paddler-client 4.0.0

Python client for Paddler LLM load balancer

paddler-client

Python client for the Paddler LLM load balancer.

Install

pip install paddler-client

Requires Python 3.11+. Built on httpx, websockets, and pydantic v2.

Quick start

WebSocket inference (multiplexed, request-id-keyed)

import asyncio

from paddler_client import ClientInference
from paddler_client.continue_from_conversation_history_params import (
    ContinueFromConversationHistoryParams,
)
from paddler_client.conversation_message import ConversationMessage


async def main() -> None:
    params = ContinueFromConversationHistoryParams(
        add_generation_prompt=True,
        conversation_history=[
            ConversationMessage(content="Say hello.", role="user"),
        ],
        enable_thinking=False,
        max_tokens=128,
    )

    async with ClientInference(url="http://localhost:8061") as client:
        stream = await client.continue_from_conversation_history(params)

        async for message in stream:
            if message.is_token:
                print(message.token, end="", flush=True)
            elif message.is_terminal:
                break


asyncio.run(main())

HTTP NDJSON streaming

async with ClientInference(url="http://localhost:8061") as client:
    async for message in client.post_continue_from_conversation_history(params):
        if message.is_token:
            print(message.token, end="", flush=True)
        elif message.is_terminal:
            break

ClientInference also provides continue_from_raw_prompt and generate_embedding_batch.

Management

from paddler_client import ClientManagement


async with ClientManagement(url="http://localhost:8062") as client:
    snapshot = await client.get_agents()

    for agent in snapshot.agents:
        print(agent.id, agent.slots_total)

    state = await client.get_balancer_desired_state()
    await client.put_balancer_desired_state(state)

    metrics = await client.get_metrics()

ClientManagement also provides agents_stream, get_buffered_requests, buffered_requests_stream, get_chat_template_override, and get_model_metadata.

Coverage

• Transport: WebSocket (multiplexed), HTTP NDJSON, HTTP JSON, Server-Sent Events
• Models: every Paddler wire-format type (validated via pydantic)
• Fully typed (py.typed), async-first API
• Specialized error types per failure mode

License

Apache-2.0