Chat Completions

import os

from openai import OpenAI

client = OpenAI(
    api_key=os.environ["LITHOSAI_API_KEY"],
    base_url="https://api.lithosai.cloud/v1",
)

response = client.chat.completions.create(
    model="moonshotai/Kimi-K3",
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LITHOSAI_API_KEY,
  baseURL: "https://api.lithosai.cloud/v1",
});

const response = await client.chat.completions.create({
  model: "moonshotai/Kimi-K3",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(response.choices[0].message.content);

curl https://api.lithosai.cloud/v1/chat/completions \
  -H "Authorization: Bearer $LITHOSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"moonshotai/Kimi-K3","messages":[{"role":"user","content":"Hello"}]}'

Streaming

Set stream: true to receive tokens as they are generated.

import os

from openai import OpenAI

client = OpenAI(
    api_key=os.environ["LITHOSAI_API_KEY"],
    base_url="https://api.lithosai.cloud/v1",
)

stream = client.chat.completions.create(
    model="moonshotai/Kimi-K3",
    messages=[{"role": "user", "content": "Hi"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LITHOSAI_API_KEY,
  baseURL: "https://api.lithosai.cloud/v1",
});

const stream = await client.chat.completions.create({
  model: "moonshotai/Kimi-K3",
  messages: [{ role: "user", content: "Hi" }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

curl https://api.lithosai.cloud/v1/chat/completions \
  -H "Authorization: Bearer $LITHOSAI_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/Kimi-K3",
    "stream": true,
    "messages": [{ "role": "user", "content": "Hi" }]
  }'

LithosAI includes a usage object in every chunk.

Errors

Error response bodies specify the issue. code disambiguates the error, and for rate limits, type specifies the triggered limit.

{
  "error": {
    "message": "rate limit exceeded",
    "type": "input_tokens",
    "param": null,
    "code": "rate_limit_exceeded"
  }
}

Status	Code	Meaning	Retry
`400`	`invalid_json`	The request body is not valid JSON.	No
`400`	`model_required`	No model was named: the field is absent, empty, not a string, or there is no body.	No
`400`	`invalid_stream_options`	`stream_options` is present but is not an object or null.	No
`400`	`request_too_large`	The prompt is larger than this model’s input budget can ever hold.	No
`401`	—	The key is missing, malformed, unknown or revoked.	No
`402`	`insufficient_quota`	Your organization is out of credit.	No
`404`	`model_not_found`	The catalog holds no such id. The message quotes it back.	No
`429`	`rate_limit_exceeded`	One of your three per-minute budgets is empty. `error.type` names which.	Yes, after `retry-after`
`429`	`provider_overloaded`	The model is at capacity.	Yes, after `retry-after`

Retry 429 and 5xx with exponential backoff and jitter. Prefer the delay we advise, retry-after-ms first and then retry-after, over an interval of your own. Do not retry 400, 401, 402 or 404, and do not retry anything carrying x-should-retry: false: the answer will not change until you do something about it. A 429 means one of your three per-minute budgets is empty — see Rate limits for what they are and how to read the headers that report them.

Getting Started

API Reference

Account

Streaming

Errors

​Streaming

​Errors

Streaming

Errors