Providers

Configuring models and API providers.

Built-in providers

Provider	Name	Env var
OpenAI	openai	OPENAI_API_KEY
Anthropic	anthropic	ANTHROPIC_API_KEY
Google Gemini	google	GOOGLE_API_KEY
Mistral	mistral	MISTRAL_API_KEY
Cohere	cohere	COHERE_API_KEY
OpenRouter	openrouter	OPENROUTER_API_KEY

Check which keys are configured:

aevyra-verdict providers

Inline flags

Pass --model (or -m) once per model in provider/model format:

aevyra-verdict run data.jsonl \
  -m openai/gpt-5.4-nano \
  -m qwen/qwen3.5-9b \
  -m google/gemini-2.0-flash

Config file

For more than a couple of models, use a config file. Supports .yaml, .json, and .toml.

aevyra-verdict run data.jsonl --config models.yaml

# models.yaml
models:
  - provider: openai
    model: gpt-5.4-nano
    label: gpt-5.4-nano

  - provider: openrouter
    model: qwen/qwen3.5-9b
    label: qwen3.5-9b

  - provider: openrouter
    model: meta-llama/llama-3.1-8b-instruct
    label: llama-openrouter

The label field sets the display name in results. It’s optional — defaults to provider/model.

OpenRouter

OpenRouter gives you access to 200+ models across every major provider with a single API key. Model names follow the provider/model format listed on openrouter.ai/models.

aevyra-verdict run data.jsonl -m openrouter/mistralai/mistral-large

from aevyra_verdict.providers import get_provider

provider = get_provider(
    "openrouter",
    "meta-llama/llama-3.1-8b-instruct",
    site_url="https://yoursite.com",  # optional, for OpenRouter analytics
    app_name="aevyra-verdict",
)

Local models (vLLM / Ollama)

Any OpenAI-compatible local server works via the openai provider with a custom base_url.

vLLM

Start the server:

vllm serve meta-llama/Llama-3.1-8B-Instruct

Config entry:

- provider: openai
  model: meta-llama/Llama-3.1-8B-Instruct
  base_url: http://localhost:8000/v1
  api_key: "none"
  label: llama-local

Ollama

Start the server:

ollama serve

Config entry:

- provider: openai
  model: llama3.1
  base_url: http://localhost:11434/v1
  api_key: "ollama"
  label: llama-ollama

Custom providers

Subclass Provider and register it:

from aevyra_verdict.providers import Provider, register_provider, CompletionResult

class MyProvider(Provider):
    name = "my_provider"

    def complete(self, messages, temperature=0.0, max_tokens=1024, **kwargs):
        # call your API here
        return CompletionResult(
            text="response text",
            model=self.model,
            provider=self.name,
            latency_ms=100.0,
        )

register_provider("my_provider", MyProvider)

Python API

from aevyra_verdict.providers import get_provider

provider = get_provider("openai", "gpt-5.4-nano")
result = provider.complete([{"role": "user", "content": "Hello"}])

print(result.text)
print(result.latency_ms)
print(result.usage)  # {"prompt_tokens": 10, "completion_tokens": 5}