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.
git clone --depth 1 https://github.com/liuhao6741/openclaw-memoryWrote 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/rules/liuhao6741/openclaw-memory/memory)<a href="https://agentmods.dev/rules/liuhao6741/openclaw-memory/memory"><img src="https://agentmods.dev/badge/rules/liuhao6741/openclaw-memory/memory/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/rules/liuhao6741/openclaw-memory/memory"><img src="https://agentmods.dev/badge/rules/liuhao6741/openclaw-memory/memory.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 | $0.00326 | $0.00326 |
| Opus 5.5 | $0.00130 | $0.00130 |
| Sonnet 5.5 | $0.00065 | $0.00065 |
| Haiku 4.5 | $0.00033 | $0.00033 |
Grade A, and why
memory 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
Chat History Recording
强制要求（不可省略）
每次回复的最后一步必须调用 memory_log_conversation()，不可省略。
无论回复长短、是否写代码、是否只给一句话，都必须在结束前执行一次该工具，否则对话不会写入 journal。
user_message: 用户在本轮的完整输入（不截断）。agent_response: 你的完整回复（不省略、不用 "..." 或摘要）。model: 当前模型名（如 "claude-4-opus"）。code_changes: 若有创建/修改文件，列出（如 "-src/foo.py(created)"）。title: 可选。本轮一句话摘要；不传则用用户消息首行自动生成。
回复特别长时：先 memory_log_conversation(user_message, first_part)，再按需多次 memory_log_conversation_append(remaining_part)。
You have access to a chat history system via MCP tools (claw-memory); it writes each turn to .openclaw_memory/journal/YYYY-MM-DD.md. The above step is mandatory for every reply.
Search
Use memory_search(query) when:
- User mentions "before", "last time", "remember", "we discussed"
- You need to recall a past conversation
- Use
since="YYYY-MM-DD"to narrow by date
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 · 32 lines · 326 tokens per session scan A 0b1a01773069
memory is a cursor rule published in the GitHub repository liuhao6741/openclaw-memory (4 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 326 tokens to every session, about $0.0013 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 2026-08-31.
Other cursor rules, from other repositories
mempalace-recall-always
Always-on MemPalace recall — search the palace before answering about past work, people, projects, or prior decisions.
dreamd-recall
Search dreamd at the start of a task and quote a hit. Append when the user corrects you or states a decision.
session-memory
Use at conversation wrap-up or when the user explicitly indicates end-of-session — capture residual lessons not captured in-flight.
common_memory_bank
I am Cursor, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read…
self-improving-obsidian-llm-wiki
LLM Wiki OS operating rules.
memsprout
Cursor rule "memsprout" from memsprout/agents, covering using memsprout, search first, capturing, spaces and topics and files.