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Most AI agents are static they lock into one expensive model (like GPT-4) for everything, burning budget on simple "hello" message.
I built a Runtime Router using LangChain Middlewares to solve this. It dynamically switches models based on the complexity of the user's query without breaking conversation state.
The Architecture:
Simple Facts => Routes to GPT-4 Nano
Reasoning => Routes to GPT-4 Mini
Complex Code/Analysis => Routes to GPT-4 Standard
The Stack:
LangChain Middleware: Intercepts the chat context to analyze token count + complexity.
MongoDB Vector Search: Long-term memory retrieval.
Checkpointers: Persists the graph state (via thread_id) so the agent can switch "brains"
The Result: A truly adaptive agent that matches computational cost to actual query complexity in real-time.
How are you handling large context windows?
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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