# pllm.client Python API

Public objects, practical usage, and research context for pllm.client.

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Document ID: `pllm.docs.reference.python.pllm.client`  
Release: `0.1.0`

Client facades expose synchronous and asynchronous private-inference clients plus bounded response streams without requiring callers to import runtime internals. Presence documents API availability, not complete model, security, or deployment coverage; check [implementation status](/sdk/reference/status/) before relying on a path.

## Research context

This API is project infrastructure rather than an implementation of one specific paper. Relevant method pages link their primary sources separately.

## Python SDK example

The example performs a bounded offline task and checks an observable result. It does not download a checkpoint or contact a provider.

```python
import pllm.client as client

closed = []
stream = client.ResponseStream(iter(["first", "second"]), lambda: closed.append(True))
assert list(stream) == ["first", "second"]
stream.close()
assert closed == [True]
```

API: [`pllm.client`](/sdk/reference/python/pllm/client/#objects-and-signatures)

## Objects and signatures

### `AsyncOpenAI`

Asynchronous OpenAI-compatible client that targets the trusted local PLLM gateway.

- Canonical object: `pllm.runtime.client.AsyncOpenAI`
- Kind: `class`
- Signature/type: `(**kwargs: 'Any') -> 'None'`
- Public exports: `pllm.AsyncOpenAI`, `pllm.client.AsyncOpenAI`, `pllm.runtime.AsyncOpenAI`
- Public members: `close(self) -> 'None'`; `prepared_rows_for_response(self, input: 'str', max_output_tokens: 'int', *, model: 'str \| None' = None, instructions: 'str \| None' = None) -> 'int'`; `preprocess(self, model: 'str \| None' = None, *, count: 'int \| None' = None, stages: 'list[str] \| None' = None) -> 'dict[str, Any]'`; `property privacy_audit: 'PrivacyAudit'`

### `OpenAI`

Synchronous OpenAI-compatible client that targets the trusted local PLLM gateway.

- Canonical object: `pllm.runtime.client.OpenAI`
- Kind: `class`
- Signature/type: `(*, api_key: 'str \| None' = None, base_url: 'str \| None' = None, default_model: 'str \| None' = None, model: 'str \| None' = None, session_transport: 'str \| None' = None, correlation_mode: 'str \| None' = None, preparation_base_url: 'str \| None' = None, preparation_api_key: 'str \| None' = None, correlation_prefetch: 'int \| None' = None, prepared_inventory_rows: 'int \| None' = None, background_inventory_refill: 'bool' = True, token_cache_size: 'int \| None' = None, bundle_cache_mode: 'str \| None' = None, bundle_cache_dir: 'str \| Path \| None' = None, tenseal_path: 'str \| None' = None, timeout: 'float \| None' = None, http_client: 'httpx.Client \| None' = None, preparation_http_client: 'httpx.Client \| None' = None, experiment: 'Experiment \| ExperimentProfile \| None' = None) -> 'None'`
- Public exports: `pllm.OpenAI`, `pllm.client.OpenAI`, `pllm.runtime.OpenAI`
- Public members: `close(self) -> 'None'`; `prepared_inventory_status(self, model: 'str \| None' = None) -> 'dict[str, Any]'`; `prepared_rows_for_response(self, input: 'str', max_output_tokens: 'int', *, model: 'str \| None' = None, instructions: 'str \| None' = None) -> 'int'`; `preprocess(self, model: 'str \| None' = None, *, count: 'int \| None' = None, stages: 'list[str] \| None' = None) -> 'dict[str, Any]'`; `property privacy_audit: 'PrivacyAudit'`

### `ResponseStream`

Abstract base class for generic types.

- Canonical object: `pllm.runtime.client.ResponseStream`
- Kind: `class`
- Signature/type: `(iterator: 'Iterator[T]', close: 'Callable[[], None] \| None' = None) -> 'None'`
- Public exports: `pllm.ResponseStream`, `pllm.client.ResponseStream`
- Public members: `close(self) -> 'None'`