Llmrouter

**Judgement:** Llmrouter is a well-designed, practical LLM router that fits OpenClaw use cases strongly, especially for users juggling multiple providers and wanting automatic cost/quality tradeoffs. It exposes an OpenAI-compatible API, supports several major providers plus local Ollama, and has explicit OpenClaw compatibility features. **Strengths** - Provides automatic complexity classification and routing to different models and providers. - Strong OpenClaw story: dedicated `--openclaw` mode, correct model name rewriting, and tool name/ID remapping for tool calls. - Flexible classifier backends (local vs. several cloud options) and editable routing logic via `ROUTES.md`. - Good operational guidance: health endpoint, logs, macOS service example, troubleshooting notes. **Key Risks / Limitations** - **Misclassification risk:** The routing quality depends on the classifier and `ROUTES.md`; incorrectly tuned rules can either waste money (over-routing to expensive models) or degrade output quality (under-routing complex tasks). - **Operational burden:** It is a user-managed Python service. You must handle deployment, monitoring, restarts, and upgrades yourself; no built-in HA or scaling strategy is provided. - **Cost visibility:** In OpenClaw, the router is declared with cost `0`, so actual per-provider costs are opaque in the UI and must be monitored externally. - **Security & compliance:** The router can forward traffic to multiple external providers. Correct API key storage, OAuth token handling, and data governance remain the user’s responsibility; the skill does not add extra safety or content filtering. - **Model/config drift:** Example configs include specific model IDs and capabilities. Provider API or model changes can break routing unless you maintain `config.yaml` and related settings. **Recommended Scenarios** - OpenClaw users who want a **single default model** that automatically chooses the right backend model for each task based on complexity. - Teams managing **multi-provider LLM infrastructure** who want centralized routing and cost optimization without reworking each agent/tool. - Workloads with a large mix of trivial and complex queries (e.g., support assistants, mixed chat+coding agents) where cost can be meaningfully reduced by downgrading simple requests. - Users comfortable managing a small Python service (venv, config files, logs) and, optionally, a local Ollama instance for classification.