litellm-verathos

LiteLLM custom provider for Verathos -- verified LLM inference on the Bittensor network.

Every response from Verathos is backed by cryptographic proofs (ZK sumcheck + Merkle commitments) that guarantee the output was produced by the declared model. No output substitution is possible.

Installation

pip install litellm-verathos

Quick Start

import litellm
from litellm_verathos import VerathosProvider

# Register the provider (once, at startup)
VerathosProvider.register()

# Use "verathos/auto" for automatic best-model selection
response = litellm.completion(
    model="verathos/auto",
    messages=[{"role": "user", "content": "Explain zero-knowledge proofs"}],
    api_key="vrt_sk_...",  # your Verathos API key
)
print(response.choices[0].message.content)

Model Names

Use the verathos/ prefix followed by any model identifier:

Model string	What happens
verathos/auto	Verathos picks the best available model for you
verathos/Qwen/Qwen3-30B-A3B	Routes to a specific model
verathos/meta-llama/Llama-3.3-70B-Instruct	Routes to a specific model

To discover available models, query the Verathos API directly:

curl https://api.verathos.ai/v1/models -H "Authorization: Bearer $VERATHOS_API_KEY"

Authentication

Pass the API key in any of these ways (checked in order):

1. api_key= parameter on each call
2. VERATHOS_API_KEY environment variable

export VERATHOS_API_KEY="vrt_sk_..."

# Then no api_key= needed
response = litellm.completion(
    model="verathos/auto",
    messages=[{"role": "user", "content": "Hello!"}],
)

Getting an API Key

1. Visit verathos.ai and sign up
2. Fund your account with TAO or USDC deposits
3. Generate an API key from the dashboard

Streaming

Streaming works out of the box:

response = litellm.completion(
    model="verathos/auto",
    messages=[{"role": "user", "content": "Write a haiku about cryptography"}],
    api_key="vrt_sk_...",
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Async

import asyncio
import litellm
from litellm_verathos import VerathosProvider

VerathosProvider.register()

async def main():
    response = await litellm.acompletion(
        model="verathos/auto",
        messages=[{"role": "user", "content": "Hello!"}],
        api_key="vrt_sk_...",
    )
    print(response.choices[0].message.content)

asyncio.run(main())

All Standard Parameters

Verathos is OpenAI-compatible, so all standard chat completion parameters work:

response = litellm.completion(
    model="verathos/auto",
    messages=[{"role": "user", "content": "Solve x^2 - 4 = 0"}],
    api_key="vrt_sk_...",
    temperature=0.0,
    max_tokens=512,
    top_p=0.95,
    stop=["\n\n"],
    seed=42,
)

Custom API Base

For self-hosted Verathos validators or local development:

response = litellm.completion(
    model="verathos/auto",
    messages=[{"role": "user", "content": "Hello!"}],
    api_key="vrt_sk_...",
    api_base="http://localhost:8080/v1",
)

Or via environment variable:

export VERATHOS_API_BASE="http://localhost:8080/v1"

LiteLLM Proxy (config.yaml)

You can also use the Verathos provider with the LiteLLM proxy server. Add to your config.yaml:

model_list:
  - model_name: verathos-auto
    litellm_params:
      model: openai/auto
      api_base: https://api.verathos.ai/v1
      api_key: os.environ/VERATHOS_API_KEY

  - model_name: verathos-qwen
    litellm_params:
      model: openai/Qwen/Qwen3-30B-A3B
      api_base: https://api.verathos.ai/v1
      api_key: os.environ/VERATHOS_API_KEY

Note: The LiteLLM proxy's config.yaml uses the openai/ prefix since it handles OpenAI-compatible endpoints natively. The litellm-verathos Python package is for programmatic usage where you want the cleaner verathos/ prefix.

x402: Pay-Per-Request with USDC (No API Key Needed)

Verathos supports x402 -- a protocol for HTTP-native micropayments. With x402, you pay per request using USDC on Base L2, with no account or API key required.

x402 works at the HTTP level (the server returns HTTP 402 with payment instructions, you sign a USDC payment and resend). Since this operates below the LiteLLM abstraction layer, use the x402 client SDK directly for pay-per-request:

# x402 example (direct, not through LiteLLM)
from openai import OpenAI
from x402.client import create_x402_client
import httpx

x402_client = create_x402_client(
    httpx.Client(),
    wallet,  # your Base wallet with USDC
)
client = OpenAI(
    base_url="https://api.verathos.ai/v1",
    api_key="x402",  # any placeholder
    http_client=x402_client,
)
response = client.chat.completions.create(
    model="auto",
    messages=[{"role": "user", "content": "Hello!"}],
)

Why Verathos?

Traditional LLM APIs are a black box -- you have no way to verify that the provider actually ran your prompt through the model they claim. Verathos changes this:

• Verified inference: Every response includes cryptographic proofs that the output was computed by the declared model
• No output substitution: SHA256 output commitments + Fiat-Shamir binding prevent response tampering
• Decentralized: Runs on Bittensor's incentive network -- miners compete to serve models, validators verify proofs
• OpenAI-compatible: Drop-in replacement for any OpenAI-compatible client

License

MIT