agentic-memory AGENTS.md

Project instructions for an AI memory system that stores and retrieves relevant information for coding agents.

In plain words
What is it for?
Use them when working in the agentic-memory project to load context, store memories through its approved tools, and maintain its generated instruction files.
Why use it?
They prevent agents from bypassing the memory system or writing generated memory files in unsafe ways.

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/elvirafa/agentic-memory/agents-md
Clone the repo
git clone --depth 1 https://github.com/elViRafa/agentic-memory

Made for: Codex, OpenCode.

Per session 2,094 This file is loaded in full into every session.
When invoked 2,094 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 $0.02094 $0.02094
Opus 5 $0.01047 $0.01047
Sonnet 5 $0.00419 $0.00419
Haiku 4.5 $0.00209 $0.00209

Measured yesterday against content hash 8ba9ba039ebc, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agentic-memory 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 · 172 lines

How it starts

The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Instructions — Memory Fabric

This file is read automatically by Claude Code, Gemini CLI, Codex, Antigravity, and other MCP-aware AI agents. GitHub Copilot reads .github/copilot-instructions.md instead.


Memory Fabric — Semantic Store Agent Instructions

🚨 CRITICAL RULES - READ FIRST 🚨

  1. NEVER use the native VS Code Copilot memory tool. You MUST ONLY use the memory-fabric MCP tools (like write_memory_store_tool). The native memory tool writes to VS Code workspace storage, bypassing this project's memory system.
  2. NEVER use raw file system tools (like create_file, write_to_file, bash, etc.) to read or write files inside the .ai-memory/ directory. Doing so bypasses secret scanning, token budgeting, and the Dreaming system.
  3. MANDATORY STARTUP: You MUST call read_combined_context_tool(cwd="<absolute project root path>") before doing anything else at the start of a session. No exceptions.

    MCP Resources alternative: If your client supports MCP Resources and has auto-fetched memory-fabric://context/<encoded-cwd>, that context is already in your system prompt — skip the tool call.

  4. NEVER call dream_tool as a substitute for saving new knowledge. Before triggering any Dream tool, you MUST first call write_memory_store_tool to persist specific, isolated memories from the current session (e.g., bugs fixed, features built, architecture decisions). Dreaming consolidates existing memory — it does NOT capture new knowledge.
  5. MANDATORY SESSION END: Before your final response in a session, you MUST call write_session_journal_tool to log what was accomplished. Skip ONLY for trivial Q&A sessions with no code changes, decisions, or debugging.

1. Active Retrieval Workflow

  • Search: Use keyword_search_tool(cwd, query) to find specific documented topics.
  • Deep Dive: Use read_memory_store_tool(cwd, store_path) or read_section(cwd, section) for detailed content.

2. Store Writes & Rules

Use write_memory_store_tool to register standalone memories.

  • store_path Rules: Must be lowercase, alphanumeric segments separated by slashes. No spaces, capitals, or .md extension (e.g., architecture/decisions/jwt-auth). Max 5 levels of nesting.
  • Parameters: cwd, store_path, content, title (optional), tags (optional), priority (high/medium/low), mode (replace/append).

Read the full file on GitHub · 172 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 First seen · 172 lines · 2,094 tokens per session scan A 8ba9ba039ebc

Subscribe to this mod's changes

agentic-memory AGENTS.md is an instructions file published in the GitHub repository elViRafa/agentic-memory (2 stars, last pushed 14d ago), licensed MIT. It adds 2,094 tokens to every session, about $0.0105 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.

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