“I Removed an LLM Call From My AI Agent — LLM Router vs Jev”

What happens when you use an LLM to route requests between specialized AI agents—and what changes when you replace that routing step with Jev? In this video, I build the *same multi-agent routing workflow in two different ways* and inspect both implementations using **LangSmith**. 🔹 Architecture 1 — LLM Router The user query is first sent to an LLM, which decides which specialized agent should handle the request: *User Query → LLM Router → Agent → LLM → Response* The routing LLM call adds an additional model invocation before the actual agent starts. 🔹 Architecture 2 — Jev Router I then replace the LLM-based routing mechanism with Jev: *User Query → Jev → Agent → LLM → Response* The downstream agents and their LLM calls remain the same. The key difference is that the routing decision itself does not require an additional LLM inference call. 🔍 What I Compare in LangSmith Throughout the video, I inspect the actual execution traces and compare: LLM calls Routing latency Token usage LLM cost Agent execution Overall workflow latency Execution traces in LangSmith The goal is not simply to say that one approach is "better," but to **look at what is actually happening inside the agent workflow**. This is especially important when designing production agentic systems, where unnecessary model calls can contribute to additional latency, token usage, and inference cost. 🧠 Key Concept The important distinction is: *LLM-based routing* ```text User ↓ LLM Router ↓ Specialized Agent ↓ LLM ↓ Response ``` *Jev-based routing* ```text User ↓ Jev ↓ Specialized Agent ↓ LLM ↓ Response ``` The objective is to demonstrate how the routing layer itself can be separated from the LLM inference layer and how observability tools like LangSmith can help us understand the difference. 🛠️ Technologies Used Python LangChain LangGraph Jev LangSmith LLMs Multi-Agent Systems Agentic AI LLM Observability If you're learning **Agentic AI, LangGraph, LangChain, or LLMOps**, this experiment provides a practical way to understand what happens inside a router-based agent architecture. #AgenticAI #LangGraph #LangChain #LangSmith #LLM #GenAI #AIEngineering #MultiAgentSystems #LLMOps #Jev #Python #ArtificialIntelligence   / rishab-raj-   https://github.com/RishOpss