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I just launched a newsletter: Agent Engineering It's about what actually breaks when you build AI agents and applications for production, and how to fix it. Not the hype. The engineering. First issue coming this week: I researched how every major framework handles context window overflow. The findings were... not great. Here's a preview: - CrewAI triggers SystemExit when context overflows. Your agent dies. - Google ADK ran compression AFTER the LLM call. The API rejected the request first. - LangChain only added ContextOverflowError in Feb 2026. It's just an exception class; you're on your own. All backed by real GitHub issues. If you're building anything with LLMs, agents, chatbots, AI-powered apps — this is for you. #AI #LLMAgents #AgentEngineering #OpenSource #NucleusIQ Subscribe to Newsletter 👇 👇

Have you ever hit a context window exceeded while running your AI agent? Which framework were you using? How did you handle it? Drop it in the comments. I want to hear your story. Because after researching every major Agent framework, here's what I found: Nobody handles this properly. CrewAI? When context exceeds the limit with respect_context_window=False, it triggers SystemExit. Your production agent doesn't degrade. It dies. → https://lnkd.in/gbSnmP2M A user hit 393,828 tokens on a 128K model. The agent just crashed. → https://lnkd.in/g8hDwaZe Their custom LLM doesn't even call get_context_window_size, so CrewAI has no idea the limit exists. → https://lnkd.in/gDC-3Nwm Google ADK? Their context compression had a critical bug — it ran AFTER the LLM call, not before. By the time compression kicks in, the API has already rejected the request. → https://lnkd.in/gksQQe6u One user asked: "Getting the context too big for the model is such a basic scenario, how come it's not handled by ADK framework?" → https://lnkd.in/gfDNX__V Even worse, when compression runs, agents start hallucinating tools that don't exist. → https://lnkd.in/gi-u8M3X LangChain/LangGraph? They only added a ContextOverflowError exception class in February 2026. Before that, there wasn't even a standard way to detect context overflow. → https://lnkd.in/g9SEcY8X And it's just an error class for detection — their agents don't prevent it or manage context automatically. Their docs literally say: except ContextOverflowError: print("Input too long - reduce context size"). You're on your own. A user hit 142,800 tokens on a 128K model using create_react_agent with a Postgres checkpointer. Maintainer's response? "This is not a langgraph or a checkpointer issue this is just a model context window limitation." ReAct agents return empty responses when context fills up. No error. Just silence. → https://lnkd.in/gGVaksiW → https://lnkd.in/g8Dr94bv LangGraph does not do context management automatically for you. Users must implement pre_model_hook + trim_messages manually. Every user reinvents this. This isn't edge cases. This is the default behaviour of the most popular agent frameworks in 2025-2026. We spent weeks researching this problem and building a proper solution in NucleusIQ v0.7.6. We couldn't find a framework that treated the context window as a managed resource. So we built it: from nucleusiq.agents.context import ContextConfig, ContextStrategy config = AgentConfig( context=ContextConfig(strategy="PROGRESSIVE") ) 3 lines. Automatic. Your agent manages its own context compacts when needed and ensures room for the response. ✈️ pip install nucleusiq nucleusiq-openai # or nucleusiq-gemini ⭐ GitHub: [ https://lnkd.in/dEBabvJ7 ] #AI #Python #OpenSource #NucleusIQ Nucleusbox #AgentFramework