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ai-python-custom-provider
Use for implementing custom providers in AI SDK for Python.
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Use for implementing custom providers in AI SDK for Python.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
Use when building serverless AI SDK for Python endpoints, handling hook approvals, deferring hooks, or resuming runs across requests.
Use when connecting AI SDK for Python streams to AI SDK UI useChat clients.
Use for AI SDK for Python basics. Configure a model, make messages, stream, declare tools, build a basic agent.
Use for AI SDK for Python async-generator tools, streaming tool output, subagent tools, PartialToolCallResult events, and custom tool aggregation.
Use for the subagent-as-a-tool pattern.
Use when building custom agent loops. Modify tool dispatch, history management, hooks, control flow.
| name | ai-python-custom-provider |
| description | Use for implementing custom providers in AI SDK for Python. |
| metadata | {"sdk-version":"0.2.1"} |
Providers emit model events. They do not run Python tools. ai.stream collects
events into a Message. ai.Agent adds tool execution, hooks, and replay.
Minimal shape:
from collections.abc import AsyncGenerator, Sequence
from typing import Any, Literal
import pydantic
import ai
class MyProtocol(ai.ProviderProtocol[Any]):
protocol_class_id: Literal["my_protocol"] = "my_protocol"
def stream(
self,
client: Any,
model: ai.Model,
messages: list[ai.messages.Message],
*,
tools: Sequence[ai.tools.Tool] | None = None,
output_type: type[pydantic.BaseModel] | None = None,
params: ai.InferenceRequestParams | None = None,
provider: str,
) -> AsyncGenerator[ai.events.Event]:
return self._stream(client, model, messages, tools=tools)
async def _stream(
self,
client: Any,
model: ai.Model,
messages: list[ai.messages.Message],
*,
tools: Sequence[ai.tools.Tool] | None,
) -> AsyncGenerator[ai.events.Event]:
yield ai.events.StreamStart()
yield ai.events.TextStart(block_id="text")
yield ai.events.TextDelta(block_id="text", chunk="Hello")
yield ai.events.TextEnd(block_id="text")
yield ai.events.StreamEnd()
class MyProvider(ai.Provider[Any]):
provider_class_id: Literal["my_provider"] = "my_provider"
name: str = "my"
default_base_url: str = "https://example.invalid"
def __init__(self, *, client: Any) -> None:
super().__init__()
self._set_client(client)
def default_protocol(self) -> ai.ProviderProtocol[Any]:
return MyProtocol()
async def list_models(self) -> list[str]:
return ["my-model"]
async def probe(self, model: ai.Model) -> None:
return None
model = ai.Model(id="my-model", provider=MyProvider(client=client))
For Python tool calls, emit ToolStart, ToolDelta, and ToolEnd:
yield ai.events.ToolStart(tool_call_id=tcid, tool_name=name)
yield ai.events.ToolDelta(tool_call_id=tcid, chunk=args_json)
yield ai.events.ToolEnd(
tool_call_id=tcid,
tool_call=ai.messages.DUMMY_TOOL_CALL,
)
The stream collector fills event.tool_call with the aggregated tool call.
Then Agent resolves and runs the tool.
If the provider runs its own built-in tool, emit BuiltinToolStart,
BuiltinToolDelta, BuiltinToolEnd, and BuiltinToolResult instead.
Do not implement a custom provider for normal app configuration. Prefer
ai.get_provider(...), ai.get_model(...), or a protocol override unless you
are adding a new upstream API adapter.