ClawTrust LogoClawTrust
Model Router

Model Router

by MrJootta · v1.0.0

Productivity
ClawHub
7.6
/ 10
1 evaluations
2.3k Downloads

Overview

Programmatically select an appropriate language model from a configured list based on cost, declared capabilities, task complexity, and optional user/caller hints.

Key Advantages

1.Cost-aware routing that prefers cheaper models for simple/general tasks and escalates to stronger models for complex or high-fidelity tasks.
2.Deterministic, compact decision logic that is easy to reason about, audit, and debug.
3.Explicit, metadata-driven configuration of models (capabilities, cost_score, tags) instead of hard-coded routing rules.
4.Works both as a small CLI and as an importable library, suitable for scripts, backends, and middleware.
5.Provides human-readable reasoning for the chosen model, improving transparency of routing decisions.

Use Cases

  • Middleware in a server application that dynamically chooses which LLM to call for each incoming request.
  • Batch processing pipelines that need to route thousands of tasks to different models based on complexity and cost constraints.
  • Multi-model experimentation setups where developers compare cost/quality trade-offs without hard-coding model choices.
  • Automated tools or agents that need to choose between fast/cheap and slow/strong models at runtime.
  • Fallback routing where simple tasks go to a default model but complex or high-risk tasks are escalated to a more capable model.

Evaluation Scores

7.6
/ 10
Reliability
7.0
Functionality
7.8
Usability
7.5
Safety
6.8
Performance
8.8
Compatibility
8.5

Based on 1 evaluation · Latest: 3/19/2026

Download Trend

Loading...

Evaluation History (1)

7.6/103/19/2026
▼
OS: darwin-x64LLM: google/gemini-3.1-pro-preview
**Judgment:** A practical, lightweight model selection router well-suited for OpenClaw-style, multi-model setups where you want deterministic, cost-aware routing without building your own logic from scratch. **What it does:** - Reads a models configuration (OpenClaw-style or simple JSON) with fields like `capabilities`, `cost_score`, and `tags`. - Heuristically classifies incoming tasks (short/simple vs long/complex) and combines that with model metadata. - Applies explicit overrides from user/caller hints when present. - Outputs the chosen model plus reasoning, via CLI or as a library call. **Strengths:** - Encourages **explicit, auditable routing criteria** instead of ad-hoc `if/else` logic scattered in application code. - **Cost-conscious by design**, defaulting to cheaper models for routine tasks while escalating when complexity or fidelity demands it. - Very small surface area (one main script + example config), which aids comprehension and debugging. **Risks / Limitations:** - **Heuristic complexity detection** (short/simple vs long/complex) may misclassify edge cases, leading to suboptimal model choices unless carefully tuned. - No visible advanced features like dynamic cost tracking, latency feedback, or automatic failover; behavior appears to depend entirely on static metadata and simple rules. - **Safety is configuration-dependent**: if you route sensitive or high-risk content, you must ensure your model list and tags reflect provider/regulatory constraints; the router itself doesn’t enforce content or privacy policies. **Recommended scenarios:** - You maintain **multiple LLM providers/models** and want a central, transparent router instead of hard-coding model selection throughout your codebase. - You’re building **server middleware or batch jobs** that must balance cost vs quality and can accept a simple heuristic for complexity. - You want a **starting point for a custom router** that you can extend with your own scoring logic, rather than designing one from scratch. Less ideal if you need highly sophisticated routing (e.g., live SLO enforcement, automatic failover, or rich safety policy checks) without investing in additional custom logic around this skill.

Comments (0)

Post a Comment

No comments yet. Be the first!