert	`profy-financial-advisor`
Other model names	Direct LLM call (existing behavior)	`deepseek-v3`

Use GET /v1/models to discover all available Expert models (type: "expert").

Request Examples (Non-Streaming)

Default behavior (stream omitted or false): the Agent completes all reasoning and tool calls, then returns a single JSON response.

from openai import OpenAI

client = OpenAI(
    api_key="sk-pro-your-key",
    base_url="https://api.profy.cn/v1",
)

response = client.chat.completions.create(
    model="profy-financial-advisor",
    messages=[
        {"role": "user", "content": "Analyze Tesla's latest earnings report"}
    ],
)

print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-pro-your-key",
  baseURL: "https://api.profy.cn/v1",
});

const response = await client.chat.completions.create({
  model: "profy-financial-advisor",
  messages: [
    { role: "user", content: "Analyze Tesla's latest earnings report" },
  ],
});

console.log(response.choices[0].message.content);

curl -X POST https://api.profy.cn/v1/chat/completions \
  -H "Authorization: Bearer sk-pro-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "profy-financial-advisor",
    "messages": [
      {"role": "user", "content": "Analyze Tesla'\''s latest earnings report"}
    ]
  }'

Request Examples (Streaming)

Set stream: true to receive incremental content in real time — useful for displaying text as it’s generated.

from openai import OpenAI

client = OpenAI(
    api_key="sk-pro-your-key",
    base_url="https://api.profy.cn/v1",
)

response = client.chat.completions.create(
    model="profy-financial-advisor",
    messages=[
        {"role": "user", "content": "Analyze Tesla's latest earnings report"}
    ],
    stream=True,
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-pro-your-key",
  baseURL: "https://api.profy.cn/v1",
});

const stream = await client.chat.completions.create({
  model: "profy-financial-advisor",
  messages: [
    { role: "user", content: "Analyze Tesla's latest earnings report" },
  ],
  stream: true,
});

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) process.stdout.write(content);
}

curl -N -X POST https://api.profy.cn/v1/chat/completions \
  -H "Authorization: Bearer sk-pro-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "profy-financial-advisor",
    "messages": [
      {"role": "user", "content": "Analyze Tesla'\''s latest earnings report"}
    ],
    "stream": true
  }'

Multi-Turn Conversations (Sessions)

A session is automatically created on the first call. The session ID is returned via the X-Session-Id response header. Pass the session ID in subsequent calls to continue the conversation:

Request body session_id field (recommended; use extra_body with the OpenAI SDK)
Request header X-Session-Id

from openai import OpenAI

client = OpenAI(
    api_key="sk-pro-your-key",
    base_url="https://api.profy.cn/v1",
)

# First turn
r1 = client.chat.completions.create(
    model="profy-financial-advisor",
    messages=[{"role": "user", "content": "My name is Alice. Help me create an investment plan."}],
    extra_body={}
)
session_id = r1._headers.get("x-session-id")

# Second turn — the Expert remembers your name and context
r2 = client.chat.completions.create(
    model="profy-financial-advisor",
    messages=[{"role": "user", "content": "Do you remember my name?"}],
    extra_body={"session_id": session_id}
)

Expert-as-Model vs. Agents Run

Feature	`/v1/chat/completions` + `profy-*`	`/v1/agents/run`
SDK Compatibility	Works with OpenAI SDK directly	Requires Profy SDK
Response Format	Standard OpenAI Chat Completions	Profy custom SSE events
Streaming / Non-Streaming	Both supported (`stream` defaults to `false`)	Streaming only
Tool Call Process	Silent (handled internally by Agent)	16 event types fully exposed
Best For	Drop-in replacement, LangChain, quick integration	Tool observability, custom UI

If you need to observe the Agent’s full tool call process (e.g., displaying “Searching…”, “Analyzing data…” intermediate states), use the /v1/agents/run endpoint with the Profy SDK.

Differences from OpenAI

Feature	Profy	OpenAI
Model names	Profy platform model identifiers	OpenAI model names
Additional response fields	`balance_remaining` (remaining credits)	None
Function Calling	Supported (select models)	Fully supported
Vision	Supported (select models)	Supported
Billing	Profy credits	USD

Use GET /v1/models to query each model’s capabilities to check whether the model supports temperature, tools, vision, and other features.

Error Codes

HTTP Status Code	Description
`400`	Missing `model` or `messages`, or model unavailable
`401`	Authentication failed
`502`	Upstream model provider error

Overview

Platform API

Authentication & Keys

Integrations

SDK

Cookbook

Reference

Chat Completions

Authentication

Request Body

Non-Streaming Request Examples

Non-Streaming Response

Streaming Request Examples

As an OpenAI Drop-In Replacement

Call a Profy Expert (Expert-as-Model)

Model Naming Convention

Request Examples (Non-Streaming)

Request Examples (Streaming)

Multi-Turn Conversations (Sessions)

Expert-as-Model vs. Agents Run

Differences from OpenAI

Error Codes

Next Steps

Model List

Report Billing Events

​Authentication

​Request Body

​Non-Streaming Request Examples

​Non-Streaming Response

​Streaming Request Examples

​As an OpenAI Drop-In Replacement

​Call a Profy Expert (Expert-as-Model)

​Model Naming Convention

​Request Examples (Non-Streaming)

​Request Examples (Streaming)

​Multi-Turn Conversations (Sessions)

​Expert-as-Model vs. Agents Run

​Differences from OpenAI

​Error Codes

​Next Steps

Model List

Report Billing Events

Authentication

Request Body

Non-Streaming Request Examples

Non-Streaming Response

Streaming Request Examples

As an OpenAI Drop-In Replacement

Call a Profy Expert (Expert-as-Model)

Model Naming Convention

Request Examples (Non-Streaming)

Request Examples (Streaming)

Multi-Turn Conversations (Sessions)

Expert-as-Model vs. Agents Run

Differences from OpenAI

Error Codes

Next Steps