alt Hacker NewsNitpick/question: the "LLM" is what you get via raw API call, correct?
If you are using an LLM via a harness like claude.ai, chatgpt.com, Claude Code, Windsurf, Cursor, Excel Claude plug-in, etc... then you are not using an LLM, you are using something more, correct?
An example I keep hearing is "LLMs have no memory/understanding of time so ___" - but, agents have various levels of memory.
I keep trying to explain this in meetings, and in rando comments. If I am not way off-base here, then what should be the term, or terms, be? LLM-based agents?
Replies
You're not off-base at all. The way I think about it:
- LLM = the model itself (stateless, no tools, just text in/text out) - LLM + system prompt + conversation history = chatbot (what most people interact with via ChatGPT, Claude, etc.) - LLM + tools + memory + orchestration = agent (can take actions, persist state, use APIs)
When someone says "LLMs have no memory" they're correct about the raw model, but Claude Code or Cursor are agents - they have context, tool access, and can maintain state across interactions.
The industry seems to be settling on "agentic system" or just "agent" for that last category, and "chatbot" or "assistant" for the middle one. The confusion comes from product names (ChatGPT, Claude) blurring these boundaries - people say "LLM" when they mean the whole stack.
> Nit pick/question: The LLM is what you get via raw API call, correct?
You always need a harness of some kind to interact with an LLM. Normal web APIs (especially for hosted commercial systems) wrapped around LLMs are non-minimal harnesses, that have built in tools, interpretation of tool calls, application of what is exposed in local toolchains as “prompt templates” to transform the context structure in the API call into a prompt (in some cases even supporting managing some of the conversation state that is used to construct the prompt on the backend.)
> If you are using an LLM via a harness like claude.ai, chatgpt.com, Claude Code, Windsurf, Cursor, Excel Claude plug-in, etc... then you are not using an LLM, you are using something more, correct?
You are essentially always using something more than an LLM (unless “you” are the person writing the whole software stack, and the only thing you are consuming is the model weights, or arguably a truly minimal harness that just takes setting and a prompt that is not transformed in any way before tokenization, and returns the result after no transformations or filtering other than mapping back from tokens to text.)
But, yes, if you are using an elaborate frontend of the type you enumerate (whether web or CLI or something else), you are probably using substantially more stuff on top of the LLM than if you are using the providers web API.