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LiteLLM

Call 100+ LLM providers through LiteLLM's unified API. Use when you need to call a different model than your primary (e.g., use GPT-4 for code review while running on Claude), compare outputs from multiple models, route to cheaper models for simple tasks, or access models your runtime doesn't natively support.
通过 LiteLLM 统一 API 调用 100 多个 LLM 提供商。适用于以下场景:需调用主模型以外的模型(如在 Claude 上运行时调用 GPT-4 进行代码审查)、对比多模型输出、将简单任务路由至更经济的模型,或访问运行环境原生不支持的模型。
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概述

LiteLLM - Multi-Model LLM Calls

Use LiteLLM when you need to call LLMs beyond your primary model.

When to Use

  • Model comparison: Get outputs from multiple models and compare
  • Specialized routing: Use code-optimized models for code, writing models for prose
  • Cost optimization: Route simple queries to cheaper models
  • Fallback access: Access models your runtime doesn't support

Quick Start

import litellm

# Call any model with unified API
response = litellm.completion(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Explain this code"}]
)
print(response.choices[0].message.content)

Common Patterns

Compare Multiple Models

import litellm

prompt = [{"role": "user", "content": "What's the best approach to X?"}]

models = ["gpt-4o", "claude-sonnet-4-20250514", "gemini/gemini-1.5-pro"]
for model in models:
    resp = litellm.completion(model=model, messages=prompt)
    print(f"{model}: {resp.choices[0].message.content[:200]}...")

Route by Task Type

import litellm

def smart_call(task_type: str, prompt: str) -> str:
    model_map = {
        "code": "gpt-4o",           # Strong at code
        "writing": "claude-sonnet-4-20250514",  # Strong at prose
        "simple": "gpt-4o-mini",    # Cheap for simple tasks
        "reasoning": "o1-preview",  # Deep reasoning
    }
    model = model_map.get(task_type, "gpt-4o")
    resp = litellm.completion(
        model=model,
        messages=[{"role": "user", "content": prompt}]
    )
    return resp.choices[0].message.content

Use LiteLLM Proxy (Recommended)

If a LiteLLM proxy is available, point to it for caching, rate limiting, and observability:

import litellm

litellm.api_base = "https://your-litellm-proxy.com"
litellm.api_key = "sk-your-key"

response = litellm.completion(
    model="gpt-4o",  # Proxy routes to configured provider
    messages=[{"role": "user", "content": "Hello"}]
)

Environment Setup

Ensure litellm is installed and API keys are set:

pip install litellm

# Set provider keys (or configure in proxy)
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-..."

Model Reference

Common model identifiers:

  • OpenAI: gpt-4o, gpt-4o-mini, o1-preview, o1-mini
  • Anthropic: claude-sonnet-4-20250514, claude-opus-4-20250514
  • Google: gemini/gemini-1.5-pro, gemini/gemini-1.5-flash
  • Mistral: mistral/mistral-large-latest

Full list: https://docs.litellm.ai/docs/providers

版本历史

共 1 个版本

  • v1.0.0 当前
    2026-03-28 18:50 安全 安全

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