response_format with a JSON Schema and the model returns strictly structured JSON your code can parse directly.
curl https://tokendog.io/v1/chat/completions \
-H "Authorization: Bearer $TOKENDOG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model":"gpt-5",
"messages":[{"role":"user","content":"Extract: John Doe, age 28"}],
"response_format":{"type":"json_schema","json_schema":{
"name":"person","schema":{"type":"object",
"properties":{"name":{"type":"string"},"age":{"type":"integer"}},
"required":["name","age"],"additionalProperties":false}}}
}'
from openai import OpenAI
client = OpenAI(base_url="https://tokendog.io/v1", api_key="YOUR_TOKENDOG_API_KEY")
resp = client.chat.completions.create(
model="gpt-5",
messages=[{"role":"user","content":"Extract: John Doe, age 28"}],
response_format={"type":"json_schema","json_schema":{
"name":"person","schema":{"type":"object",
"properties":{"name":{"type":"string"},"age":{"type":"integer"}},
"required":["name","age"],"additionalProperties":False}}},
)
print(resp.choices[0].message.content)
import OpenAI from "openai";
const client = new OpenAI({ baseURL:"https://tokendog.io/v1", apiKey:"YOUR_TOKENDOG_API_KEY" });
const resp = await client.chat.completions.create({
model:"gpt-5",
messages:[{role:"user",content:"Extract: John Doe, age 28"}],
response_format:{type:"json_schema",json_schema:{
name:"person",schema:{type:"object",
properties:{name:{type:"string"},age:{type:"integer"}},
required:["name","age"],additionalProperties:false}}},
});
console.log(resp.choices[0].message.content);
The content is a schema-conforming JSON string — parse it directly with
JSON.parse / json.loads.