Providers

Praval provider adapters translate provider wire formats into praval.models.ModelRequest, ModelResponse, and ModelEvent contracts. Adapters should not own policy. Runtime policy belongs in ModelRuntime.

Provider Names

Use explicit provider and model names:

from praval import Agent

agent = Agent("assistant", provider="anthropic", model="claude-sonnet-5")

Compact model strings remain supported:

agent = Agent("assistant", model="openai:gpt-5.4-mini")

Praval’s registry includes release-time profiles for OpenAI gpt-5.4, gpt-5.4-mini, gpt-5.4-nano, and gpt-5.5; Anthropic claude-sonnet-5, claude-fable-5, claude-opus-4-8, and claude-haiku-4-5; Cohere command-a-03-2025; and Gemini gemini-3.5-flash, gemini-3.1-flash-lite, and gemini-3.1-pro-preview. The names were checked against the official model catalogs for the 0.8 release line. They are package metadata, not a live catalog. Use each provider’s model-list API when availability must be checked at runtime.

Capability Matrix

Legend:

Mark	Meaning
Native	Implemented directly by the provider endpoint.
Emulated	Praval can provide a fallback or wrapper.
Unsupported	Runtime rejects the request by default.
Depends	Server or model dependent. Enable with explicit profiles.

Provider	Text	Streaming	Tools	Structured Output	Image	File	Audio/Video Input	Transcription	Speech	Reasoning	Local
OpenAI	Native	Native	Native	Native	Native	Unsupported by default	Unsupported by default	Native	Native	Native	No
Anthropic	Native	Native	Native	Native	Native	Unsupported by default	Unsupported	Unsupported	Unsupported	Native	No
Cohere	Native	Unsupported	Native	Unsupported	Unsupported	Unsupported	Unsupported	Unsupported	Unsupported	Unsupported	No
Gemini	Native	Native	Native	Native	Native	Native	Native	Unsupported	Unsupported	Native	No
Ollama	Native	Native	Unsupported by default	Depends	Depends	Unsupported by default	Depends	Unsupported	Unsupported	Depends	Yes
vLLM	Native	Native	Unsupported by default	Depends	Depends	Unsupported by default	Depends	Unsupported	Unsupported	Depends	Yes
LM Studio	Native	Native	Unsupported by default	Depends	Depends	Unsupported by default	Depends	Unsupported	Unsupported	Depends	Yes
llama.cpp	Native	Native	Unsupported by default	Depends	Depends	Unsupported by default	Depends	Unsupported	Unsupported	Depends	Yes
Generic OpenAI-compatible	Native	Native	Depends	Depends	Depends	Depends	Depends	Unsupported by default	Unsupported by default	Depends	Depends

“Tools” in this table means client/function tools. ModelRuntime parses the provider’s tool calls, executes registered Praval tools, emits normalized tool_call and tool_result events, submits results, and continues until the model returns final text. This stable loop is implemented for OpenAI, Anthropic, Cohere, and Gemini. HITL-gated tools suspend with provider-neutral continuation state and can resume after approval, editing, or rejection.

Provider-Hosted Tools and MCP Descriptors

Provider-hosted tools, provider-hosted MCP descriptors, and computer-use descriptors are not stable cross-provider capabilities in the 0.8 line. OpenAI Responses and Anthropic Messages can receive raw experimental descriptors only through an explicit per-call opt-in:

response = agent.generate(
    "Use the provider-hosted tool when useful.",
    provider_options={
        "allow_experimental_tools": True,
        "experimental_tools": [{"type": "web_search"}],
    },
)

The runtime rejects this option for other providers, rejects it on OpenAI Chat Completions, and rejects nested credential-bearing fields. Raw descriptors are provider-specific and may change without Praval compatibility guarantees.

This is distinct from the first-class tools-only client in praval.mcp. That client owns a stdio or Streamable HTTP connection and registers discovered tools through Praval’s normal provider-neutral runtime. See MCP Tool Clients. Praval does not convert provider-hosted descriptors into client connections.

Local OpenAI-compatible profiles are intentionally conservative. Richer support requires an explicit capability override or registered profile, because local servers differ substantially by version, model, and command-line flags.

Registry Inspection

from praval import get_provider_registry

registry = get_provider_registry()
print(registry.list_providers())
print(registry.resolve_profile("ollama", "llama3"))
print(registry.resolve_capabilities("openai", "gpt-5.4-mini"))

The registry resolves provider aliases such as ollama, vllm, lmstudio, llama-cpp, and local to the OpenAI-compatible provider implementation while preserving alias-specific profiles.

Provider Profile Fields

Profiles can include provider, model, endpoint, local preset, context window, output token limits, default parameters, unsupported combinations, downgrade policy, and notes. The downgrade policy is error by default: a declared but unsupported feature should fail before execution.

Provider catalogs should be audited against provider documentation before a release and captured in tests. The 0.8 audit used the official OpenAI model catalog, Claude model overview, Gemini model catalog, and Cohere model catalog.

When a provider releases a new model, add or update a ProviderProfile, record the endpoint and capability assumptions, and add a registry test. Do not add placeholder model names to docs or defaults.