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版本：0.5（最新）

Capability Descriptors

Capability descriptors added in cubepi 0.5.

This page is about reaching any model — the big SaaS APIs, a regional endpoint, a coding-plan tier, or your own vLLM box — without writing per-vendor glue. The progression is deliberate:

  1. The default is zero config. For Anthropic and OpenAI you write a provider and a model. Nothing on this page is required.
  2. For an off-default endpoint, describe the quirks as data. A CapabilityDescriptor captures the differences declaratively — no subclassing, no forking.
Preset catalogs live in the host application

cubepi ships the mechanism (the CapabilityDescriptor and the wire runtime that applies it), not a catalog of vendors. A ready-made list of providers — base URLs, auth, regional/coding-plan endpoints, model lists — is product data and belongs to the application embedding cubepi (for example, cubebox maintains its own provider catalog). To reach a specific vendor, build the provider with the right base_url + CapabilityDescriptor as shown below.

1. The simple case — no config at all​

Most users never touch capabilities. The built-in providers ship with sensible defaults:

import cubepi
from cubepi import Agent, Model
from cubepi.providers.anthropic import AnthropicProvider

agent = Agent(
    provider=AnthropicProvider(),                 # reads ANTHROPIC_API_KEY
    model=Model(id="claude-sonnet-4-6", provider="anthropic"),
)
await agent.prompt("Hello!")

That's the whole setup. A provider built without capability= produces byte-identical output to cubepi 0.4 — the machinery below only kicks in when you ask for it.

2. Off-default endpoints — the CapabilityDescriptor​

When you want a model that isn't OpenAI or Anthropic — DeepSeek, Qwen, Doubao, an OpenRouter route, a local server — the awkward part is each one's wire dialect (does it want max_tokens or max_completion_tokens? how is reasoning toggled?). You don't subclass a provider; you describe the quirks as a CapabilityDescriptor and pass it in, along with the right base_url and provider class for the endpoint's wire shape:

import os
from cubepi import CapabilityDescriptor
from cubepi.providers.openai import OpenAIProvider

provider = OpenAIProvider(
    api_key=os.environ["DEEPSEEK_API_KEY"],
    base_url="https://api.deepseek.com",
    capability=CapabilityDescriptor(
        reasoning_off_payload={"extra_body": {"reasoning": {"exclude": True}}},
        reasoning_on_payload={"extra_body": {"reasoning": {"exclude": False}}},
    ),
)

Pick the provider class by the endpoint's wire dialect:

Wire dialectProvider class
anthropic-messagesAnthropicProvider
openai-completionsOpenAIProvider
openai-responsesOpenAIResponsesProvider

Each field maps to one wire behavior, and an unset field does nothing — so you only declare what's actually different.

max_tokens_field​

"max_tokens" (default) or "max_completion_tokens". Some OpenAI-compatible servers accept only one spelling; this renames the key on the way out. Effect: wrong choice → the server ignores your output cap or 400s.

temperature​

A TemperatureSpec controlling how the caller's temperature is treated:

from cubepi import TemperatureSpec

TemperatureSpec(mode="free", min=0.0, max=2.0, default=1.0)  # clamp into [min, max]
TemperatureSpec(mode="fixed", fixed_value=1.0)               # always overwrite
TemperatureSpec(mode="ignored")                              # drop the key
  • free — the caller's value is clamped into [min, max]; if none was sent, nothing is written. Effect: protects against an out-of-range value the backend would reject.
  • fixed — fixed_value always wins. Effect: use for models that permit only one temperature (e.g. some o-series reasoning models).
  • ignored — the key is stripped entirely. Effect: for backends that 400 on any temperature while reasoning.

Reasoning toggle: reasoning_off_payload / reasoning_on_payload​

When thinking is off, reasoning_off_payload is deep-merged into the request; when it's on, reasoning_on_payload is. Effect: this is how "turn reasoning on/off" becomes whatever field the vendor expects:

CapabilityDescriptor(
    reasoning_off_payload={"extra_body": {"enable_thinking": False}},
    reasoning_on_payload={"extra_body": {"enable_thinking": True}},
)

The merge recurses into nested dicts; arrays are atomic; on a collision the capability value wins.

Reasoning level: reasoning_level (three shapes)​

Beyond on/off, cubepi maps a ThinkingLevel (off/minimal/low/medium/high/xhigh) onto a concrete wire value written at a dotted path. kind picks the shape:

from cubepi import ReasoningLevelSpec

# int_budget — a token budget (Anthropic).
ReasoningLevelSpec(
    path="thinking.budget_tokens", kind="int_budget",
    level_budgets={"off": 0, "minimal": 1024, "low": 2048,
                   "medium": 8192, "high": 16384, "xhigh": 16384},
)

# effort — an effort string (OpenAI Responses).
ReasoningLevelSpec(
    path="reasoning.effort", kind="effort",
    level_to_effort={"minimal": "minimal", "low": "low",
                     "medium": "medium", "high": "high", "xhigh": "high"},
)

# enum — a vendor-specific state (Doubao's 3-state thinking).
ReasoningLevelSpec(
    path="thinking.type", kind="enum",
    level_to_enum={"off": "disabled", "low": "enabled", "high": "enabled"},
)

Effect: a level missing from the map is simply not written, so the endpoint keeps its own default for that level.

supports_tools / supports_images / supports_streaming​

Declarative flags read by host applications and frontends (for example, to grey out image upload). The providers themselves don't gate on them.

Per-model overrides on a shared endpoint​

One gateway (OpenRouter, LiteLLM, an internal proxy) often serves both reasoning and non-reasoning models. model_capability_overrides maps a model_id to a descriptor that replaces the base one for that model:

provider = OpenAIProvider(
    api_key="…",
    base_url="https://openrouter.ai/api/v1",
    capability=base_cap,                        # default for unlisted models
    model_capability_overrides={
        "deepseek/deepseek-r1": reasoning_cap,  # this model only
    },
)

Resolution is exact-match on model_id; anything not listed falls back to capability.

See also​