MemOS is a memory operating system that stores, retrieves, and manages long-term memory for large language models and AI agents. It supports context-aware interactions by organizing text, images, tool traces, personas, and knowledge bases for reuse across tasks.
Borrowing it
Nothing to install: this file belongs to MemTensor/MemOS. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MemTensor/MemOS/main/AGENTS.mdgit clone --depth 1 https://github.com/MemTensor/MemOSWrote 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.
[](https://agentmods.dev/instructions/memtensor/memos/agents-md)<a href="https://agentmods.dev/instructions/memtensor/memos/agents-md"><img src="https://agentmods.dev/badge/instructions/memtensor/memos/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/memtensor/memos/agents-md"><img src="https://agentmods.dev/badge/instructions/memtensor/memos/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.02750 | $0.02750 |
| Opus 5.5 | $0.01100 | $0.01100 |
| Sonnet 5.5 | $0.00550 | $0.00550 |
| Haiku 4.5 | $0.00275 | $0.00275 |
Grade C, and why
MemOS AGENTS.md scanned grade C with 1 finding 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 2mo 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- Do not run wide-scope `rm -rf` outside `src/`; do not `git push --force` or `git reset --hard origin/*`. How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md
Single source of truth for the project across AI runtimes. Claude Code, Codex, Cursor, Copilot, etc. all defer to this file. Runtime-specific adaptation belongs in each runtime's own file (Claude reads
CLAUDE.md); do not mix it in here.
Project Overview
MemOS / MemoryOS: a memory operating system for LLM agents. Python library plus a FastAPI service, providing multiple memory types (textual / tree / preference / skill / KV cache / LoRA parametric) plus scheduling, version management, and vector & graph storage.
- Repository: https://github.com/MemTensor/MemOS
- Documentation: https://memos-docs.openmem.net/home/overview/
- PyPI: https://pypi.org/project/MemoryOS/
- License: Apache-2.0
- Top-level package:
src/memos/. Distribution nameMemoryOS; import namememos. - CLI:
memos(entrymemos.cli:main) - API service:
memos.api.start_api:app
Repository Layout
| Path | Purpose |
|---|---|
src/memos/mem_os/ |
MOS / MOSCore — top-level Memory OS entry |
src/memos/mem_cube/ |
GeneralMemCube — memory container aggregating multiple memory types |
src/memos/memories/ |
Memory implementations: textual/, activation/, parametric/ |
src/memos/mem_scheduler/ |
Memory scheduler + monitors + ORM + task scheduling |
src/memos/mem_user/ |
User / multi-tenant management (MySQL / Redis backends) |
src/memos/mem_chat/ mem_reader/ mem_agent/ mem_feedback/ multi_mem_cube/ |
Chat sessions, ingest pipeline, agent integration, feedback channel, multi-cube routing |
src/memos/llms/ embedders/ vec_dbs/ graph_dbs/ chunkers/ parsers/ reranker/ |
Provider implementations (base.py + factory.py + each backend) |
src/memos/api/ |
FastAPI service (routers / handlers / middleware / MCP server) |
src/memos/configs/ |
All pydantic configuration classes (one-to-one with the modules above) |
src/memos/context/ |
Cross-thread context (trace_id / user / env) |
tests/ |
pytest cases, subdirectories mirror src/memos/ |
apps/ |
Independent sub-projects, each with its own README; not part of the main Harness flow |
extensions/ |
Official plugin examples |
docker/ docs/ evaluation/ scripts/ |
Deployment, documentation, evaluation, helper scripts |
.claude/agents/, .codex/agents/ |
Project-recommended AI sub-agent definitions |
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.
- 2mo ago First seen · 156 lines · 2,750 tokens per session scan C 2eac7cfe6bb9
MemOS AGENTS.md is an instructions file published in the GitHub repository MemTensor/MemOS (12,182 stars, last pushed today), licensed Apache-2.0. It adds 2,750 tokens to every session, about $0.0110 per session on Opus 5.5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.