Memory Literary Analysis

Memory Literary Analysis

Transform a complete literary work into a structured knowledge graph. Characters, themes, chapters, locations, symbols, and literary devices become interconnected notes — searchable, validatable, and traversable.

When to Use

• Analyzing a novel, play, poem, or non-fiction book end-to-end
• Building a teaching or study resource for a literary text
• Creating a book club companion knowledge base
• Research projects requiring structured close reading
• Stress-testing Basic Memory at scale (~200+ notes, 1000+ relations)

Pipeline Overview

Phase 0: Setup         → project, schemas, directory structure
Phase 1: Seed          → stub notes for known major entities
Phase 2: Process       → chapter-by-chapter notes in batches
Phase 3: Cross-ref     → enrich arcs, add parallels, write analysis
Phase 4: Validate      → schema checks, drift detection, consistency
Phase 5: Explore       → traverse the graph, write synthesis notes

Tools

Writing always goes through write_note and edit_note. For reading — which is most of the work in a long analysis — prefer the POSIX read verbs where they are available (enable_posix_tools for the MCP tools; the bm CLI verbs are always available):

The two rules that matter across a 100+ chapter run:

• Never read a note to check a field. That is what --meta predicates and --fields projection are for — one call answers what a read-per-note loop would cost.
• Never pull a whole file into context to reach one part of it. Sections and line ranges slice the output: the full note is still fetched, then cut down before it is returned. What they save is context, not I/O — a long chapter or a full source text costs you the tokens of the relevant part, not of the whole file.

These compound. In measured runs, predicate queries replaced 28-call scans with a single call; across 138 chapters that difference is the run.

Three sharp edges to know before you write a query. The first two fail quietly — a wrong answer, exit 0, no warning — so learn them here rather than from a graph you thought you had audited:

• `--meta` matches case-sensitively, and the stored value is always snake_case. note_type is an alias for the frontmatter type: key, compared with SQL =. write_note normalizes note_type through to_snake_case before writing, so a note authored as note_type="Chapter" is stored as type: chapter — the casing you author with is not the casing on disk. Query the snake_case form: --meta 'note_type=chapter'. The capitalized spelling returns zero rows and exit 0. The value a result row displays is the value to query with.
• `find` pages, and the default page is 10. Any query whose answer is "all N chapters" needs --page-size 200 (the maximum) — see Coverage Checks.
• `--name` cannot combine with `--meta`. The metadata search has no filename glob. Scope a --meta query with the positional path instead: find /characters --meta 'note_type=character'. That path is matched on a directory boundary against the file path a note is indexed under — where the note actually lives, not its permalink, which stops mirroring the file path once a note pins permalink: in frontmatter or is moved. So /characters reaches everything filed under characters/ (including characters/major/), and never characters-cut/.

If the POSIX verbs are unavailable, every step below still works with search_notes, read_note, and list_directory — it just costs more.

Phase 0: Setup

Create the Project

create_memory_project(name="<work-name>", path="~/basic-memory/<work-name>")

Use a kebab-case slug of the work's title (e.g., great-gatsby, hamlet, beloved).

Define Schemas

Write 6 schema notes to schema/. Each schema defines the entity type's fields, observation categories, and relation types. Adapt fields to fit the work — the schemas below are starting points, not rigid templates.

Character Schema

write_note(
  title="Character",
  directory="schema",
  note_type="schema",
  metadata={
    "entity": "Character",
    "version": 1,
    "schema": {
      "role(enum)": "[protagonist, antagonist, supporting, minor], character's narrative role",
      "description": "string, brief character description",
      "first_appearance?": "string, chapter or scene of first appearance",
      "status?(enum)": "[alive, dead, unknown, transformed], character status at end of work"
    },
    "settings": {"validation": "warn"}
  },
  content="""# Character

Schema for character entity notes.

## Observations
- [convention] Major characters in characters/major/, minor in characters/minor/
- [convention] Observation categories: trait, motivation, arc, quote, appearance, relationship, symbolism, fate
- [convention] Relations: appears_in, contrasts_with, allied_with, commands, symbolizes, associated_with"""
)

Add work-specific fields as needed — e.g., rank for military fiction, house for family sagas, species for fantasy.

Theme Schema

write_note(
  title="Theme",
  directory="schema",
  note_type="schema",
  metadata={
    "entity": "Theme",
    "version": 1,
    "schema": {
      "description": "string, what this theme explores",
      "prevalence(enum)": "[major, minor], how central to the work",
      "first_introduced?": "string, where theme first appears"
    },
    "settings": {"validation": "warn"}
  },
  content="""# Theme

Schema for thematic analysis notes.

## Observations
- [convention] Observation categories: definition, manifestation, evolution, counterpoint, quote, interpretation
- [convention] Relations: embodied_by, contrasts_with, reinforced_by, explored_in, expressed_through"""
)

Chapter Schema

write_note(
  title="Chapter",
  directory="schema",
  note_type="schema",
  metadata={
    "entity": "Chapter",
    "version": 1,
    "schema": {
      "chapter_number": "integer, sequential chapter number",
      "pov?": "string, point-of-view character or narrator mode",
      "setting?": "string, primary location",
      "narrative_mode?(enum)": "[dramatic, expository, reflective, epistolary, mixed], chapter's primary mode"
    },
    "settings": {"validation": "warn"}
  },
  content="""# Chapter

Schema for chapter-level analysis notes.

## Observations
- [convention] Chapters stored in chapters/ directory
- [convention] Observation categories: summary, event, tone, technique, quote, significance, foreshadowing
- [convention] Relations: features, set_in, explores, contains, employs, follows, precedes, parallels"""
)

Location Schema

write_note(
  title="Location",
  directory="schema",
  note_type="schema",
  metadata={
    "entity": "Location",
    "version": 1,
    "schema": {
      "description": "string, what this place is",
      "location_type(enum)": "[city, building, landscape, body_of_water, region, fictional, vehicle], type of place",
      "real_or_fictional(enum)": "[real, fictional, both], whether the place exists"
    },
    "settings": {"validation": "warn"}
  },
  content="""# Location

Schema for location and setting notes.

## Observations
- [convention] Observation categories: description, atmosphere, symbolism, significance, geography
- [convention] Relations: setting_for, associated_with, symbolizes, contains, part_of"""
)

Symbol Schema