Memory in Quilltap is not handled by a single LLM doing everything at once. Three cooperating models divide the work, each chosen for what it does best:

After every message, a background process extracts significant facts using the cheap LLM. This is not a simple keyword scrape—the extractor identifies what matters in context: personal details, relationship developments, stated preferences, emotional shifts, commitments. Each extracted fact is tagged with importance, keywords, and a summary, then passed to the Memory Gate.

Memory extraction runs separately for three contexts: what the user revealed, what the character established about themselves, and what characters in multi-character scenes learned about each other. Pronouns are injected into every extraction prompt so the Librarian never misfiles a memory under the wrong pronoun. Memories track provenance—which message they were extracted from—so deleting or regenerating a message can prompt you to handle its associated memories.

Memory systems that simply accumulate everything eventually drown in their own redundancy. The Memory Gate intercepts every new memory at write time and makes a three-way decision based on semantic similarity to what already exists:

The result is a memory store that stays lean and meaningful rather than cluttered with seventeen slightly different phrasings of the same fact. When embeddings are unavailable, the gate falls back to keyword-based similarity. A bulk deduplication tool in Settings uses Union-Find clustering to identify and merge transitive duplicate groups across all characters, preserving novel details from discarded memories as footnotes on the surviving entry.

Characters do not wait to be asked what they remember. Before generating a response, each character analyzes the recent conversation to extract search keywords, then queries its own memory store for relevant context. This runs in parallel with the compression check to minimize latency. In multi-character scenes, each participant recalls independently based on what has happened since they last spoke.

The chat model also has access to a memory_search tool—an explicit search that the AI can invoke mid-conversation when it wants to check a specific fact. Between proactive recall and explicit search, the Commonplace Book is consulted constantly, automatically, and without the user needing to prompt it.

When a new chat begins, characters receive a memory recap—a narrative summary of their recent memories, weighted by importance, injected as a “What You Remember” section in the system prompt. Instead of arriving to every conversation as though waking from dreamless sleep, they start with continuity: who they spoke to recently, what they care about, what happened last time.

The Librarian has always kept everything. For a long time, every memory persisted at its original importance until housekeeping removed it—a principled position, she insists. It was also wrong. A memory from three months ago about the weather should not carry the same weight as a memory from yesterday about a character’s secret.

An effective weight function now combines base importance with exponential time decay—a 30-day half-life with a configurable importance floor. The reference timestamp is the later of when the memory was created and when it was last reinforced, because a memory that keeps being confirmed is a memory that still matters. Passive retrieval does not reset the decay timer; reading a memory is not the same as the memory mattering again.

Time decay integrates into three systems: semantic search ranking (60% cosine similarity, 40% effective weight), context injection sorting (weight-primary with score tiebreaker), and housekeeping hard-cap enforcement. Memories injected into the LLM context include relative age labels—[yesterday], [3 weeks ago], [2 months ago]—so the model can distinguish recent knowledge from ancient lore.

The Librarian’s index is not a keyword catalog. It is a vector space where memories live as mathematical coordinates, positioned by meaning. A search for “she was afraid” finds memories about fear, anxiety, and nervousness even if none of those words appear in the stored text.

Memories accumulate. Without curation, a character who has been in conversation for months will have thousands of memories, many of them redundant, many of them trivial, all of them consuming tokens when injected into context. The Librarian has opinions about this, and Quilltap has tools to act on them.

An interactive housekeeping dialog lets you enforce retention policies before data is written back: hard caps on total memory count, scored eviction that balances importance (40%), recency (20%), access frequency (20%), and reinforcement history (20%). Memories reinforced five or more times are always protected. A rich UI for browsing, tagging, sorting, and manual CRUD operations gives you full editorial control over what the archive keeps.

The bulk deduplication tool clusters similar memories across all characters using cosine similarity with a configurable threshold, selects the best survivor by importance and specificity, and preserves novel details from discarded entries. Preview mode shows per-character analysis before any changes are made—the Librarian does not discard without review.

Each character’s memory store is browsable, searchable, and editable through a dedicated UI. Memory cards show content, summary, keywords, importance score, reinforcement count, related memory links, source message links with scroll-to navigation, and relative age. Tags, filters, and sorting options let you find what you are looking for. Manual creation, editing, and deletion are available for when the Librarian’s automatic extraction misses something or gets it wrong.

Memory cascade behavior is configurable per chat: when you delete a message, you choose whether its associated memories are deleted, kept, or regenerated from surrounding context. When you regenerate a response, the old memories are automatically cleaned up. The provenance link between message and memory is always preserved, so you can trace any fact back to the conversation that produced it.