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 (all map to the API's provider-agnostic config):

resp = llmshim.chat(
    "openai/gpt-5.5",
    "Explain quicksort",
    max_tokens=500,
    temperature=0.7,
    top_p=0.9,
    top_k=40,
    stop=["\n\n"],
    reasoning_effort="high",
)

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-5.5", 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-5.5", "gemini/gemini-3-flash-preview"],
)

Error Handling

Non-streaming errors (bad model, unknown provider, provider failures) raise LlmShimError, which carries the API's structured error fields:

try:
    llmshim.chat("unknown/model", "hi")
except llmshim.LlmShimError as e:
    print(e.status_code)  # 400
    print(e.code)         # "bad_request"
    print(e.message)      # human-readable message

Streaming errors that occur mid-stream instead arrive as an error event (event["type"] == "error"); an HTTP error before the stream starts still raises LlmShimError.

Types

Spec-faithful TypedDict definitions are available in llmshim.types (and the common ones are re-exported at the top level) for static type-checking:

from llmshim.types import ChatResponse, StreamEvent, Message, Config

resp: ChatResponse = llmshim.chat("claude-sonnet-4-6", "hi")

Available: ChatRequest, ChatResponse, Config, Message, ToolCall, Usage, ResponseMessage, ModelEntry, ModelsResponse, HealthResponse, ErrorResponse, and the StreamEvent union (ContentEvent, ReasoningEvent, ToolCallEvent, UsageEvent, DoneEvent, ErrorEvent).

Other

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

How It Works

On first call, the package:

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

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

Development

The unit tests under tests/ are fully mocked — they run a local HTTP server returning canned JSON and SSE, so they need no API keys and make no real provider calls:

pip install httpx pytest
pytest tests/

test_e2e.py is a separate LIVE suite that spawns the real binary and makes billed provider calls; run it only when you deliberately want to hit real APIs.

Supported Models

Provider	Models
OpenAI	`gpt-5.5`, `gpt-5.5-pro`, `gpt-5.4`, `gpt-5.4-pro`, `gpt-5.4-mini`, `gpt-5.4-nano`
Anthropic	`claude-opus-5`, `claude-opus-4-8`, `claude-sonnet-5`, `claude-opus-4-7`, `claude-opus-4-6`, `claude-sonnet-4-6`, `claude-haiku-4-5-20251001`
Gemini	`gemini-3.7-flash`, `gemini-3.6-flash`, `gemini-3.5-flash`, `gemini-3.5-flash-lite`
xAI	`grok-4.6`, `grok-4.5`, `grok-4.3`, `grok-4.20-multi-agent-beta-0309`, `grok-4.20-beta-0309-reasoning`, `grok-4.20-beta-0309-non-reasoning`

Call llmshim.models() for the live list filtered to your configured providers.

Development Status
- 4 - Beta
Intended Audience
- Developers
License
- OSI Approved :: MIT License
Programming Language
- Python :: 3
- Rust
Topic
- Software Development :: Libraries

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llmshim 0.3.3

llmshim

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