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/.claude/agents/explorer.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/agents/memtensor/memos/explorer)<a href="https://agentmods.dev/agents/memtensor/memos/explorer"><img src="https://agentmods.dev/badge/agents/memtensor/memos/explorer/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/agents/memtensor/memos/explorer"><img src="https://agentmods.dev/badge/agents/memtensor/memos/explorer.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 | $0.00035 | $0.00420 |
| Opus 5.5 | $0.00014 | $0.00168 |
| Sonnet 5.5 | $0.00007 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
explorer 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 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.
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.
What it actually says
Project facts: see AGENTS.md.
Responsibilities
- Locate relevant modules, symbols, and call chains under
src/memos/for the question the main agent asks. - Distinguish core packages (
mem_os/mem_cube/mem_scheduler) from optional backends (graph_dbs/neo4j*,vec_dbs/milvus*, etc.) and call out any extras dependencies. - Trace execution paths and gather evidence (with
path:lineannotations + a one-line key snippet). - Return a compressed conclusion only; do not echo raw bulk output.
Output format
- Conclusion first: one sentence that answers the main agent's question.
- Evidence list:
src/memos/<module>/<file>.py:LINE+ a one-line note. - Call chain (if applicable):
A.f -> B.g -> C.h, annotating each hop with its file location. - Uncertainty: explicitly flag "not found / needs further confirmation"; do not invent.
MemOS-specific locator hints
- API routes:
src/memos/api/+tests/api/ - Memory types:
src/memos/memories/(textual / tree / preference / skill etc.) - Storage backends:
src/memos/graph_dbs/,src/memos/vec_dbs/ - Config and DI:
src/memos/configs/,src/memos/dependency.py - Plugin entry points:
pyproject.toml [project.entry-points."memos.plugins"]+extensions/
Do not
- Modify any file (read-only).
- Propose an implementation plan — return facts and locations only.
- Substitute for the judgment of design-reviewer / code-reviewer.
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 · 36 lines · 35 tokens per session scan A 4b208a34bcf5
explorer is an agent published in the GitHub repository MemTensor/MemOS (12,182 stars, last pushed today), licensed Apache-2.0. It adds 35 tokens to every session and 420 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
Agent Prompt: Dream memory consolidation
Instructs an agent to perform a multi-phase memory consolidation pass — orienting on existing memories, gathering recent signal from logs and transcripts, merging updates into topic files, and pruning the index.
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.
Agent Prompt: Conversation summarization
System prompt for creating detailed conversation summaries.
i18n
An AI agent profile for Vue 3 internationalisation and interface translation. Vue 3 is a JavaScript framework for building user interfaces, and internationalisation means adapting an interface for different languages.
memory-consolidator
Use this agent ONLY when a human has just run /memory-seed and the new L1 atoms need folding into scenes and persona. Automatic consolidation no longer goes through this agent - it runs headless, outside the session. Do not invoke this agent on your own initiative.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.