llmshim 0.1.10

Multi-provider LLM gateway — one interface, every provider

llmshim

One interface, every LLM provider. The proxy server starts automatically — no setup needed.

Install

pip install llmshim

Configure

import llmshim

# Set API keys (writes to ~/.llmshim/config.toml — only needed once)
llmshim.configure(
    anthropic="sk-ant-...",
    openai="sk-...",
    gemini="AIza...",
    xai="xai-...",
)

Or from the CLI: llmshim configure

Chat

import llmshim

resp = llmshim.chat("claude-sonnet-4-6", "What is Rust?")
print(resp["message"]["content"])

With options:

resp = llmshim.chat(
    "openai/gpt-5.4",
    "Explain quicksort",
    max_tokens=500,
    temperature=0.7,
)

With message history:

resp = llmshim.chat("claude-sonnet-4-6", [
    {"role": "system", "content": "You are a pirate."},
    {"role": "user", "content": "Hello!"},
], max_tokens=500)

Streaming

for event in llmshim.stream("claude-sonnet-4-6", "Write a poem"):
    if event["type"] == "content":
        print(event["text"], end="", flush=True)
    elif event["type"] == "reasoning":
        pass  # thinking tokens
    elif event["type"] == "usage":
        print(f"\n[↑{event['input_tokens']} ↓{event['output_tokens']}]")

Multi-Model Conversations

Switch models mid-conversation. History carries over.

messages = [{"role": "user", "content": "What is a closure?"}]

r1 = llmshim.chat("claude-sonnet-4-6", messages, max_tokens=500)
print(f"Claude: {r1['message']['content']}")

messages.append({"role": "assistant", "content": r1["message"]["content"]})
messages.append({"role": "user", "content": "Now explain differently."})

r2 = llmshim.chat("gpt-5.4", messages, max_tokens=500)
print(f"GPT: {r2['message']['content']}")

Reasoning / Thinking

resp = llmshim.chat(
    "claude-sonnet-4-6",
    "Solve: x^2 - 5x + 6 = 0",
    max_tokens=4000,
    reasoning_effort="high",
)
print(resp["reasoning"])        # thinking content
print(resp["message"]["content"])  # answer

Tool Use / Function Calling

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get current weather",
        "parameters": {
            "type": "object",
            "properties": {"city": {"type": "string"}},
            "required": ["city"],
        },
    },
}]

resp = llmshim.chat("claude-sonnet-4-6", "Weather in Tokyo?", max_tokens=500, tools=tools)
for tc in resp["message"].get("tool_calls", []):
    print(f"{tc['function']['name']}({tc['function']['arguments']})")

Tools are accepted in OpenAI Chat Completions format and auto-translated to each provider's native format.

Fallback Chains

resp = llmshim.chat(
    "anthropic/claude-sonnet-4-6",
    "Hello",
    max_tokens=100,
    fallback=["openai/gpt-5.4", "gemini/gemini-3-flash-preview"],
)

Other

llmshim.models()   # list available models
llmshim.health()   # {"status": "ok", "providers": [...]}

How It Works

On first call, the package:

1. Finds the llmshim binary (bundled, on PATH, or in repo)
2. Starts the proxy on a random localhost port
3. Routes your request through it
4. Server stops automatically when Python exits

No Docker, no background services, no manual server management.

Supported Models

Provider	Models
OpenAI	gpt-5.4
Anthropic	claude-opus-4-6, claude-sonnet-4-6, claude-haiku-4-5-20251001
Gemini	gemini-3.1-pro-preview, gemini-3-flash-preview, gemini-3.1-flash-lite-preview
xAI	grok-4.20-multi-agent-beta-0309, grok-4.20-beta-0309-reasoning, grok-4.20-beta-0309-non-reasoning, grok-4-1-fast-reasoning, grok-4-1-fast-non-reasoning