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-...",
openrouter="sk-or-...",
)
Or from the CLI: llmshim configure
Self-hosted servers (vLLM / SGLang)
vLLM and SGLang are configured via environment variables (not
config.toml) — the auto-spawned proxy inherits them from your Python
process. Set them before your first call:
import os
os.environ["VLLM_BASE_URL"] = "http://localhost:8000/v1"
os.environ["VLLM_API_KEY"] = "..." # optional
os.environ["SGLANG_BASE_URL"] = "http://localhost:30000/v1"
os.environ["SGLANG_API_KEY"] = "..." # optional
Then address them via the model string — vllm/<served-model> or
sglang/<served-model> (see the model table below).
Chat
import llmshim
resp = llmshim.chat("claude-sonnet-5-5", "What is Rust?")
print(resp["message"]["content"])
With options (all map to the API's provider-agnostic config):
resp = llmshim.chat(
"openai/gpt-6-sol",
"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-5-5", [
{"role": "system", "content": "You are a pirate."},
{"role": "user", "content": "Hello!"},
], max_tokens=500)
Streaming
for event in llmshim.stream("claude-sonnet-5-5", "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-5-5", 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-6-sol", messages, max_tokens=500)
print(f"GPT: {r2['message']['content']}")
Reasoning / Thinking
Two provider-agnostic knobs control reasoning; both are clamped to the nearest tier the target model supports:
reasoning_effort—"none","low","medium","high","xhigh", or"max"reasoning_mode—"standard"(default) or"pro"(requests substantially more model work; native on OpenAI gpt-5.6/-pro, emulated as an effort bump elsewhere)
resp = llmshim.chat(
"claude-sonnet-5-5",
"Solve: x^2 - 5x + 6 = 0",
max_tokens=4000,
reasoning_effort="high",
reasoning_mode="pro",
)
print(resp["reasoning"]) # thinking content
print(resp["message"]["content"]) # answer
For full native control, bypass the unified mapping with a namespaced
provider_config (see below), e.g.
provider_config={"x-anthropic": {"thinking": {"type": "adaptive"}, "output_config": {"effort": "high"}}}.
Provider-Specific Controls (provider_config)
provider_config merges into the request root and carries anything the
unified config doesn't cover. Native provider controls MUST be namespaced
per provider (x-anthropic, x-openai, x-gemini, x-openrouter, x-vllm,
x-sglang); it also carries the top-level tools, response_format, and
reasoning_summary keys.
resp = llmshim.chat(
"anthropic/claude-sonnet-5-5",
"Solve this step by step: 17 * 23",
max_tokens=4000,
provider_config={
# native Anthropic adaptive-thinking control
"x-anthropic": {"thinking": {"type": "adaptive"}, "output_config": {"effort": "high"}},
},
)
OpenRouter routing preferences use the x-openrouter namespace:
resp = llmshim.chat(
"openrouter/anthropic/claude-sonnet-5",
"Hello",
max_tokens=200,
provider_config={"x-openrouter": {"provider": {"sort": "throughput"}}},
)
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-5-5", "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-5-5",
"Hello",
max_tokens=100,
fallback=["openai/gpt-6-sol", "gemini/gemini-3.8-flash"],
)
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-5-5", "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
llmshimbinary (bundled, on PATH, or in repo) - Lets the child bind a random localhost port and reads its private startup record
- Pins the child's one-time TLS certificate, authenticates every request, and keeps a parent-liveness pipe open
- 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.
Advertised Models
| Provider | Models |
|---|---|
| OpenAI | gpt-6-astra, gpt-6-sol, gpt-6-luna |
| Anthropic | claude-fable-5-1, claude-opus-5-5, claude-sonnet-5-5, claude-haiku-4-5-20251001 |
| Gemini | gemini-3.8-flash, gemini-3.5-flash-lite |
| xAI | grok-4.6 |
Call llmshim.models() for the live list filtered to your configured providers.
OpenRouter & self-hosted (vLLM / SGLang)
These providers are addressed by the model string plus environment variables — any model the upstream serves is reachable, so they aren't in the table above.
| Provider | Address as | Env vars | Native controls |
|---|---|---|---|
| OpenRouter | openrouter/<vendor>/<model> (e.g. openrouter/anthropic/claude-sonnet-5) |
OPENROUTER_API_KEY (or llmshim.configure(openrouter=...)) |
provider_config={"x-openrouter": {...}} (provider, models, transforms) |
| vLLM | vllm/<served-model> |
VLLM_BASE_URL (+ optional VLLM_API_KEY) |
provider_config={"x-vllm": {...}} |
| SGLang | sglang/<served-model> |
SGLANG_BASE_URL (+ optional SGLANG_API_KEY) |
provider_config={"x-sglang": {...}} |
1 maintainer
Metadata
1 maintainer
Release files for llmshim 0.19.0
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Built distributions (wheels)
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| llmshim-0.19.0-py3-none-win_amd64.whl | Python 3 | none | Windows x86-64 | Details |
| llmshim-0.19.0-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | Python 3 | none | Linux glibc 2.17+ x86-64 | Details |
| llmshim-0.19.0-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl | Python 3 | none | Linux glibc 2.17+ ARM64 | Details |
| llmshim-0.19.0-py3-none-macosx_11_0_arm64.whl | Python 3 | none | macOS 11.0+ ARM64 | Details |
| llmshim-0.19.0-py3-none-macosx_10_12_x86_64.whl | Python 3 | none | macOS 10.12+ x86-64 | Details |
Total release size: 36.3 MB
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