Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Provides persistent memory management for storing, retrieving, updating, and deleting user-related information across conversations in OpenClaw AI.
Provides persistent memory management for storing, retrieving, updating, and deleting user-related information across conversations in OpenClaw AI.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
openclaw-tinmem provides persistent memory capabilities for the OpenClaw AI assistant.
Search and retrieve relevant memories from past conversations. Use this tool when: The user references something from a previous conversation You need context about user preferences, background, or past decisions You want to check if a problem has been solved before Parameters: query (required): Search query to find relevant memories scope: Memory namespace (default: "global") categories: Filter by category: profile, preferences, entities, events, cases, patterns limit: Max results (default: 10) level: Detail level - L0 (headline), L1 (summary), L2 (full content) Example: { "query": "user's preferred programming language", "categories": ["profile", "preferences"], "level": "L1" }
Store new information as a persistent memory. Use this tool when: The user shares important personal information A significant decision is made A problem is solved in a novel way A new project or entity is introduced Parameters: content (required): The information to store category (required): profile | preferences | entities | events | cases | patterns scope: Memory namespace importance: 0.0-1.0 (default: 0.5) tags: Keywords for searchability Example: { "content": "User is building a SaaS product called TaskFlow for project management, using Next.js 14 and PostgreSQL with Prisma", "category": "entities", "importance": 0.9, "tags": ["taskflow", "nextjs", "postgresql", "saas"] }
Remove memories that are no longer relevant or are incorrect. Parameters: id: Specific memory ID to delete query: Search and delete matching memories scope: Limit deletion to this scope categories: Limit deletion to these categories Example: { "query": "old job at previous company", "categories": ["profile", "entities"] }
Update an existing memory with corrected or additional information. Parameters: id (required): Memory ID to update content: New full content summary: New summary headline: New headline importance: New importance score tags: New tags (replaces existing)
CategoryWhat to StoreMerge BehaviorprofileUser identity, role, expertise, demographicsAlways mergepreferencesLikes/dislikes, habits, recurring preferencesTopic-based mergeentitiesProjects, people, tools, organizationsMerge if same entityeventsDecisions made, milestones, things that happenedAlways append (never merge)casesProblem-solution pairs, debugging sessionsAlways append (never merge)patternsRecurring workflows, methodologies, best practicesMerge if same pattern
Memories are automatically injected into context before each response via <agent-experience> tags New memories are automatically extracted after each conversation turn Similar memories are deduplicated using LLM-based analysis All memories persist across sessions in a local LanceDB database
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Largest current source with strong distribution and engagement signals.