llmwire 0.1.1

Lightweight multi-provider LLM client for Python

LLMWire

Lightweight multi-provider LLM client for Python. A single async interface to OpenAI, Anthropic, and Ollama — with automatic fallback, exponential-backoff retry, streaming, and structured Pydantic output. No provider SDK dependencies; all requests go over plain httpx.

Features

• Unified API — one LLMClient for all supported providers
• Async-first — built entirely on asyncio and httpx
• Automatic fallback — on provider failure, tries the next provider in the list
• Exponential backoff — configurable retry with full jitter
• Streaming — token-by-token via client.stream(), async generator interface
• Structured output — pass any Pydantic BaseModel as response_model
• No provider SDKs — runtime deps are only httpx, pydantic, pydantic-settings, pyyaml
• Environment variable config — all settings readable from LLMKIT_* env vars

Quick Start

pip install llmwire

Chat

import asyncio
from llmwire import LLMClient, LLMConfig, ProviderConfig

config = LLMConfig(
    providers=[
        ProviderConfig(name="openai", api_key="sk-...", model="gpt-4o"),
        ProviderConfig(name="anthropic", api_key="sk-ant-...", model="claude-3-5-sonnet-20241022"),
    ]
)

async def main():
    async with LLMClient(config) as client:
        response = await client.chat("What is the capital of France?")
        print(response.content)
        # Provider: openai | Model: gpt-4o | Tokens: 42

asyncio.run(main())

Streaming

async def main():
    async with LLMClient(config) as client:
        async for chunk in client.stream("Write a haiku about async programming."):
            print(chunk.content, end="", flush=True)
        print()

Structured Output

from pydantic import BaseModel

class Sentiment(BaseModel):
    label: str        # "positive", "negative", or "neutral"
    confidence: float

async def main():
    async with LLMClient(config) as client:
        result: Sentiment = await client.chat(
            "Classify: 'I love this library!'",
            response_model=Sentiment,
        )
        print(result.label, result.confidence)  # positive  0.97

Configuration

Direct

from llmwire import LLMConfig, ProviderConfig

config = LLMConfig(
    providers=[
        ProviderConfig(name="openai", api_key="sk-...", model="gpt-4o"),
        ProviderConfig(name="ollama", model="llama3.2"),          # no key needed
    ],
    fallback=True,    # try next provider on failure (default: True)
    max_retries=3,    # per-provider retry attempts (default: 3)
    timeout=30.0,     # request timeout in seconds (default: 30.0)
)

Environment Variables

export LLMKIT_PROVIDERS__0__NAME=openai
export LLMKIT_PROVIDERS__0__API_KEY=sk-...
export LLMKIT_PROVIDERS__0__MODEL=gpt-4o

export LLMKIT_PROVIDERS__1__NAME=anthropic
export LLMKIT_PROVIDERS__1__API_KEY=sk-ant-...
export LLMKIT_PROVIDERS__1__MODEL=claude-3-5-sonnet-20241022

export LLMKIT_FALLBACK=true
export LLMKIT_MAX_RETRIES=3

config = LLMConfig()  # reads from environment

Provider Support

Provider	Chat	Streaming	Auth	Default endpoint
OpenAI	yes	yes	API key	https://api.openai.com/v1
Anthropic	yes	yes	API key	https://api.anthropic.com/v1
Ollama	yes	yes	none	http://localhost:11434

The base_url field on ProviderConfig lets you point any provider at a compatible endpoint (e.g. Azure OpenAI, local OpenAI-compatible servers).

Further Reading

• ARCHITECTURE.md — design decisions, component overview, and provider protocol
• CONTRIBUTING.md — dev setup, code style, and how to add a new provider
• Documentation — full API reference and guides

License

MIT. See LICENSE.