Mneno AGENTS.md

Mneno AGENTS.md is an instructions file for Codex, OpenCode from Mneno-AI/Mneno. It costs 3,120 tokens per session, scanned A, original, Apache-2.0.

Repository instructions for Mneno, a Python SDK that helps AI applications keep compact, explainable, and verifiable memory over time.

In plain words
What is it for?
Use them when developing Mneno's memory scoring, compaction, conflict handling, retrieval, storage, provider, observability, or evaluation code.
Why use it?
They explain the project's purpose, code layout, development setup, and ways to build, check, and test changes.

Instructions file for CodexOpenCode

Written for Codex and OpenCode: the file is AGENTS.md. Also seen: mentions AGENTS.md.

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/mneno-ai/mneno/agents-md
Clone the repo
git clone --depth 1 https://github.com/Mneno-AI/Mneno

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 Mneno AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/mneno-ai/mneno/agents-md.svg)](https://agentmods.dev/instructions/mneno-ai/mneno/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/mneno-ai/mneno/agents-md"><img src="https://agentmods.dev/badge/instructions/mneno-ai/mneno/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 3,120 This file is loaded in full into every session.
When invoked 3,120 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 5.1 $0.03120 $0.03120
Opus 5 $0.01560 $0.01560
Sonnet 5 $0.00624 $0.00624
Haiku 4.5 $0.00312 $0.00312

Measured 5d ago against content hash 87734113d652, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

Mneno 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 5d ago.

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 · 343 lines

How it starts

The opening of the file, as written. The whole thing — 343 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md

Project Overview

Mneno is a Python-first SDK for AI memory. It is an anti-context-rot memory runtime for copilots, support agents, internal AI tools, and long-running AI applications.

Mneno should maintain useful, compact, explainable, and verifiable context over time. It is not a generic memory store and should not become a thin wrapper around provider APIs.

Repository Structure

  • mneno/: core Python package.
  • mneno/scoring/: memory scoring interfaces and temporal scoring.
  • mneno/compaction/: explainable compaction interfaces and diff templates.
  • mneno/conflicts/: deterministic conflict detection, reports, and safe resolution policies.
  • mneno/hierarchy/: memory layer definitions, retention policies, and lifecycle transition management.
  • mneno/observability/: local trace models, in-memory recorder, and inspector utilities.
  • mneno/evaluation/: deterministic metrics, benchmark adapters, operation reports, and stable export helpers.
  • mneno/retrieval/: retrieval interfaces.
  • mneno/storage/: storage interfaces and local in-memory storage.
  • mneno/providers/: provider protocols, dummy local providers, and provider registry.
  • mneno/policies/: runtime policy configuration.
  • tests/: pytest suite.
  • examples/: runnable usage examples.
  • scripts/: development, formatting, and test commands.
  • docs/: project documentation.

Development Setup

Use Python 3.11 or newer.

scripts/setup_dev.sh

The setup script uses uv when available and falls back to pip with a local virtual environment.

Build, Lint, and Test Commands

scripts/format.sh
scripts/check.sh
scripts/test.sh
python -m build

scripts/check.sh must pass before a pull request is ready.

Core APIs

The main public entrypoint is:

from mneno import MemoryClient

The initial client supports local in-memory add and search operations. Public APIs must be documented and covered by tests when they are added or changed.

Read the full file on GitHub · 343 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. 5d ago First seen · 343 lines · 3,120 tokens per session scan A 87734113d652

Subscribe to this mod's changes

Mneno AGENTS.md is an instructions file published in the GitHub repository Mneno-AI/Mneno (2 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 3,120 tokens to every session, about $0.0156 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 2026-08-31.