Using model profiles and compatibility policies

ProviderProfile is deprecated (1.0.0rc2) and removed in 1.0.0

Everything a profile said has one home now. Migrate like this:

You had	Write instead
ProviderProfile.inference(provider=..., base_url=URL, compat=OpenAIResponsesCompat.preset("ollama")) + OpenAILM.from_profile(api_key=K, profile=p)	OpenAILM(api_key=K, compat="ollama") — a preset name supplies its own address; add base_url=URL only if yours differs
a per-model ModelInfo.compat on the Responses dialect	Config(extensions={"openai_responses_compat": {...}}) on that model's requests (the chat dialect has OpenAIChatCompat.model_overrides)
a compat guessed from base_url (openrouter.ai, api.meta.ai)	compat="openrouter" / compat="meta" — the guess now warns and will be removed

ModelInfo, ModelRegistry and the lm15.compat policies are not deprecated. Why: a profile was a second configuration-resolution system beside the adapter's and the router's; two systems is how a user ends up not knowing which one picked the URL. Decision record: lm15-contract/changes/2026-09-11-job-handles-live-turns-profiles.md § 3.

lm15 keeps inference requests simple:

Request(model="gpt-4.1-mini", messages=(...))

Model profiles and compatibility policies are optional helpers for applications that need model metadata, local OpenAI-compatible servers, routing, or exact provider dialect control.

They answer different questions:

Request              What the caller wants the model to do.
ModelInfo            What a model can do and what it costs.
ProviderProfile      Where/how to call provider endpoints.
Compat policy        How to serialize for a provider API dialect.
ModelRegistry        How to index and resolve model metadata.

Why profiles are separate from Request

Provider compatibility is not a semantic user request. For example, whether an OpenAI-compatible server expects max_tokens or max_output_tokens is a wire serialization detail, not a different task for the model.

For that reason, these settings live outside lm15.types.Request:

• lm15.compat — typed compatibility policy objects.
• lm15.models — model metadata, pricing, and ModelRegistry.
• lm15.profiles — provider endpoint profiles and compatibility resolution.

Minimal use: no profile

Existing code does not change.

import os

from lm15.providers import OpenAILM
from lm15.types import Message, Request

lm = OpenAILM(api_key=os.environ["OPENAI_API_KEY"])
response = lm.complete(
    Request(
        model="gpt-4.1-mini",
        messages=(Message.user("Say hello."),),
    )
)

Local OpenAI-compatible server

For Chat Completions (/chat/completions), start with Connect an unlisted OpenAI-compatible server. It shows OpenAIChatLM with no preset, an unknown-name error, and a custom OpenAIChatCompat object. You do not need a profile or a registry entry.

The example below is for Responses (/responses), a different API. Use it only if your server implements that endpoint. Use ProviderProfile.inference() with an OpenAIResponsesCompat preset.

from lm15.compat import OpenAIResponsesCompat
from lm15.profiles import ProviderProfile
from lm15.providers import OpenAILM

profile = ProviderProfile.inference(
    provider="ollama",
    api_family="openai_responses",
    base_url="http://localhost:11434/v1",
    compat=OpenAIResponsesCompat.preset("ollama"),
)

lm = OpenAILM.from_profile(api_key="ollama", profile=profile)

The preset tells the OpenAI Responses serializer to use local-server-friendly choices such as max_tokens, system for developer instructions, no strict schema field, and no prompt-cache fields.

Compatibility policy fields

OpenAIResponsesCompat is a partial policy. Fields default to None, which means "inherit from the parent profile". The string value "auto" is different: it explicitly asks the adapter to use its automatic default for that field.

from lm15.compat import OpenAIResponsesCompat

compat = OpenAIResponsesCompat(
    developer_role="system",
    max_output_tokens_field="max_tokens",
    reasoning_format="qwen",
    strict_tools="omit",
    cache_control="none",
)

Important fields:

Field	Meaning
developer_role	Serialize Message.developer(...) as developer or system.
max_output_tokens_field	Emit max_output_tokens, max_completion_tokens, or max_tokens.
reasoning_format	Map Config.reasoning to Responses reasoning, reasoning_effort, Qwen, DeepSeek, etc.
tool_result_name	Include or omit name on function call outputs.
strict_tools	Include or omit the OpenAI strict field on function tools.
cache_control	Emit OpenAI prompt-cache fields, no cache fields, or future dialects.
routing	Provider routing object, for example OpenRouter provider selection.

Available presets include:

OpenAIResponsesCompat.preset("openai")
OpenAIResponsesCompat.preset("openrouter")
OpenAIResponsesCompat.preset("ollama")
OpenAIResponsesCompat.preset("vllm")
OpenAIResponsesCompat.preset("qwen")
OpenAIResponsesCompat.preset("deepseek")
OpenAIResponsesCompat.preset("zai")

Provider-level plus model-level overrides

Provider profiles can carry default compatibility, and individual models can override it.

from lm15.compat import OpenAIResponsesCompat
from lm15.models import InferenceModelInfo, ModelInfo
from lm15.profiles import ProviderProfile

profile = ProviderProfile.inference(
    provider="local",
    api_family="openai_responses",
    base_url="http://localhost:8000/v1",
    compat=OpenAIResponsesCompat.preset("vllm"),
    models=(
        ModelInfo(
            id="qwen3-coder",
            provider="local",
            api_family="openai_responses",
            aliases=("qwen",),
            inference=InferenceModelInfo(
                input_modalities=("text",),
                output_modalities=("text",),
                context_window=128000,
                max_output_tokens=8192,
                supports_reasoning=True,
                reasoning_efforts=("off", "low", "medium", "high"),
            ),
            compat=OpenAIResponsesCompat(reasoning_format="qwen"),
        ),
    ),
)

Resolution order for OpenAI Responses requests is:

base URL default
  < ProviderProfile endpoint compat
  < ModelInfo compat for Request.model
  < Config.extensions request-level override

None fields inherit. Non-None fields override, including "auto".

Request-level escape hatch

Request-level compatibility is supported for experiments and temporary provider features, but it should not be the normal configuration path.

from lm15.types import Config, Message, Request

request = Request(
    model="weird-local-model",
    messages=(Message.user("Hi"),),
    config=Config(
        max_tokens=200,
        extensions={
            "openai_responses_compat": {
                "max_output_tokens_field": "max_tokens",
                "reasoning_format": "qwen",
            },
            "metadata": {"user_id": "u1"},
        },
    ),
)

Compatibility keys are consumed by the adapter and are not passed through to the provider payload. Other extension keys, such as metadata, still pass through.

Supported request-level shapes:

{"openai_responses_compat": {...}}
{"openai_compat": {...}}
{"compat": {"openai_responses": {...}}}
{"compat": {"openai": {...}}}

Model metadata

ModelInfo describes a model without changing the request shape.

from lm15.models import InferenceModelInfo, InferencePricing, ModelInfo

model = ModelInfo(
    id="gpt-4.1-mini",
    provider="openai",
    api_family="openai_responses",
    aliases=("mini",),
    inference=InferenceModelInfo(
        input_modalities=("text", "image"),
        output_modalities=("text",),
        context_window=1_000_000,
        max_output_tokens=32768,
        supports_reasoning=False,
        pricing=InferencePricing(
            input_per_million=0.40,
            output_per_million=1.60,
            cache_read_per_million=0.10,
        ),
    ),
)

Capabilities are endpoint-specific. ModelInfo.inference describes inference capabilities; new endpoint families can be described later without touching it. Fine-tune provenance lives on ModelOrigin (type, base_model) — lm15 does inference with tuned models, not training.

Model registry

ModelRegistry is optional. Use it when your application needs discovery, validation, aliases, or cost estimation.

from lm15.models import ModelRegistry

registry = ModelRegistry.from_profiles([profile])

info = registry.resolve("qwen", provider="local")
assert info is not None
assert info.inference is not None
print(info.inference.context_window)

You can also add generated or fine-tuned models at runtime:

registry.add(
    ModelInfo(
        id="ft:gpt-4.1-mini:org:project:abc123",
        provider="openai",
        api_family="openai_responses",
        origin=ModelOrigin(
            type="fine_tune",
            id="ftjob-abc123",
            base_model="gpt-4.1-mini",
        ),
    )
)

Cost estimation

Pricing is deliberately separate from Usage, because price tables change and some providers price non-token dimensions differently.

pricing = info.inference.pricing
if pricing is not None:
    cost = pricing.estimate(
        input_tokens=response.usage.input_tokens,
        output_tokens=response.usage.output_tokens,
        cache_read_tokens=response.usage.cache_read_tokens,
        cache_write_tokens=response.usage.cache_write_tokens,
    )
    print(cost, pricing.currency)

Design rules

• Keep Request semantic and provider-neutral.
• Put provider wire quirks in compat policies.
• Put endpoint URLs and defaults in ProviderProfile.
• Put model capabilities and prices in ModelInfo.
• Use ModelRegistry only when you need discovery or validation.
• Prefer provider/model profiles over request-level compat overrides.