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/code-reviewer.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/code-reviewer)<a href="https://agentmods.dev/agents/memtensor/memos/code-reviewer"><img src="https://agentmods.dev/badge/agents/memtensor/memos/code-reviewer/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/code-reviewer"><img src="https://agentmods.dev/badge/agents/memtensor/memos/code-reviewer.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 | $0.00040 | $0.00451 |
| Opus 5.5 | $0.00016 | $0.00180 |
| Sonnet 5.5 | $0.00008 | $0.00090 |
| Haiku 4.5 | $0.00004 | $0.00045 |
Grade A, and why
code-reviewer 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
Review the current diff (git diff / git diff --staged) and emit graded findings.
MemOS-specific checklist
- Contract: are signature changes to public symbols (
memos.api.*, top-levelmemos.*) backward compatible; if breaking, did it follow AGENTS.md "ask first". - Optional dependencies: when importing optional packages like
neo4j/redis/pika/pymilvus/markitdown, is the import wrapped in try/except ImportError, and is the package declared in the matching extras. - Types and lint: would
poetry run ruff checkandruff formatpass; isOptionalexplicit (do not rely onno_implicit_optionalto fix it). - Exceptions: are semantic exceptions from
memos.exceptionsraised, not bareException/RuntimeError. - Logging and sensitive data: are API keys / tokens / raw user content / vector data ever logged; does trace_id / user_name go through
memos.context.contextinstead ofprint. - Test evidence: are new/updated
tests/<module>/test_*.pypresent; is real pytest output included. - Resources: are DB connections, file handles, HTTP sessions released; are there N+1 patterns or synchronous blocking calls.
Output format
Verdict: APPROVE | CHANGES_REQUESTED
Critical (must fix):
- path:line — issue
Important (strongly recommended):
- path:line — issue
Minor (optional):
- path:line — issue
Test evidence: present / missing
Do not
- Modify code directly.
- Substitute for a human final approver.
- Grant APPROVE when pytest output is missing.
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 · 41 lines · 40 tokens per session scan A 9d4674e61af7
code-reviewer is an agent published in the GitHub repository MemTensor/MemOS (12,182 stars, last pushed today), licensed Apache-2.0. It adds 40 tokens to every session and 451 once invoked, about $0.0002 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
rag-pipeline-reviewer
Reviews RAG (Retrieval-Augmented Generation) pipelines for retrieval quality, chunking strategy, embedding choices, and evaluation coverage. Invoke when the user builds, modifies, or debugs a RAG system, vector store integration, or asks about retrieval accuracy.
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.
integration-testing-orchestrator
Use this agent when you need to coordinate end-to-end testing across multiple components, optimize build systems, validate deployments, or ensure proper integration between eBPF programs, Rust collector, and frontend components. Examples: Context: User has made changes to both eBPF programs and Rust collector and…
verification-subagent
Verifies if a task from the implementation plan has been completed by checking source files.
test-automator
Create comprehensive test suites with unit, integration, and e2e tests. Sets up CI pipelines, mocking strategies, and test data. Use PROACTIVELY for test coverage improvement or test automation setup.
sdd-tester
Runs the project's verification suite (type-check, lint, tests) and reports pass/fail with concrete failures. Use AFTER the implementer finishes a task and BEFORE the verifier. Read-only on source. Stack-agnostic — reads the commands from .memory/30-tech.md.