بنقرة واحدة
memory
Persistent triple-layer memory — store, search, and connect knowledge across sessions
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Persistent triple-layer memory — store, search, and connect knowledge across sessions
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | memory |
| version | 0.1.0 |
| description | Persistent triple-layer memory — store, search, and connect knowledge across sessions |
| author | openclaw |
| license | MIT |
| tags | ["memory","knowledge-graph","vector-search","persistence"] |
| tier | core |
Give your agent persistent memory that survives across sessions. Store facts, search by meaning, and traverse entity relationships.
When installed, your agent gains the ability to:
Store important information as it comes up in conversation. Don't store everything — store what matters: decisions, facts about people, project context, preferences, and lessons learned.
Good memories to store:
Don't store:
Before answering questions about people, projects, or past decisions, search memory first. This prevents the agent from asking the user to repeat themselves.
When the user asks relationship questions ("who reports to Alice?", "what projects is the platform team working on?"), use graph queries instead of text search.
The memory service is available at http://localhost:7777. Check health:
curl http://localhost:7777/api/health
curl -X POST http://localhost:7777/api/memories \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $MEMORY_TOKEN" \
-d '{
"agent_id": "YOUR_AGENT_ID",
"scope": "user",
"subject_id": "USER_ID",
"content": "The fact or preference to remember",
"tags": ["optional", "tags"]
}'
user — About a specific person (requires subject_id)agent — Agent's private knowledgeglobal — Shared across all agentsproject — About a specific project (subject_id = project slug)session — Conversation summariescurl -X POST http://localhost:7777/api/search \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $MEMORY_TOKEN" \
-d '{
"agent_id": "YOUR_AGENT_ID",
"query": "natural language question",
"limit": 5
}'
The search auto-selects the best strategy:
User: We just hired Sarah Kim as the new DevOps lead. She'll report to Alice.
Agent: Got it — I'll remember that.
[stores: "Sarah Kim hired as DevOps lead, reports to Alice Chen"]
[graph: Sarah Kim (Person, DevOps Lead) -[REPORTS_TO]-> Alice Chen]
User: (3 days later) Who's on Alice's team now?
Agent: [graph query: related entities for Alice Chen]
Based on what I know, Alice Chen's direct reports include:
• Bob Smith (Team Lead)
• Sarah Kim (DevOps Lead, joined recently)
User: What did we decide about the message queue situation?
Agent: [search: "message queue decision"]
Found a relevant memory from January 15th:
"Decided to switch from Kafka to NATS for the event bus.
Reasons: simpler ops, lower latency for our workload."
MEMORY_URL — Service URL (default: http://localhost:7777)MEMORY_TOKEN — Auth token