Your Coding Agent Probably Doesn’t Need a Memory SaaS
- ai agents
- developer tools
- agentic coding
I kept seeing SaaS products for coding-agent memory. The continuity I actually needed fit in one small, bounded Markdown file inside the repository.

I started seeing a whole bunch of vibe-coded SaaS solutions for giving coding agents memory between sessions. Some of them are interesting, but I thought there might be a much simpler way. For this particular problem, a full-blown SaaS is not really needed.
At least, not for what I was trying to solve.
I did not need another account, SDK, database, synchronization layer, or service running in the background. I needed an agent to remember what it had already tried, what state the task was in, and what it should do next.
So I made the smallest thing I could think of: a bounded Markdown scratchpad that lives inside the repository.
The problem I was actually trying to solve
Source code preserves implementation state, but it does not preserve all the reasoning around an unfinished task.
After a context reset or a handoff to another agent, the code can show what currently exists. It usually cannot explain:
- Which approaches were already attempted and why they failed.
- Whether an observed result came from the current code or a stale process.
- Which external facts were verified and which were assumptions.
- Which working-tree changes already belonged to the user.
- Which development server or process is currently relevant.
- What the next concrete action should be.
- Which discoveries might eventually deserve permanent documentation.
This missing context causes agents to repeat investigations, retry failed approaches, overwrite existing work, or confidently continue from an assumption that is no longer true.
A conversation transcript can sometimes help, but it is chronological and much larger than the handful of facts required to continue a task. It may also be compacted, reset, unavailable to another agent, or full of details that no longer matter.
What I needed was not long-term semantic memory. I needed reliable task continuity. Just a good handoff note.
The smaller solution
The pattern uses two files:
.ai_scratchpad.mdholds the short-lived state for one active task.AGENTS.mdtells coding agents when and how to maintain it.
The scratchpad is deliberately boring. It is a Markdown file in the root of the repository with four sections and a hard limit of 80 physical lines.
The important part is that it does not try to remember everything. It stores only the information another agent, or the same agent after a context reset, needs to continue the next few actions safely.
Check out the full gist with setup instructions here https://gist.github.com/corpulent/f7fbad0a4d9cdb6a5d4f31d249a2341c.