From wallet to first call in two minutes.

from openai import OpenAI
client = OpenAI(base_url="https://ai.dagentx.com/v1", api_key=os.environ["WRAITH_NEXUS_KEY"])

stream = client.chat.completions.create(
    model="near/anthropic/claude-sonnet-5",
    messages=[{"role": "user", "content": "Write a haiku about Monad"}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

import anthropic
client = anthropic.Anthropic(base_url="https://ai.dagentx.com", api_key=os.environ["WRAITH_NEXUS_KEY"])

msg = client.messages.create(
    model="near/anthropic/claude-sonnet-5",
    max_tokens=512,
    messages=[{"role": "user", "content": "Hello"}],
)
print(msg.content[0].text)

import { createOpenAICompatible } from "@ai-sdk/openai-compatible";
import { generateText } from "ai";

const monad = createOpenAICompatible({
  name: "wraith-nexus",
  baseURL: "https://ai.dagentx.com/v1",
  apiKey: process.env.WRAITH_NEXUS_KEY,
});
const { text } = await generateText({ model: monad("venice/zai-org-glm-5"), prompt: "Hello" });

from langchain_openai import ChatOpenAI, OpenAIEmbeddings

llm = ChatOpenAI(base_url="https://ai.dagentx.com/v1", api_key=KEY, model="venice/zai-org-glm-5")
emb = OpenAIEmbeddings(base_url="https://ai.dagentx.com/v1", api_key=KEY,
                       model="venice/text-embedding-bge-m3", check_embedding_ctx_length=False)

import litellm
r = litellm.completion(
    model="openai/venice/zai-org-glm-5",          # "openai/" = OpenAI-compatible route
    api_base="https://ai.dagentx.com/v1",
    api_key=KEY,
    messages=[{"role": "user", "content": "Hello"}],
)

# Most OpenAI-compatible tools read these:
export OPENAI_BASE_URL=https://ai.dagentx.com/v1
export OPENAI_API_KEY=crk_…

# Cursor: Settings → Models → OpenAI API Key = crk_…,
#   "Override OpenAI Base URL" = https://ai.dagentx.com/v1,
#   then add model names exactly as listed on /models.

# Image
img = client.images.generate(model="venice/flux-2-pro", prompt="a purple ghost over a city", size="1024x1024")

# Text to speech
audio = client.audio.speech.create(model="venice/tts-kokoro", voice="af_sky", input="Built on Monad.")
audio.write_to_file("out.mp3")

# Speech to text
with open("clip.mp3", "rb") as f:
    t = client.audio.transcriptions.create(model="venice/openai/whisper-large-v3", file=f)

# Embeddings
e = client.embeddings.create(model="venice/text-embedding-bge-m3", input=["hello", "world"])