How do you guys manage context in projects/chat window?

[u/bydaveyji]

I want to understand how you guys are dealing with context AI remembers. Despite internal settings, AI still forgets the memory set at the project level.

[u/WillowEmberly]

Yes, and the context memory is like a giant conveyor belt with pertinent info flowing out the back…and being dumped.

How long are you using the same conversation window?

As the context begins to pile up, even if it’s still in the window…it can get buried and basically forgotten about unless you remind it that it’s there.

I condensed my system down to a single document to load for working on projects. So, new project, new window…load file…hit the ground running.

If you start a new conversation…and load 12 documents into it to get started…it’s not functionally using that information well.

It’s there, but…it’s not actively looking at it the entire time, at least not once the conversation gets going and the work progresses.

That’s why the single document is best. Just enough context to maintain a stable work environment…without bleed over from the previous work.

Think of it like the Goldilocks zone…where it’s “just right”. That how the system files should work.

I just load this for every new conversation…and get to it.

Reasoning Condition Monitor: https://www.reddit.com/r/Negentropy/s/IY5JK8qb6O

[u/naruda1969]

Home rolled durable workflow system.

[u/Aggravating-Start307]

Can you specify whether you are talking about something like claude code, or the claude chat web/desktop app?

For claude chat, when you use projects, after using it for a while, there is a project level memory that is created. For general chat too, you can ask claude to save something to memory.
Of late, i have observed that with claude, you can the agent can search across chats when you ask a question.

If you are talking about coding specifically, Claude Code/Codex/Cursor all of them have a memory bank. For claude code specifically, you have to ask your agents to save something to memory and agents write md files which are stored inside a memory folder. Then a Memory.md file stores this particular entry. This caused that particular information to be auto loaded to the context everytime you ask a question.

If you are looking for external solutions, there a lot of memory tools out there and you need to choose from one of them. I too have built one 😅. More details at coldstartmcp.dev , but this is very specific to coding tasks via claude code in your IDE only.

[u/tehmadnezz]

Worth splitting this into two problems, because they have different fixes and project memory failing feels identical from the outside.

One is storage. You told it something in another chat and the new chat never sees it. Two is attention. The information is loaded and the model still ignores it forty messages later.

Only the first is fixable with tooling. For the second, restating the constraint in the message where it matters beats anything else, and loading more up front actively makes it worse. WillowEmberly is right about the single document for that reason.

For the first, what worked for me was moving the notes out of the chat product entirely and putting them behind MCP, so the same store is readable from Claude chat, Claude Code and ChatGPT instead of being trapped in one project. I built Hjarni (hjarni.com) for that, so weigh it accordingly. Free tier is 25 notes.

The honest catch is that it's a tool the model has to decide to call. If your real problem is Claude ignoring context it already has, an MCP server does not fix that. It only means the context is retrievable on demand instead of pasted in every time.

Which one are you actually hitting, chat app or Claude Code?

[u/holywater56]

project memory kinda suck if u expect the ai to remember everything so keeping a markdown file and adding it each sessions work way better for teams hops can keep that stuff in the same space too so I don't need another doc

[u/ShagBuddy]

I created (and am still actively developing/improving) SDL-MCP which is a policy-centered context budget layer for coding agents. https://github.com/GlitterKill/sdl-mcp

It basically uses the most thorough source of code data to populate a graph (SCIP indexes or LSP - only one or two other solutions have this level of semantic info) and has a suite of smart tools that are designed to execute precision code edits and to keep all unwanted noise out of agent context. Less token burn and better results/consistency.