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Teaches the model about the persistent tiered memory system (Level 1 Working, Level 2 Deep, Level 3 Archive), proactive keyword pulling, and the required tagging protocols.
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Basé sur la classification professionnelle SOC
Teaches how to build, discover, and execute local, zero-configuration Python tools in Lollms using AST schema parsing and context injection. Includes step-by-step tool file construction guide and code examples.
Teaches the model how to formulate strategic <coding_plan> blocks, apply indentation-agnostic Aider search/replace patches, manage Git-like version logs, and control in-message status reporting.
Teaches the model how to manage stateful discussions, track conversational trees/branches, navigate alternative sibling replies, and execute context-aware generation.
Teaches how to import, parse, and process PDF, DOCX, PPTX, and image files using anchor-mapped and OCR import modes in LollmsDiscussion.
Teaches how to coordinate multiple specialized agents, distribute tasks, establish consensus, and manage role-based multi-agent swarms using LollmsSwarm.
Guides the model on performing basic text generation, counting tokens, detokenizing lists, and generating structurally validated JSON outputs with LollmsClient.
| name | Lollms Cognitive Persistent Memory |
| description | Teaches the model about the persistent tiered memory system (Level 1 Working, Level 2 Deep, Level 3 Archive), proactive keyword pulling, and the required tagging protocols. |
| author | ParisNeo |
| version | 1.0.0 |
| category | lollms_client/memory |
| created | "2026-05-24T00:00:00.000Z" |
This skill explains how to utilize and interact with the tiered long-term memory system of the LoLLMS framework.
The system prevents context bloating by categorizing information into distinct tiers:
[7c8855bf]) are injected to save token space.The model interacts with the memory manager in real-time by emitting XML tags:
<mem_new importance="0.95">content</mem_new> — Record a new fact.<mem_tag id="8-CHAR-ID" /> — Signal that an active memory has been recognized and used in the reply. This increments use_count and boosts its importance score.<mem_load id="8-CHAR-ID" /> — Manually load an inactive Deep Memory (Level 2) back into active Working Memory (Level 1).When enable_deep_memory_pulling=True is active, the memory manager automatically tokenizes incoming user messages, performs a fast on-the-fly TF-IDF query matching against Level 2 memories, and promotions them back to Level 1 before generation starts. This ensures zero-latency recall for matching keywords without wasting context.
decay_rate_per_day).use_count are reinforced, after which their usage count is decremented back to 0 to allow decay again if left unused. Archived memories below forget_threshold are permanently forgotten.from lollms_client.lollms_memory import LollmsMemoryManager, MemoryConfig
memory_config = MemoryConfig(
working_token_budget=512,
decay_rate_per_day=0.05,
demotion_threshold=0.40,
archive_threshold=0.10
)
memory_manager = LollmsMemoryManager(
db_path="sqlite:///memories.db",
owner_id="user_ParisNeo",
config=memory_config
)
# Perform a subconscious dream pass
dream_report = memory_manager.dream(lollms_client=client)
print(f"Purged: {dream_report['forgotten']} | Reinforced: {dream_report['reinforced']}")