LiteLLM
/install litellm
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
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install litellm - 安装完成后,直接呼叫该 Skill 的名称或使用
/litellm触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
LiteLLM 是什么?
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. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 1895 次。
如何安装 LiteLLM?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install litellm」即可一键安装,无需额外配置。
LiteLLM 是免费的吗?
是的,LiteLLM 完全免费(开源免费),可自由下载、安装和使用。
LiteLLM 支持哪些平台?
LiteLLM 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 LiteLLM?
由 ishaan-jaff(@ishaan-jaff)开发并维护,当前版本 v1.0.0。