agent-memory-atlas AGENTS.md

agent-memory-atlas AGENTS.md is an instructions file for Codex, OpenCode from neoneye/agent-memory-atlas. It costs 1,758 tokens per session, scanned A, original, MIT.

Repository instructions for agent-memory-atlas, a collection of research reports and design patterns about systems that help software agents remember information.

In plain words
What is it for?
Choosing a memory design, writing an implementation brief, extending the atlas, and following its documented workflow and tests.
Why use it?
They direct contributors to the right parts of the repository and set limits on how to use its research when designing another product.

Instructions file for CodexOpenCode

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add instructions/neoneye/agent-memory-atlas/agents-md
Clone the repo
git clone --depth 1 https://github.com/neoneye/agent-memory-atlas

Made for: Codex, OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for agent-memory-atlas AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/neoneye/agent-memory-atlas/agents-md.svg)](https://agentmods.dev/instructions/neoneye/agent-memory-atlas/agents-md)
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<a href="https://agentmods.dev/instructions/neoneye/agent-memory-atlas/agents-md"><img src="https://agentmods.dev/badge/instructions/neoneye/agent-memory-atlas/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 1,758 This file is loaded in full into every session.
When invoked 1,758 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.01758 $0.01758
Opus 5 $0.00879 $0.00879
Sonnet 5 $0.00352 $0.00352
Haiku 4.5 $0.00176 $0.00176

Measured yesterday against content hash 13f861202eaa, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agent-memory-atlas AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

AGENTS.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Working in this repository

A code-grounded field guide to agent memory systems: 357 reports, each read at a pinned commit, plus 21 design patterns extracted from them.

There are two jobs here, and they use different parts of the repo.


Job 1 — You are designing or building memory for some other product

Start with the use-the-atlas skill (.agents/skills/use-the-atlas/). It is this section as a workflow: read the target repository, pick a profile, write a build brief, stop for approval, implement in an order where each stage stands alone, then run the tests by id. The rest of this section is what it is built on.

Do not read the reports. There are hundreds and reading widely is how an agent ends up recommending the most interesting mechanism instead of the smallest sufficient one. Read five things, in this order.

  1. content/patterns/index.md, the section How to use the library. It is a list of failure modes, each pointing at the pattern that closes it. Start from the failure the product cannot tolerate, not from the mechanism that sounds most rigorous.

  2. The Stacks, by what you are building table on the same page. Five rows — single-user tool, multi-tenant, companion/roleplay, autonomous actor, and memory that must be correctable and defensible — each naming the failure that actually hurts for that shape. Pick the row, then read the What you can defer paragraph under it, which is the part that keeps the build small.

  3. The pattern pages you selected. Each carries Cost to adopt, Tradeoffs, Implementation checklist, Tests to require, and Seen in the atlas. The last one names the systems worth reading — that is your entry point into the corpus, and the only one you need.

  4. content/overview.md §8 What I Would Build and §10 Practical Checklist. §8 gives a build order in which each stage works on its own; vector search and model-based extraction come last, deliberately.

Read the full file on GitHub · 138 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday Changed · +4 lines · +69 tokens per session 13f861202eaa
  2. 5d ago First seen · 134 lines · 1,689 tokens per session scan A 1dee1ab96fb6

Subscribe to this mod's changes

agent-memory-atlas AGENTS.md is an instructions file published in the GitHub repository neoneye/agent-memory-atlas (61 stars, last pushed today), licensed MIT. It adds 1,758 tokens to every session, about $0.0088 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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