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

string

required

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

array

required

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

boolean

default:"false"

Whether to use SSE streaming response.

number

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

integer

Maximum number of tokens to generate.

number

Nucleus sampling parameter.

number

Frequency penalty parameter, range -2.0 to 2.0.

number

Presence penalty parameter, range -2.0 to 2.0.

string | string[]

Stop sequences for generation.

array

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

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

Get Started

Authentication & Keys

API Guides

SDK

Integrations

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