litelm 0.5.1

Lightweight litellm replacement — thin OpenAI-SDK routing layer for multi-provider LLM calls

litelm

litellm's routing + translation in ~2,300 lines and 2 dependencies (openai, httpx).

litellm routes LLM calls across providers and translates between message formats. That core is buried under 100k+ LOC of proxy servers, caching layers, cost tracking, and dozens of features most users never touch. litelm extracts just the call path — model routing, message translation, streaming, tool use, embeddings — and nothing else. No Router class, no proxy, no caching.

Install

pip install litelm                # openai + httpx
pip install litelm[anthropic]     # + anthropic SDK
pip install litelm[bedrock]       # + boto3
pip install litelm[all]           # everything

Usage

import litelm

# Basic completion
response = litelm.completion("openai/gpt-4o", messages=[{"role": "user", "content": "Hello!"}])
print(response.choices[0].message.content)

# Streaming
for chunk in litelm.completion("groq/llama-3.1-70b-versatile", messages=[...], stream=True):
    print(chunk.choices[0].delta.content or "", end="")

# Embeddings
response = litelm.embedding("openai/text-embedding-3-small", input=["hello world"])

Every function has an async variant: acompletion, aembedding, aresponses, atext_completion.

The API mirrors litellm — same function names, same arguments, same response types. If you're using litellm today, switching is s/litellm/litelm/ in your imports.

What's in / what's out

	litellm	litelm
Model routing (provider/model → right endpoint)	✓	✓
Message translation (Anthropic, Bedrock, Cloudflare, Mistral)	✓	✓
Streaming + stream_chunk_builder	✓	✓
Tool use (function calling)	✓	✓
Embeddings	✓	✓
Text completions	✓	✓
OpenAI Responses API	✓	✓
Mock responses	✓	✓
Router (load balancing, fallbacks)	✓	✗
Proxy server	✓	✗
Caching / budgeting / cost tracking	✓	✗
Token counting	✓	✗
Image gen, audio, OCR, fine-tuning	✓	✗
Agents, guardrails, scheduler	✓	✗

Providers

Routes to 19 providers via "provider/model-name" syntax. Any OpenAI-compatible endpoint works via api_base.

Provider	Env Var	Handler	Verified
OpenAI	OPENAI_API_KEY	OpenAI SDK	Yes
Anthropic	ANTHROPIC_API_KEY	Custom	Yes
Groq	GROQ_API_KEY	OpenAI-compat	Yes
Mistral	MISTRAL_API_KEY	Custom	Yes
xAI	XAI_API_KEY	OpenAI-compat	Yes
OpenRouter	OPENROUTER_API_KEY	OpenAI-compat	Yes
Azure	AZURE_API_KEY	OpenAI SDK (Azure)	No
Bedrock	AWS_ACCESS_KEY_ID	Custom	No
Cloudflare	CLOUDFLARE_API_TOKEN	Custom	No
Together	TOGETHERAI_API_KEY	OpenAI-compat	No
Fireworks	FIREWORKS_API_KEY	OpenAI-compat	No
DeepSeek	DEEPSEEK_API_KEY	OpenAI-compat	No
Perplexity	PERPLEXITYAI_API_KEY	OpenAI-compat	No
DeepInfra	DEEPINFRA_API_TOKEN	OpenAI-compat	No
Gemini	GEMINI_API_KEY	OpenAI-compat	No
Cohere	COHERE_API_KEY	OpenAI-compat	No
Ollama	—	OpenAI-compat	No
vLLM	—	OpenAI-compat	No
LM Studio	—	OpenAI-compat	No

API Keys

Set the environment variable for your provider:

export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...

Or pass directly:

litelm.completion("openai/gpt-4o", messages=[...], api_key="sk-...")
litelm.completion("openai/gpt-4o", messages=[...], api_base="http://localhost:8000/v1")

Error Handling

All provider errors are mapped to litelm's exception hierarchy:

from litelm import ContextWindowExceededError, RateLimitError, AuthenticationError

try:
    response = litelm.completion("openai/gpt-4o", messages=messages)
except ContextWindowExceededError:
    # prompt too long — truncate and retry
    pass
except RateLimitError:
    # back off
    pass
except AuthenticationError:
    # bad API key
    pass

Tool Calling

tools = [{"type": "function", "function": {
    "name": "get_weather",
    "parameters": {"type": "object", "properties": {"city": {"type": "string"}}},
}}]

response = litelm.completion(
    "openai/gpt-4o", messages=[{"role": "user", "content": "Weather in Paris?"}],
    tools=tools, tool_choice="required",
)
tool_call = response.choices[0].message.tool_calls[0]
print(tool_call.function.name, tool_call.function.arguments)

Custom / Local Providers

Any OpenAI-compatible server works via api_base:

# vLLM
litelm.completion("openai/my-model", messages=[...], api_base="http://localhost:8000/v1")

# Ollama
litelm.completion("ollama/llama3", messages=[...], api_base="http://localhost:11434/v1")

# LM Studio
litelm.completion("openai/local-model", messages=[...], api_base="http://localhost:1234/v1")

Development transparency

litelm is human-directed, AI-assisted software. Much of the code was written with Claude Code using Claude Opus 4.6/4.7. Code written from 2026-05-14 onward is written through Pi using GPT-5.5. Compatibility claims are based on tests and maintainer review, not AI authorship.

Upstream attestation

Maintainer attestation, 2026-05-14: upstream litellm reviewed through 649eb2d. Upstream-watch found no actionable in-scope drift; the only current drift is responses_api_bridge_check(reasoning_summary), part of litellm's chat-completions↔Responses bridge and outside litelm's declared scope. Local scoped tests: 214 passed, 54 skipped with uv run --extra all pytest tests/ --ignore=tests/ported --timeout=10 -q.

This attests litelm's declared routing/formatting/DSPy surface only, not full litellm compatibility.

Status

Alpha. 214 own tests passing, 56 ported litellm tests passing unmodified via sys.modules shimming.

DSPy drop-in verified — all 7 execution paths proven live (Predict, CoT, typed signatures, streaming, embeddings, tool use, multi-output).

Tests

uv run pytest tests/ -x --ignore=tests/ported        # 129 unit tests
uv run pytest tests/test_live.py -m live --timeout=30 # 37 live provider tests
uv run pytest tests/test_dspy_smoke.py -m live --timeout=60  # 10 DSPy integration tests

Live tests require API keys in .env.test. Skipped by default; run with -m live.