May 17
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Stop burning your budget on runaway AI agents! 🚀 In this video, we dive deep into a critical middleware for LangChain and LangGraph developers: Model Call Limits. When building AI agents that use multiple tools, it’s easy for a model to get stuck in an infinite loop or for a user session to rack up unnecessary costs. We explore how to implement guardrails using three key properties: Thread Limit: Restrict the total number of model calls across an entire conversation thread. Run Limit: Control the maximum number of internal model calls allowed for a single request (perfect for preventing tool-calling loops). Exit Behavior: Choose between a graceful "End" (returning the current state) or an "Error" (triggering an exception). What’s inside this tutorial: ✅ Why you need model call limits for system stability. ✅ Setting up the Middleware in your notebook. ✅ Real-world demos: Testing thread_limit with simple math queries. ✅ Preventing tool-looping: Testing run_limit with a multi-country weather tool scenario. ✅ Comparing "End" vs "Error" exit behaviors. By the end of this video, you'll know how to protect your API keys and ensure your AI applications remain stable and cost-effective. Tools Used: LangChain / LangGraph Python Notebooks Google Antigravity IDE Timestamps (Suggested) 0:00 - Introduction to Model Call Limits 0:45 - Scenario 1: Managing Long Threads & Costs 1:20 - Scenario 2: Preventing Infinite Tool Loops 2:10 - Middleware Properties: Thread Limit, Run Limit, & Exit Behavior 3:45 - Code Walkthrough: Implementing Thread Limits 5:30 - Testing "End" vs "Error" Exit Behaviors 7:15 - Deep Dive: Using Run Limits with Tools (Weather Example) 9:40 - Summary & Best Practices Github: https://github.com/NajiAboo/langchain-v1/blob/main/01-middleware/model_call_limit.ipynb LangChain, LangGraph, AI Agents, LLM Middleware, Model Call Limits, AI Cost Optimization, Python AI Tutorial, LangChain Tutorial, Prevent Infinite Loops AI, LangGraph Middleware, Agentic Workflows, AI Development, Tech Tutorial, LangChain Tool Calling, LLM Guardrails, Vertex AI, n8n Automation, Data Science Tips