How to give your coding agents product memory with PaellaDoc

TLDR: Agents execute very well and forget between sessions: Tuesday’s decision, the why of an architecture, the acceptance criterion. The instructions file solves technical context and falls short on product context. PaellaDoc attacks that gap as a local factory on your Mac: discovery, specifications, execution with your agents (Claude, Codex, Gemini) and validation connected with persistent memory. Free for individual use.

This guide is for anyone building products with agents who has lived the symptom: the third session explaining the same thing, the agent undoing last week’s decision because nobody told it, the long project where context lives in your head and forty lost conversations.

It is the underlying problem of working with agents, and it has a ladder of solutions this guide climbs whole.

1. Recognize the problem: agents forget

Coding agents have session memory: within a conversation they keep the thread, and closing it kills the context. For loose tasks that is irrelevant. For building products it is the dominant cost: every new session starts by rebuilding (pasting files, repeating decisions, retelling the why), and what does not get rebuilt gets lost, with the agent making decisions that contradict the ones already made.

The economic symptom is measurable: in long projects, the fraction of each session spent re-establishing context grows until it eats the saving the agent brought. And the quality symptom is worse: implicit product decisions (why this is the way it is) do not travel, and the code starts contradicting the product.

2. Exhaust the simple route first: the instructions file

Before adding tools, squeeze what the base system already gives. The repository’s instructions file (from the first project with agents guide) solves technical context excellently: conventions, commands, architecture, forbidden zones. Every serious agent reads it on start, and keeping it alive is the best effort-to-result practice there is.

Its limit lies in the nature of what it stores: it is a static document of how we work here, not a memory of what we are building and what has been decided. Specifications, project state, evidence of what was validated: none of that reasonably fits in a growing text file.

When you notice the instructions file running small for the product (not the technique), that is the signal to climb a rung.

3. Build the factory with PaellaDoc

PaellaDoc attacks exactly that gap: a local Mac application keeping the full cycle connected as one system with memory. Discovery (what is being built, for whom, why) gets captured once. Specifications derive from it. Planning slices the work. And execution is done by your usual agents (Claude Code, Codex, Gemini) working inside that structure, with every task inheriting the product’s full context.

The practical change shows in the second session: you never explain the product again. And in the second week: the agent executing today respects Tuesday’s decision because the decision lives in the system, not in a closed conversation.

Everything runs locally (data, repositories and evidence on your Mac), it is free for individual use up to three projects, and it does not replace your agent subscriptions: it orchestrates them. Your factory’s cost remains what you already pay for your models.

4. Work in cycles with gates and evidence

The factory’s structure imposes a rhythm that turns out to be the healthy one: every piece of work passes through specification (what will be done and how you will know it is right), execution (the agent works with inherited context) and validation (the gates check the result before accepting it, leaving evidence).

That gated cycle is what turns delegating into trusting: the agent does not advance without passing the checks, and every step leaves a verifiable trail. It is the systematized version of the discipline the reviewing AI code guide describes by hand.

And it has a valuable side effect: with durable context and validation, small models perform above expectations, because they work a bounded problem instead of guessing. The factory does not just remember: it makes things cheaper.

For pure parallelism without the product layer, Conductor remains the light piece, and both coexist on the same Mac. The rest of the sector lives in AI for software development.

Common mistakes

Rebuilding context by hand every session. It is the silent tax of working with agents, and normalizing it is expensive: this guide’s ladder exists so you stop paying it.

Stuffing the whole product into the instructions file. The file grows until nobody maintains it and the agent skims it. Technical context to the file, product to a system with memory.

Jumping to the factory without the base habit. PaellaDoc structures what you already do: without the habit of specifying and reviewing, the structure feels like a corset instead of help.

Ignoring the evidence. Validation gates leave a trail of what was checked: not looking at it is returning to the blind trust the system came to replace.

Frequently asked questions

Does PaellaDoc replace Claude Code?

No: it orchestrates it. The factory supplies memory, specifications and validation, and your usual agents execute inside. You need your model subscriptions or keys.

What do I gain over a well-kept instructions file?

The file stores how you work. The factory also stores what you are building, what was decided and what was validated, with every task inheriting that context. For long product projects, they are different layers of the same problem.

Why local?

Because product context is the most sensitive thing you own: decisions, plans and evidence. In PaellaDoc it all lives on your Mac, and what travels to the models is what each task needs. It is the exact counterpoint of the cloud factories.

Is it really free?

For individual use, up to three projects and no account, with the declared promise that personal use stays free. The core is source-available and the extension SDK is open.