I wish there was a clean way to compact the conversation into a prompt with all necessary context for a new fresh conversation.

▲

simonw 23 minutes ago | parent | next [-]

I'm fed up with compaction. I want my agent to get compacted but also retain full access to the prior conversation via search and tool calls - I want it to know "the requirements for X were discussed in detail previously in conversation C51E31CE-C985-4633-A749-DCC9805A7FEB" and have a tool that lets it dispatch a subagent to find those details again.

Do any of the coding agents have this already?

▲

wccrawford 40 minutes ago | parent | prev | next [-]

Have you tried Matt Pocock's "handoff" skill?

	▲	leobg 15 minutes ago \| parent [-]
		https://github.com/mattpocock/skills/blob/main/skills/produc... > Write a handoff document summarising the current conversation so a fresh agent can continue the work. > […]

▲

4b11b4 3 hours ago | parent | prev [-]

There is, but your wish of "clean" is ambiguous.

	▲	Alifatisk an hour ago \| parent \| next [-]
		With clean I mean the opposite of how I currently do it, which is by asking the model to compact the whole thread into a prompt which will act as context for next model. My way of prompting this varies and every time I receive the blob of output, I can’t fell how well it managed to capture the necessary details. This way feels lika a dirty way to transfer knowledge from one conversation to another. You said there is? What’s the options?
	▲	anonthrownaway 2 hours ago \| parent \| prev [-]
		Here's a view for "clean:" 1. Every chat should have a context used/remaining measurement so you know when you have to ditch the current chat for a fresh one. 2. Every chat should analyze and categorizes each element of context by how useful it is towards the overarching goal of the chat. 3. Every chat has a handoff button with a "usefulness" slider (say 1-5) that shows the total size of the context based on its setting. 4. The handoff automatically creates a new chat with the desired amount of context and a prompt to get it back to where you were. That said, I am newb and so there is some reason why these non-deterministic LLMs can't do this :-/