← Back to Skills Marketplace
ishaan-jaff

LiteLLM

by ishaan-jaff · GitHub ↗ · v1.0.0
cross-platform ⚠ suspicious
1895
Downloads
1
Stars
11
Active Installs
1
Versions
Install in OpenClaw
/install litellm
Description
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.
README (SKILL.md)

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

Usage Guidance
This skill is coherent with its stated purpose (calling many LLMs), but take these precautions before installing: - Verify the litellm package source/version on PyPI or the project's official docs before running 'pip install'. Prefer pinned versions or vetted packages. - Expect to provide API keys (OpenAI, Anthropic, or a LiteLLM proxy key). The registry omitted these declarations — confirm which keys you will supply and where they are stored. - Be aware that any prompt you send may be transmitted to third-party providers. Do not send sensitive secrets, credentials, or private data to this skill unless you trust the destination. - Consider deploying and pointing the skill to a trusted LiteLLM proxy (as suggested) to centralize auditing, caching, and to avoid sprinkling provider keys across environments. - If you need higher assurance, ask the publisher for: an authoritative homepage or repo, package signing or checksum, and explicit declared required env vars in the registry metadata.
Capability Analysis
Type: OpenClaw Skill Name: litellm Version: 1.0.0 The skill bundle is benign. The `SKILL.md` provides clear instructions and examples for using the LiteLLM library to interact with various LLMs, without any prompt injection attempts or malicious directives for the agent. The `scripts/llm_call.py` script is a straightforward command-line utility that wraps the LiteLLM library, reading standard environment variables (`LITELLM_API_BASE`, `LITELLM_API_KEY`) for configuration, which is expected behavior for an LLM client. There is no evidence of data exfiltration, malicious execution, persistence, or obfuscation.
Capability Assessment
Purpose & Capability
The skill's code and SKILL.md implement a generic LiteLLM client for calling many LLM providers, which is coherent with the name/description. However, the registry metadata declares no required environment variables or primary credential while the documentation and code clearly expect API keys (e.g., LITELLM_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY or a proxy key). This is an inconsistency (likely sloppy metadata) but not itself evidence of malice.
Instruction Scope
Runtime instructions and the included script stay within the defined purpose: build messages and call litellm.completion (optionally via a proxy). The instructions do not ask the agent to read unrelated files or system secrets. They do instruct users to set provider API keys and an optional proxy endpoint, which will cause prompts to be transmitted to external LLM providers (expected for this skill).
Install Mechanism
There is no formal install spec in the registry (instruction-only), and SKILL.md suggests 'pip install litellm' with no version or source verification. Installing an unpinned package from PyPI is common but increases risk; you should verify the package origin and integrity (official project, expected maintainer) before installing.
Credentials
Although provider API keys are appropriate for a multi-provider LLM caller, the registry metadata lists no required env vars while SKILL.md and the script reference several sensitive variables (LITELLM_API_BASE, LITELLM_API_KEY, OPENAI_API_KEY, ANTHROPIC_API_KEY). This mismatch means the skill may access or require sensitive credentials that weren't declared up front. Also note that any prompts and related data will be sent to external services tied to those keys — consider data-sensitivity implications.
Persistence & Privilege
The skill is not always-enabled, doesn't request elevated or persistent system privileges, and does not modify other skills' configs. Autonomous invocation is allowed by default but is not combined here with other high-risk properties.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install litellm
  3. After installation, invoke the skill by name or use /litellm
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release - call 100+ LLMs via unified API
Metadata
Slug litellm
Version 1.0.0
License
All-time Installs 12
Active Installs 11
Total Versions 1
Frequently Asked Questions

What is 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. It is an AI Agent Skill for Claude Code / OpenClaw, with 1895 downloads so far.

How do I install LiteLLM?

Run "/install litellm" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is LiteLLM free?

Yes, LiteLLM is completely free (open-source). You can download, install and use it at no cost.

Which platforms does LiteLLM support?

LiteLLM is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created LiteLLM?

It is built and maintained by ishaan-jaff (@ishaan-jaff); the current version is v1.0.0.

💬 Comments