Thanks for sharing this. At a time where this is a rush towards multi-agent systems, this is helpful to see how an LLM-first organization is going after it. Lots of the design aspects here are things I experiment with day to day so it's good to see others use it as well

A few takeaways for me from this (1) Long prompts are good - and don't forget basic things like explaining in the prompt what the tool is, how to help the user, etc (2) Tool calling is basic af; you need more context (when to use, when not to use, etc) (3) Using messages as the state of the memory for the system is OK; i've thought about fancy ways (e.g., persisting dataframes, parsing variables between steps, etc, but seems like as context windows grow, messages should be ok)

▲

chazeon 3 days ago | parent | next [-]

I want to note that: long prompts are good only if the model is optimized for it. I have tried to swap the underlying model for Claude Code. Most local models, even those claimed to work with long context and tool use, don't work well when instruction becomes too long. This has become an issue for tool use, where tool use works well in small ChatBot-type conversation demos, but when Claude's code-level prompt length increases, it just fails, either forgetting what tools are there, forgetting to use them, or returning in the wrong formats. Only the model by OpenAI, Google's Gemini, kind of works, but not as well as Anthropic's own models. Besides they feel much slower.

▲

nuwandavek 4 days ago | parent | prev [-]

(author of the blogpost here) Yeah, you can extract a LOT of performance from the basics and don't have to do any complicated setup for ~99% of use cases. Keep the loop simple, have clear tools (it is ok if tools overlap in function). Clarity and simplicity >>> everything else.

▲

samuelstros 4 days ago | parent [-]

does a framework like vercel's ai sdk help, or is handling the loop + tool calling so straightforward that a framework is overcomplicating things?

for context, i want to build a claude code like agent in a WYSIWYG markdown app. that's how i stumbled on your blog post :)

	▲	ahmedhawas123 3 days ago \| parent \| next [-]
		Function / tool calling is actually super simple. I'd honestly recommend either doing it through a single LLM provider (e.g., OpenAI or Gemini) without a hard framework first, and then moving to one of the simpler frameworks if you feel the need to (e.g., LangChain). Frameworks like LangGraph and others can get really complicated really quickly.
	▲	brabel 3 days ago \| parent \| prev \| next [-]
		Check the OpenAI REST API reference. Most engines implement that and you can see how tool calls work. It’s just a matter of understanding the responses they give you, how to put them in the messages history and how to invoke a tool when the LLM asks for it.
	▲	nuwandavek 3 days ago \| parent \| prev [-]
		There may be other reasons to use ai sdk, but I'd highly recommend starting with a simple loop + port most relevant tools from Claude Code before using any framework. Nice, do share a link, would love to check out your agent!