litellm
✓Verified·Scanned 2/18/2026
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.
from clawhub.ai·v0cb58f3·5.1 KB·0 installs
Scanned from 1.0.0 at 0cb58f3 · Transparency log ↗
$ vett add clawhub.ai/ishaan-jaff/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