[GH-ISSUE #22] Feature Request: Intelligent Prompt Routing to Multiple AI Agents #12

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opened 2026-02-27 16:46:45 +03:00 by kerem · 0 comments

kerem commented

2026-02-27 16:46:45 +03:00

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Originally created by @benzntech on GitHub (Nov 29, 2025).
Original GitHub issue: https://github.com/router-for-me/EasyCLI/issues/22

Feature Request: Intelligent Prompt Routing

Overview

Implement an intelligent prompt routing system similar to Archgw (https://github.com/katanemo/archgw) that automatically routes user prompts to the most appropriate AI agent/model based on the request content and complexity.

Problem Statement

Currently, users must manually select which AI provider/model to use for their requests. This creates friction and may lead to suboptimal results when users choose inappropriate models for their specific tasks.

Proposed Solution

Implement a multi-tier routing system inspired by Archgw's architecture:

1. Model-based Routing

Direct assignment to specific LLM providers based on simple rules
Example: Route code generation to Sonnet, route quick queries to Haiku

2. Alias-based Routing

Semantic model names decoupled from provider specifics
Example: fast-model → Claude Haiku, balanced-model → GPT-4o, powerful-model → Sonnet

3. Preference-aligned Routing (Advanced)

Use a lightweight classification model to analyze prompt intent
Route based on natural language descriptions of agent capabilities
Target: <100ms routing decision time

Key Features from Archgw Architecture

Function-calling architecture with specialized LLMs for agent routing
Lightweight autoregressive models to map requests to appropriate agents
Configuration-driven routing policies outside application code
Multi-provider support with unified interface

Benefits

For Users:

Reduced cognitive load (no need to choose models manually)
Better results through optimal model selection
Faster responses for simple queries
More powerful processing for complex tasks

For the Application:

Cost optimization (use cheaper models when appropriate)
Better resource utilization
Enhanced user experience
Competitive differentiation

References

Archgw GitHub: https://github.com/katanemo/archgw
Routing time: <100ms with purpose-built LLMs
Multi-provider support: 11+ LLM providers

Originally created by @benzntech on GitHub (Nov 29, 2025). Original GitHub issue: https://github.com/router-for-me/EasyCLI/issues/22 ## Feature Request: Intelligent Prompt Routing ### Overview Implement an intelligent prompt routing system similar to Archgw (https://github.com/katanemo/archgw) that automatically routes user prompts to the most appropriate AI agent/model based on the request content and complexity. ### Problem Statement Currently, users must manually select which AI provider/model to use for their requests. This creates friction and may lead to suboptimal results when users choose inappropriate models for their specific tasks. ### Proposed Solution Implement a multi-tier routing system inspired by Archgw's architecture: **1. Model-based Routing** - Direct assignment to specific LLM providers based on simple rules - Example: Route code generation to Sonnet, route quick queries to Haiku **2. Alias-based Routing** - Semantic model names decoupled from provider specifics - Example: fast-model → Claude Haiku, balanced-model → GPT-4o, powerful-model → Sonnet **3. Preference-aligned Routing (Advanced)** - Use a lightweight classification model to analyze prompt intent - Route based on natural language descriptions of agent capabilities - Target: <100ms routing decision time ### Key Features from Archgw Architecture - Function-calling architecture with specialized LLMs for agent routing - Lightweight autoregressive models to map requests to appropriate agents - Configuration-driven routing policies outside application code - Multi-provider support with unified interface ### Benefits **For Users:** - Reduced cognitive load (no need to choose models manually) - Better results through optimal model selection - Faster responses for simple queries - More powerful processing for complex tasks **For the Application:** - Cost optimization (use cheaper models when appropriate) - Better resource utilization - Enhanced user experience - Competitive differentiation ### References - Archgw GitHub: https://github.com/katanemo/archgw - Routing time: <100ms with purpose-built LLMs - Multi-provider support: 11+ LLM providers