LiteLLM is a Python library and proxy server that provides a unified OpenAI-compatible interface to 100+ LLM providers — enabling developers to switch between GPT-4, Claude, Gemini, Llama, Mistral, and any other model by changing a single string, with built-in cost tracking, rate limiting, fallbacks, and load balancing across providers.

What Is LiteLLM?

• Definition: An open-source Python package (and optional proxy server) that maps every major LLM provider's API to the OpenAI chat.completions format — developers write code once using the OpenAI interface, LiteLLM handles translation to Anthropic, Google, Cohere, Mistral, Bedrock, or any other provider's native format.
• Provider Coverage: 100+ providers including OpenAI, Anthropic, Google Gemini, Azure OpenAI, AWS Bedrock, Cohere, Mistral, Together AI, Groq, Ollama, HuggingFace, Replicate, and any OpenAI-compatible endpoint.
• Proxy Server Mode: LiteLLM can run as a standalone proxy (litellm --model gpt-4) exposing an OpenAI-compatible HTTP endpoint — enabling existing OpenAI SDK code to route through LiteLLM without code changes, just a base_url update.
• Cost Tracking: Real-time token cost calculation across providers — response._hidden_params["response_cost"] gives per-call cost in USD.
• Load Balancing: Distribute requests across multiple API keys or providers with configurable routing strategies — reduce rate limit exposure and improve throughput.

Why LiteLLM Matters

• Vendor Independence: Write provider-agnostic code that can switch from OpenAI to Claude with one word — prevents vendor lock-in and enables rapid model evaluation.
• Cost Optimization: Route expensive requests to GPT-4o and simple classification to GPT-4o-mini (or Haiku) based on task complexity — cost-aware routing reduces LLM spend by 40-60% in mixed-workload applications.
• Reliability via Fallbacks: Configure automatic fallbacks — if OpenAI returns a 429 or 500, retry on Anthropic or Azure automatically, with no application code changes.
• Budget Guardrails: Set per-user, per-team, or per-project spending limits — when a user hits their monthly budget, LiteLLM blocks further requests without application-level changes.
• Observability: Built-in logging to Langfuse, Helicone, Datadog, and 20+ other platforms — every request is traced regardless of provider.

Core Python Usage

Basic Unified Call:

from litellm import completion

# Same interface, different models
response = completion(model="gpt-4o", messages=[{"role":"user","content":"Hello!"}])
response = completion(model="claude-3-5-sonnet-20241022", messages=[{"role":"user","content":"Hello!"}])
response = completion(model="gemini/gemini-1.5-pro", messages=[{"role":"user","content":"Hello!"}])
response = completion(model="ollama/llama3", messages=[{"role":"user","content":"Hello!"}])

Fallbacks:

from litellm import completion

response = completion(
    model="gpt-4o",
    messages=[{"role":"user","content":"Summarize this document."}],
    fallbacks=["claude-3-5-sonnet-20241022", "gemini/gemini-1.5-pro"],
    num_retries=2
)

Async + Load Balancing:

from litellm import Router

router = Router(model_list=[
    {"model_name": "gpt-4", "litellm_params": {"model":"gpt-4o", "api_key":"key1"}},
    {"model_name": "gpt-4", "litellm_params": {"model":"gpt-4o", "api_key":"key2"}},  # Round-robin across keys
])
response = await router.acompletion(model="gpt-4", messages=[...])

Proxy Server Setup

# config.yaml for LiteLLM proxy
model_list:
  - model_name: gpt-4
    litellm_params:
      model: openai/gpt-4o
      api_key: sk-...
  - model_name: claude
    litellm_params:
      model: anthropic/claude-3-5-sonnet-20241022
      api_key: sk-ant-...

router_settings:
  routing_strategy: least-busy
  fallbacks: [{"gpt-4": ["claude"]}]

Run with: litellm --config config.yaml --port 8000

Then existing OpenAI SDK code connects with just base_url="http://localhost:8000".

Key LiteLLM Features

• Token Counter: litellm.token_counter(model="gpt-4", messages=[...]) — accurate token counts before sending requests for budget planning.
• Cost Calculator: litellm.completion_cost(completion_response=response) — exact USD cost for any completed request across all providers.
• Streaming: Unified streaming interface — same stream=True parameter works for all providers, LiteLLM normalizes the SSE format.
• Vision: Pass image messages in OpenAI format — LiteLLM translates to provider-specific format (Anthropic base64, Gemini inlineData, etc.).
• Function Calling: Unified tool/function calling interface — define once in OpenAI format, LiteLLM handles provider-specific translation.

LiteLLM vs Alternatives

LiteLLM is the essential abstraction layer for any LLM application that needs to work across multiple providers — by normalizing 100+ provider APIs into the single most-familiar interface in AI development, LiteLLM enables teams to evaluate models, optimize costs, and ensure reliability without writing provider-specific integration code.