Unified Memory Engine

Have you ever had to repeat yourself to an AI assistant? Tell it your name again, explain your project setup for the third time, remind it that you prefer TypeScript over JavaScript and dark roast over medium? Most AI assistants forget everything the moment you close the conversation.

Meggy doesn't. Its unified memory engine remembers your conversations, preferences, and documents — building a persistent, searchable knowledge base about you and your world. The more you use it, the less you have to explain.

Facts, episodic conversation summaries, and vault document chunks all live in a single SQLite store, searchable through a hybrid retrieval pipeline that combines vector cosine similarity with FTS5 full-text search.

Memory Types

The memory engine manages three distinct categories of information:

Facts

Discrete, declarative pieces of information extracted from conversations. For example:

• "The user prefers TypeScript over JavaScript"
• "The kids' school pickup is at 3:15 PM"
• "Mom is allergic to shellfish"

Facts are stored with embeddings so they can be retrieved semantically when relevant to a new conversation. Over time, Meggy builds a rich understanding of your preferences, routines, and family.

Episodic Summaries

After each conversation, the memory engine generates a compressed summary that captures the key decisions, outcomes, and context. These summaries provide continuity across sessions without replaying entire conversation histories. Last week's research session informs this week's follow-up.

Vault Documents

Chunks from ingested vault documents (see the Vault article) are also accessible through the memory engine. This means a search for "database schema" can return both conversation facts and technical documentation in a single result set.

Memory Presets

Not everyone wants the same level of memory. Meggy includes memory presets that control how aggressively facts are stored and recalled:

• Elephant — Maximum memory. Aggressively extracts and stores facts from every conversation. Best for power users who want Meggy to remember everything.
• Default — Balanced. Stores important facts and preferences while filtering out transient details.
• Goldfish — Minimal memory. Only stores facts you explicitly ask it to remember. Best for privacy-conscious users or shared devices.

Hybrid Retrieval Pipeline

When the AI needs context, the memory engine runs a two-stage retrieval:

Stage 1 — Parallel search:

• Vector search — Cosine similarity against fact and chunk embeddings
• FTS5 keyword search — SQLite's full-text search engine for exact term matches

Stage 2 — Fusion and ranking: Both result sets are merged using Reciprocal Rank Fusion (RRF), producing a single ranked list that captures both semantic relevance and keyword precision.

AI-Callable Memory Tools

The AI can actively manage memory during conversations:

• remember_fact — Store a new fact extracted from the conversation
• recall_facts — Search memory for relevant context given a query
• forget_fact — Remove an outdated or incorrect fact
• list_facts — Browse stored facts with optional filtering

Context Injection

Before each AI response, the memory engine automatically injects relevant context:

1. The current message is embedded using the embedding model role
2. Hybrid retrieval finds the top-K most relevant facts, episodes, and vault chunks
3. Matched context is formatted and prepended to the system prompt
4. The AI generates its response with full awareness of prior context

This happens transparently — Meggy always has access to its accumulated knowledge without you needing to manually provide context. It just knows.