OpenClaw Multi-LLM Adapter

Universal adapter for multiple LLM providers. Unified interface for OpenAI, Anthropic, Google Gemini, Ollama, and 100+ providers via LiteLLM. Automatic fallb...

Key takeaways and actions before installing: - Expectation mismatch: The documentation promises many providers (LiteLLM, Google Gemini, etc.), but the included code only implements OpenAI, Anthropic, and Ollama. If you need the broader provider support, do not assume it's present — inspect the provider implementations before use. - Secret handling: The tool expects API keys (OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, OLLAMA_HOST). The package metadata did not declare these required env vars — ensure you know which secrets you'll provide and avoid placing them in shared config files without encryption. - Exec/read/write permissions: The SKILL.md requests agent tools read/write/exec. Exec lets the agent run shell commands; only enable this if you trust the skill and run it in a sandbox. Prefer running the CLI manually rather than giving the agent autonomous exec rights. - Dependency installation: requirements.txt will pull packages from PyPI (openai, anthropic, requests). Install dependencies in an isolated virtualenv/container and review package versions. - Audit and test: Review lib/client.py and scripts/main.py locally to confirm no hidden network endpoints or unexpected behavior. If you want higher assurance, run the skill in an isolated environment (container or VM), limit network access, and avoid giving broad host-level permissions. - If you plan to use this skill in production or with sensitive data, ask the provider/maintainer for a complete implementation list, explicit metadata declaring required env vars, and justification for the agent exec permission. If those answers are not satisfactory, treat it as untrusted and run locally in a restricted sandbox.

Type: OpenClaw Skill Name: oc-multi-llm Version: 1.0.0 The skill bundle provides a legitimate universal adapter for multiple LLM providers (OpenAI, Anthropic, Ollama) with support for fallback and model comparison. The code in lib/client.py and scripts/main.py follows standard practices for API integration, including environment variable handling for secrets and structured message processing, with no evidence of malicious intent, data exfiltration, or prompt injection.