llm-catalog-ai-sdk

AI SDK for Python adapter for llm-catalog. It turns a role in your catalog config (ai-sdk-catalog.json, shared verbatim with ai-sdk-catalog) into a native ai.Model — a gateway model is routed through your gateway via the core GatewayTransport, a direct model calls the vendor's own endpoint.

import json
from pathlib import Path

import ai
from llm_catalog.ai_sdk import AISDKCatalog

config = json.loads(Path("ai-sdk-catalog.json").read_text(encoding="utf-8"))

async with AISDKCatalog(config) as cat:  # closes its HTTP clients on exit
    model = cat.model_for_role("fast")
    params = cat.params_for_role("fast")
    async with ai.stream(model, [ai.user_message("hi")], params=params) as stream:
        async for event in stream:
            if isinstance(event, ai.events.TextDelta):
                print(event.chunk, end="", flush=True)

ai.Model carries no default call settings, so the catalog's merged settings come back separately from params_for_role() / params() as an ai.InferenceRequestParams to pass (or refine) per call. providerOptions is skipped: its entries are options of the TypeScript AI SDK providers and have no counterpart here.

Vendors and call surfaces

ai speaks three wire protocols directly, which covers three of the shared schema's vendors. Every other combination raises LLMCatalogError when the model is built; nothing is silently routed to a different API.

chat on anthropic follows ai-sdk-catalog, where a single-surface vendor exposes chat as an alias of its one surface. google, mistral, groq, and the other vendors of the shared schema have no native protocol in ai; declare an endpoint that speaks Chat Completions as an openai-compatible vendor (or backend) instead. stopSequences has no stop parameter on the OpenAI Responses API, so it raises there rather than being dropped.

Beta notice

ai is in public beta and may change its API in any 0.x minor release. This adapter pins the ai minor line it is tested against (currently 0.7.x) and uses only ai's documented surface: ai.get_provider with base_url / api_key / client, ai.Model with an explicit protocol, and ai.InferenceRequestParams. Its tests send requests through ai to a mock transport, so an ai release that stops honouring the injected client fails CI instead of failing at runtime.

Requires Python 3.12+ (the floor of ai). The HTTP client is httpx2, which ai and the official OpenAI / Anthropic SDKs are built on; the header_rewrite / body_rewrite hooks receive httpx2.Headers / httpx2.Request. Pass transport_factory to supply the underlying httpx2 transport yourself (a proxy, mTLS, connection limits, or httpx2.MockTransport in tests). Installing this package pulls in neither pydantic-ai nor litellm.

See the repository README for the full picture and the verification notes (§9).

import namespace: llm_catalog.ai_sdk