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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.

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Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
Import or place the package into your OpenClaw setup.

Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, scripts/llm_call.py

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Trust & source

Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: ishaan-jaff
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 8 sections Open source page

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)

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

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

1 Docs1 Scripts

SKILL.md Primary doc
scripts/llm_call.py Scripts