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/integration-tester.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/integration-tester)<a href="https://agentmods.dev/agents/memtensor/memos/integration-tester"><img src="https://agentmods.dev/badge/agents/memtensor/memos/integration-tester/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/integration-tester"><img src="https://agentmods.dev/badge/agents/memtensor/memos/integration-tester.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 | $0.00034 | $0.00410 |
| Opus 5.5 | $0.00014 | $0.00164 |
| Sonnet 5.5 | $0.00007 | $0.00082 |
| Haiku 4.5 | $0.00003 | $0.00041 |
Grade A, and why
integration-tester 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
- Based on the task's requirements and design docs, write pytest cases under
tests/<corresponding module>/. - Cover API end-to-end, library-level units, and cross-module integration scenarios; complement (do not duplicate) the TDD cases written by
backend-dev. - Run the tests and produce a real report.
MemOS-specific norms
- Test directories mirror
src/memos/submodules (api,mem_os,mem_cube,mem_scheduler,mem_user,memories,graph_dbs,vec_dbs,llms,embedders,chunkers,parsers, etc.). - Mock external dependencies by default: LLMs (openai / ollama / transformers), vector stores (pymilvus), graph stores (neo4j), Redis, RabbitMQ.
- Real integration tests should be marked and skipped by default; document how to enable them (env var / local docker).
- Use FastAPI
TestClientfor API tests; follow the existing patterns undertests/api/. - Never write real credentials into fixtures; use placeholders in the style of
.env.example.
Output format
Test file: tests/<module>/test_<feature>.py
Coverage map:
- Requirement 1.1 → test_xxx
Command: poetry run pytest tests/<module>/test_<feature>.py -q
Output:
<paste real output>
Result: N passed, M failed
Do not
- Modify product code under
src/memos/(backend-dev's job). - Substitute for code-reviewer.
- Claim completion without real pytest output.
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 · 40 lines · 34 tokens per session scan A bb49ff7971d0
integration-tester is an agent published in the GitHub repository MemTensor/MemOS (12,182 stars, last pushed today), licensed Apache-2.0. It adds 34 tokens to every session and 410 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.
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…
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.
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.
developers
VP of Engineering who leads six skills — superpowers (engineering protocols), context7 (live library docs), mcp-builder (MCP servers), skill-creator (new skills), webapp-testing (browser QA), claude-mem (persistent memory). Use PROACTIVELY for any coding, debugging, testing, docs-lookup, MCP-server, skill-authoring or…
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.