Best OpenClaw Skills for Agent Memory: Long-term Persistence, Vector Search, Knowledge Graphs & RAG
Agent memory is the unsolved problem at the center of practical AI deployment. A single-session Claude can be brilliant; the same Claude after a context reset has forgotten everything you told it yesterday. OpenClaw's memory ecosystem has grown into one of the platform's most developed categories, with skills spanning five architectural layers: session context, long-term persistence, vector retrieval, knowledge graphs, and memory maintenance.
Note: Install and download figures in text descriptions reflect stats at the time of writing and may be outdated. All skill tables are live — they fetch current data from the ClawHub database on every page load. Treat table values as authoritative.
By the Numbers
| Metric | Value |
|---|---|
| Skills in this guide | 27 |
| Architectural layers covered | 5 |
| Top skill by installs | elite-longterm-memory ( installs) |
| Top skill by downloads | ontology ( downloads) |
| Skills with 10+ installs | ~12 |
1. Session & Short-term Context Management
Short-term memory skills operate within or across a small number of sessions — bridging the gap between a single conversation and true long-term persistence. session-logs (214 installs, 5,496 downloads) lets agents search and analyze their own historical session transcripts, turning past conversations into retrievable context. session-memory (33 installs, 3,097 downloads) provides a persistent memory toolkit for saving and restoring agent context across session boundaries.
2. Long-term Persistent Memory
This is the category's core: skills that persist memory across indefinite sessions, surviving context resets and model upgrades. elite-longterm-memory leads with 298 installs and 31,900 downloads — it positions itself as the universal memory layer for Cursor, Claude, and Windsurf, supporting multiple storage backends. memory (126 installs, 6,137 downloads) is the simpler alternative: infinite organized memory that integrates directly with OpenClaw's built-in memory search. mem0 (16 installs) brings the Mem0 intelligent memory layer, which automatically decides what to remember and what to forget rather than storing everything.
3. Vector Memory & Semantic Search
Vector memory skills store embeddings rather than raw text, enabling semantic search ("find memories related to this concept") rather than keyword search. byterover (81 installs, 23,075 downloads) is a standout — its description explicitly instructs agents to use it for gathering context before answering questions, positioning it as a mandatory retrieval step rather than an optional tool. neural-memory (52 installs, 5,216 downloads) implements associative memory with spreading activation — a more sophisticated model where retrieving one memory automatically surfaces related ones.
4. Knowledge Graphs & Structured Memory
Structured memory skills impose schema on what agents remember — typed relationships, entity graphs, and hierarchical knowledge bases rather than flat text stores. ontology is the category's most-downloaded skill by a wide margin: 185 installs and 94,155 downloads. It builds a typed knowledge graph for structured agent memory — entities, relationships, and properties rather than freeform text. second-brain (20 installs, 3,938 downloads) implements a personal knowledge base with Ensue's knowledge management system. clawrag (5 installs, 1,177 downloads) provides a self-hosted RAG engine with hybrid semantic and keyword search, bridging structured retrieval and vector search.
5. Memory Hygiene & Optimization
Memory accumulates cruft over time — contradictory entries, outdated facts, duplicate records. These skills audit and clean existing memory stores rather than adding to them. memory-hygiene (136 installs, 12,957 downloads) is the leader: it audits, cleans, and optimizes Clawdbot's vector memory, identifying stale or conflicting entries and running a structured cleanup protocol. smart-memory-trigger-system (1 install) tackles the harder problem: automatically deciding when to save something to memory rather than saving everything and hoping hygiene handles the rest.
Recommended Combinations
| Your situation | Recommended stack |
|---|---|
| First memory setup for an agent | memory-setup + memory |
| Need cross-platform persistence (Cursor/Claude/Windsurf) | elite-longterm-memory |
| Semantic search over past context | byterover + vector-memory |
| Typed, relational knowledge (entities & relationships) | ontology |
| Self-hosted RAG over documents | clawrag + neural-memory |
| Auditing and cleaning existing memory | memory-hygiene |
| Intelligent memory with auto-forgetting | mem0 |
A Few Observations
ontology has an extraordinary download-to-install ratio. 94,155 downloads against 185 installs is a 500:1 ratio — the highest on the platform for any meaningful skill. This means the skill is being pulled as a dependency by other skills at massive scale, or it's being evaluated and uninstalled repeatedly. Knowledge graph memory is either a required building block that nobody installs directly, or it's the memory approach most people try and abandon.
byterover's mandatory framing is unusual. Most memory skills are optional enrichments. byterover instructs agents to use it before answering any question, making retrieval a prerequisite rather than a feature. This behavioral framing — "you MUST use this" — is an interesting prompt engineering pattern that turns a tool into a protocol.
There are two philosophies of what memory should do. One camp (memory, elite-longterm-memory, permanent-memory) treats memory as append-only: store everything, retrieve on demand. The other camp (mem0, smart-memory-trigger-system, memory-hygiene) treats memory as a managed resource: decide what's worth keeping, clean up what isn't. The first approach is simpler to implement; the second produces a cleaner context over time.
Session logs are underutilized as memory. session-logs (214 installs) lets agents search their own historical conversation transcripts — a form of memory that requires zero additional infrastructure because the data already exists. This is arguably the lowest-friction starting point for agent memory, yet it's rarely discussed in the same breath as vector databases or knowledge graphs.
Memory hygiene is the most underrated workflow. Most teams add memory skills but never run cleanup. memory-hygiene (136 installs) exists because uncurated memory accumulates contradictions and stale facts that actively degrade agent quality over time. The existence of this skill with meaningful install numbers suggests the problem is real and recognized.
Data source: ClawHub platform install and download counts as of April 12, 2026. Visit clawhub-skills.com to search for more skills.