LiteLLM

Unified Python interface for calling 100+ LLM APIs using consistent OpenAI format. Provides standardized exception handling, retry/fallback logic, and cost tracking across multiple providers.

When to Use This Skill

Use this skill when:

• Integrating with multiple LLM providers through a single interface
• Routing requests to local llamafile servers using OpenAI-compatible endpoints
• Implementing retry and fallback logic for LLM calls
• Building applications requiring consistent error handling across providers
• Tracking LLM usage costs across different providers
• Converting between provider-specific APIs and OpenAI format
• Deploying LLM proxy servers with unified configuration
• Testing applications against both cloud and local LLM endpoints

Core Capabilities

Provider Support

LiteLLM supports 100+ providers through consistent OpenAI-style API:

• Cloud Providers: OpenAI, Anthropic, Google, Azure, AWS Bedrock
• Local Servers: llamafile, Ollama, LocalAI, vLLM
• Unified Format: All requests use OpenAI message format
• Exception Mapping: All provider errors map to OpenAI exception types

Key Features

1. Unified API: Single

   completion()

   function for all providers
2. Exception Handling: All exceptions inherit from OpenAI types
3. Retry Logic: Built-in retry with configurable attempts
4. Streaming Support: Sync and async streaming for all providers
5. Cost Tracking: Automatic usage and cost calculation
6. Proxy Mode: Deploy centralized LLM gateway

Installation

# Using pip
pip install litellm

# Using uv
uv add litellm

Llamafile Integration

Provider Configuration

All llamafile models MUST use the

llamafile/

model = "llamafile/mistralai/mistral-7b-instruct-v0.2"
model = "llamafile/gemma-3-3b"

API Base URL

The

api_base

api_base = "http://localhost:8080/v1"

Critical Requirements:

• Include

  /v1

  suffix
• Do NOT add endpoint paths like

  /chat/completions

  (LiteLLM adds these automatically)
• Default llamafile port is 8080

Environment Variable Configuration

import os

os.environ["LLAMAFILE_API_BASE"] = "http://localhost:8080/v1"

Basic Usage Patterns

Synchronous Completion

import litellm

response = litellm.completion(
    model="llamafile/mistralai/mistral-7b-instruct-v0.2",
    messages=[{"role": "user", "content": "Summarize this diff"}],
    api_base="http://localhost:8080/v1",
    temperature=0.2,
    max_tokens=80,
)

print(response.choices[0].message.content)

Asynchronous Completion

from litellm import acompletion
import asyncio

async def generate_message():
    response = await acompletion(
        model="llamafile/gemma-3-3b",
        messages=[{"role": "user", "content": "Write a commit message"}],
        api_base="http://localhost:8080/v1",
        temperature=0.3,
        max_tokens=200,
    )
    return response.choices[0].message.content

result = asyncio.run(generate_message())
print(result)

Async Streaming

from litellm import acompletion
import asyncio

async def stream_response():
    response = await acompletion(
        model="llamafile/gemma-3-3b",
        messages=[{"role": "user", "content": "Hello, how are you?"}],
        api_base="http://localhost:8080/v1",
        stream=True,
    )

    async for chunk in response:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="", flush=True)
    print()

asyncio.run(stream_response())

Embeddings

from litellm import embedding
import os

os.environ["LLAMAFILE_API_BASE"] = "http://localhost:8080/v1"

response = embedding(
    model="llamafile/sentence-transformers/all-MiniLM-L6-v2",
    input=["Hello world"],
)

print(response)

Exception Handling

Import Pattern

All exceptions can be imported directly from

litellm

from litellm import (
    BadRequestError,           # 400 errors
    AuthenticationError,       # 401 errors
    NotFoundError,             # 404 errors
    Timeout,                   # 408 errors (alias: openai.APITimeoutError)
    RateLimitError,            # 429 errors
    APIConnectionError,        # 500 errors / connection issues (default)
    ServiceUnavailableError,   # 503 errors
)

Exception Types Reference

BadRequestError

ContextWindowExceededError

ContentPolicyViolationError

AuthenticationError

PermissionDeniedError

NotFoundError

Timeout

RateLimitError

APIConnectionError

APIError

ServiceUnavailableError

InternalServerError

Exception Attributes

All LiteLLM exceptions include:

• status_code

  : HTTP status code

• message

  : Error message

• llm_provider

  : Provider that raised the exception

Exception Handling Example

import litellm
import openai

try:
    response = litellm.completion(
        model="llamafile/gemma-3-3b",
        messages=[{"role": "user", "content": "Hello"}],
        api_base="http://localhost:8080/v1",
        timeout=30.0,
    )
except openai.APITimeoutError as e:
    # LiteLLM exceptions inherit from OpenAI types
    print(f"Timeout: {e}")
except litellm.APIConnectionError as e:
    print(f"Connection failed: {e.message}")
    print(f"Provider: {e.llm_provider}")

Alternative Import from litellm.exceptions

from litellm.exceptions import BadRequestError, AuthenticationError, APIError

try:
    response = litellm.completion(
        model="llamafile/gemma-3-3b",
        messages=[{"role": "user", "content": "Hello"}],
        api_base="http://localhost:8080/v1",
    )
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except BadRequestError as e:
    print(f"Bad request: {e}")
except APIError as e:
    print(f"API error: {e}")

Checking If Exception Should Retry

import litellm

try:
    response = litellm.completion(
        model="llamafile/gemma-3-3b",
        messages=[{"role": "user", "content": "Hello"}],
        api_base="http://localhost:8080/v1",
    )
except Exception as e:
    if hasattr(e, 'status_code'):
        should_retry = litellm._should_retry(e.status_code)
        print(f"Should retry: {should_retry}")

Retry and Fallback Configuration

from litellm import completion

response = completion(
    model="llamafile/gemma-3-3b",
    messages=[{"role": "user", "content": "Hello"}],
    api_base="http://localhost:8080/v1",
    num_retries=3,      # Retry 3 times on failure
    timeout=30.0,       # 30 second timeout
)

Proxy Server Configuration

For proxy deployments, use

config.yaml

model_list:
  - model_name: commit-polish-model
    litellm_params:
      model: llamafile/gemma-3-3b          # add llamafile/ prefix
      api_base: http://localhost:8080/v1   # add api base for OpenAI compatible provider

Application Integration Patterns

Connection Verification Pattern

import litellm
from litellm import APIConnectionError

def verify_llamafile_connection(api_base: str = "http://localhost:8080/v1") -> bool:
    """Check if llamafile server is running."""
    try:
        litellm.completion(
            model="llamafile/test",
            messages=[{"role": "user", "content": "test"}],
            api_base=api_base,
            max_tokens=1,
        )
        return True
    except APIConnectionError:
        return False

Async Service Pattern

import litellm
from litellm import acompletion, APIConnectionError
import asyncio

class AIService:
    """LiteLLM wrapper with llamafile routing."""

    def __init__(self, model: str, api_base: str, temperature: float = 0.3, max_tokens: int = 200):
        self.model = model
        self.api_base = api_base
        self.temperature = temperature
        self.max_tokens = max_tokens

    async def generate_commit_message(self, diff: str, system_prompt: str) -> str:
        """Generate a commit message using the LLM."""
        try:
            response = await acompletion(
                model=self.model,
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": f"Generate a commit message for this diff:\n\n{diff}"},
                ],
                api_base=self.api_base,
                temperature=self.temperature,
                max_tokens=self.max_tokens,
            )
            return response.choices[0].message.content.strip()
        except APIConnectionError as e:
            raise RuntimeError(f"Failed to connect to llamafile server at {self.api_base}: {e.message}")

Common Pitfalls to Avoid

1. Missing

   llamafile/

   prefix: Without prefix, LiteLLM won't route to OpenAI-compatible endpoint
2. Wrong port: Llamafile uses 8080 by default, not 8000
3. Missing

   /v1

   suffix: API base must end with

   /v1

4. Adding extra path segments: Do NOT use

   http://localhost:8080/v1/chat/completions

   - LiteLLM adds the endpoint path automatically
5. API key requirement: No API key needed for local llamafile (use empty string or any value if required by validation)

Configuration Examples

TOML Configuration

# ~/.config/commit-polish/config.toml
[ai]
model = "llamafile/gemma-3-3b"  # MUST have llamafile/ prefix
temperature = 0.3
max_tokens = 200

Environment Variables

export LLAMAFILE_API_BASE="http://localhost:8080/v1"
export LITELLM_LOG="INFO"  # Enable LiteLLM debug logging

Related Skills

For comprehensive documentation on related tools:

• llamafile: Activate the llamafile skill using

  Skill(command: "llamafile")

  for llamafile server setup, model management, and local LLM deployment patterns
• uv: Activate the uv skill using

  Skill(command: "uv")

  for Python project management, dependency handling, and virtual environment workflows

References

Official Documentation

• LiteLLM Documentation - Main documentation portal
• Llamafile Provider Docs - Llamafile-specific configuration
• Exception Mapping - Complete exception reference
• GitHub Repository - Source code and examples

Provider-Specific Documentation

• Llamafile API Endpoints - Llamafile OpenAI-compatible API reference
• Completion Streaming - Streaming implementation guide

Version Information

• Documentation verified against: LiteLLM GitHub repository (main branch, accessed 2025-01-15)
• Python: 3.11+
• Llamafile: 0.9.3+