llms-sdk-py

Python bindings for the llms-sdk Rust crate — a unified interface for calling OpenAI- and Anthropic-compatible LLMs.

Installation

pip install llms-sdk-py
# or, with uv
uv add llms-sdk-py

Requires Python 3.10+.

Quick start

import asyncio
import llms_sdk_py as llm


async def main():
    request = llm.LLMRequest(
        model="gpt-4o",
        api_key="sk-...",
        messages=[
            llm.Message("user", [llm.TextPart("Hello, world!")])
        ],
        stream=False,
    )

    client = llm.LLM()
    response = await client.respond(request)
    print(response.message.content[0].text)


asyncio.run(main())

Streaming

Set stream=True and iterate over the async iterator:

import asyncio
import llms_sdk_py as llm


async def main():
    request = llm.LLMRequest(
        model="gpt-4o",
        api_key="sk-...",
        messages=[llm.Message("user", [llm.TextPart("Count to 5")])],
        stream=True,
    )

    client = llm.LLM()
    async for part in client.stream_response(request):
        if str(part.type) == "text":
            print(part.text.text_delta, end="", flush=True)
        elif str(part.type) == "end":
            print("\nDone!")


asyncio.run(main())

Multimodal input

Image

import llms_sdk_py as llm

image = llm.ImagePart("path/to/photo.jpg")      # or a URL string
message = llm.Message("user", [
    llm.TextPart("Describe this image."),
    image,
])

Audio (OpenAI only)

import llms_sdk_py as llm

audio = llm.AudioPart("path/to/audio.wav")       # or raw bytes
message = llm.Message("user", [
    llm.TextPart("Transcribe this audio."),
    audio,
])

Document (Anthropic only)

import llms_sdk_py as llm

doc = llm.DocumentPart("path/to/file.pdf")       # or raw bytes
message = llm.Message("user", [
    llm.TextPart("Summarize this document."),
    doc,
])

Tool use

import llms_sdk_py as llm

tool = llm.Tool(
    name="get_weather",
    description="Return weather for a city.",
    parameters_dict={
        "type": "object",
        "properties": {
            "city": {"type": "string"}
        },
        "required": ["city"]
    },
)

request = llm.LLMRequest(
    model="gpt-4o",
    api_key="sk-...",
    messages=[llm.Message("user", [llm.TextPart("What's the weather in Paris?")])],
    stream=False,
    tools=[tool],
    tool_choice="auto",
)

Structured output

import llms_sdk_py as llm

output_format = llm.OutputFormat(
    name="capital",
    description="Country capital",
    schema_dict={
        "type": "object",
        "properties": {
            "country": {"type": "string"},
            "capital": {"type": "string"}
        },
        "required": ["country", "capital"]
    },
)

request = llm.LLMRequest(
    model="gpt-4o",
    api_key="sk-...",
    messages=[llm.Message("user", [llm.TextPart("France")])],
    stream=False,
    output_format=output_format,
)

Retry policy

import llms_sdk_py as llm

policy = llm.RetryPolicy(max_retries=5, min_retry_interval=1000)
client = llm.LLM(retry_policy=policy)

License

MIT

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0.2.1

Aug 5, 2026

0.2.0

Jul 31, 2026

This release

0.1.0

Jul 28, 2026

llms_sdk_py 0.1.0

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