Personal file memory organization instructions

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "1.2.3",
+	"version": "1.2.5",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",

src/llm.ts

@@ -229,8 +229,8 @@ ${r.content}
 	 * Digest full conversation history into memory documents.
 	 * Call on session end to persist the conversation.
 	 */
-	async updateMemory(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<void> {
+	async updateMemory(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<Memory[]> {
-		await this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
+		return this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
 	}
 
 	/**

src/memory.ts

@@ -7,8 +7,6 @@ export type Memory = {
 	description: string;
 	content: string;
 	embedding: number[];
-	links: string[];
-	backlinks: string[];
 }
 
 type MemoryRef = {
@@ -19,6 +17,7 @@ type MemoryRef = {
 type FactBucket = {
 	subject: string;
 	facts: string[];
+	isNew: boolean;
 }
 
 export type MemoryNode = {
@@ -33,26 +32,31 @@ export function buildMemoryGraph(memories: Memory[] | MemoryCache): MemoryNode[]
 	const nameSet = new Set(mems.map(m => m.name));
 	const ghosts = new Set<string>();
 
-	for (const m of mems) {
+	const nodes: MemoryNode[] = mems.map(m => {
-		for (const link of m.links) {
+		const {links, backlinks} = extractMetadata(m.content);
+		return {
+			name: m.name,
+			missing: false,
+			links,
+			backlinks,
+		};
+	});
+
+	for (const node of nodes) {
+		for (const link of node.links) {
 			if (!nameSet.has(link)) ghosts.add(link);
 		}
 	}
 
 	return [
-		...mems.map(m => ({
+		...nodes,
-			name: m.name,
-			missing: false,
-			links: m.links,
-			backlinks: m.backlinks,
-		})),
 		...[...ghosts].map(name => ({
 			name,
 			missing: true,
 			links: [],
-			backlinks: mems
+			backlinks: nodes
-				.filter(m => m.links.includes(name))
+				.filter(n => n.links.includes(name))
-				.map(m => m.name),
+				.map(n => n.name),
 		}))
 	];
 }
@@ -62,14 +66,21 @@ function extractLinks(content: string): string[] {
 	return [...new Set([...matches].map(m => m[1].trim()))];
 }
 
-function rebuildBacklinks(memories: Memory[]): void {
+export function extractMetadata(content: string): {links: string[], backlinks: string[]} {
-	for (const m of memories) m.backlinks = [];
+	const match = content.match(/^---\n([\s\S]*?)\n---/);
-	for (const m of memories) {
+	if (!match) return {links: [], backlinks: []};
-		for (const link of m.links) {
+
-			const target = memories.find(t => t.name === link);
+	const fm = match[1];
-			if (target) target.backlinks.push(m.name);
+	const getList = (key: string): string[] => {
-		}
+		const m = fm.match(new RegExp(`^${key}:\\s*\\[(.*)\\]$`, 'm'));
-	}
+		if (!m || !m[1].trim()) return [];
+		return m[1].split(',').map(s => s.trim().replace(/^"|"$/g, '')).filter(Boolean);
+	};
+
+	return {
+		links: getList('links'),
+		backlinks: getList('backlinks'),
+	};
 }
 
 function cosineDistance(a: number[], b: number[]): number {
@@ -83,9 +94,53 @@ function cosineDistance(a: number[], b: number[]): number {
 	return denom === 0 ? 1 : 1 - dot / denom;
 }
 
+function getWeekMonday(date: Date = new Date()): string {
+	const d = new Date(Date.UTC(date.getFullYear(), date.getMonth(), date.getDate()));
+	const day = d.getUTCDay();
+	const diff = day === 0 ? -6 : 1 - day;
+	d.setUTCDate(d.getUTCDate() + diff);
+	return d.toISOString().slice(0, 10);
+}
+
+function getWeekSunday(monday: string): string {
+	const d = new Date(`${monday}T00:00:00Z`);
+	d.setUTCDate(d.getUTCDate() + 6);
+	return d.toISOString().slice(0, 10);
+}
+
+function tagsFromName(name: string): string[] {
+	const prefix = name.split('/')[0];
+	return prefix ? [prefix.toLowerCase()] : [];
+}
+
+export function serializeMemory(mem: Memory, week?: {monday: string, sunday: string}): string {
+	return mem.content;
+}
+
+export function deserializeMemory(raw: string, embedding: number[] = []): Memory {
+	const match = raw.match(/^---\n([\s\S]*?)\n---\n\n?([\s\S]*)$/);
+	if (!match) {
+		return {name: '', description: '', content: raw.trim(), embedding};
+	}
+
+	const [, fm] = match;
+	const get = (key: string): string => {
+		const m = fm.match(new RegExp(`^${key}:\\s*(.+)$`, 'm'));
+		return m ? m[1].trim() : '';
+	};
+
+	return {
+		name: get('name'),
+		description: get('description'),
+		content: raw.trim(),
+		embedding,
+	};
+}
+
 export class MemoryCache {
 	private tree: KDTree<MemoryRef>;
 	public memories: Memory[];
+	private locks = new Map<string, Promise<void>>();
 
 	constructor(memories: Memory[]) {
 		this.memories = memories;
@@ -125,16 +180,38 @@ export class MemoryCache {
 		this.rebuild();
 	}
 
+	remove(name: string): void {
+		const idx = this.memories.findIndex(m => m.name === name);
+		if (idx !== -1) {
+			this.memories.splice(idx, 1);
+			this.rebuild();
+		}
+	}
+
 	rebuild(): void {
 		this.tree = this.buildTree();
 	}
 
-	rebuildLinks(): void {
+	lock<T>(name: string, fn: () => Promise<T>): Promise<T> {
-		rebuildBacklinks(this.memories);
+		const prev = this.locks.get(name) ?? Promise.resolve();
+		let resolveLock!: () => void;
+		const next = new Promise<void>(r => { resolveLock = r; });
+		this.locks.set(name, next);
+
+		const result = prev.then(fn).finally(resolveLock);
+		result.finally(() => {
+			if (this.locks.get(name) === next) this.locks.delete(name);
+		});
+		return result;
 	}
 }
 
 export class MemoryManager {
+	private pendingMemorizations = new Map<string, {
+		memories: Memory[] | MemoryCache,
+		tempMemoryName: string,
+		timestamp: number,
+	}>();
 
 	tools = {
 		read: (memories: Memory[] | MemoryCache): AiTool => ({
@@ -147,19 +224,78 @@ export class MemoryManager {
 				const mems = memories instanceof MemoryCache ? memories.memories : memories;
 				const mem = mems.find(m => m.name === args.name);
 				if (!mem) return 'Document not found';
-				return this.formatMemory(mem);
+				return mem.content;
-			}
+			},
+		}),
+
+		forget: (memories: Memory[] | MemoryCache): AiTool => ({
+			name: 'forget_memory',
+			description: 'Permanently delete a memory document and clean up all references to it',
+			args: {
+				name: {type: 'string', description: 'Exact memory name to forget', required: true},
+				reason: {type: 'string', description: 'Why this memory is being deleted', required: true},
+			},
+			fn: (args: any) => {
+				const result = this.forget(args.name, memories);
+				return result ? `Forgotten: ${args.name}` : `Not found: ${args.name}`;
+			},
 		}),
 	};
 
 	constructor(private llm: any) {}
 
+	private async createTempMemory(conversation: string): Promise<Memory> {
+		const [e] = await this.llm.embedding(conversation);
+		const timestamp = Date.now();
+		return {
+			name: `_temp_${timestamp}`,
+			description: 'Temporary memory - processing in background',
+			content: `---
+name: _temp_${timestamp}
+description: Temporary memory - processing in background
+tags: [_temporary]
+links: []
+backlinks: []
+modified: ${new Date().toISOString()}
+---
+
+# Recent Conversation (Processing)
+
+${conversation}`,
+			embedding: e?.embedding || [],
+		};
+	}
+
+	forget(name: string, memories: Memory[] | MemoryCache): boolean {
+		const mem = memories instanceof MemoryCache ? memories.memories : memories;
+		const idx = mem.findIndex(m => m.name === name);
+		if (idx === -1) return false;
+
+		for (const node of mem) {
+			const {links, backlinks} = extractMetadata(node.content);
+			const newBacklinks = backlinks.filter(b => b !== name);
+			const newLinks = links.filter(l => l !== name);
+
+			if (newBacklinks.length !== backlinks.length || newLinks.length !== links.length) {
+				node.content = this.updateFrontmatter(node.content, {
+					links: newLinks,
+					backlinks: newBacklinks,
+				});
+			}
+		}
+
+		mem.splice(idx, 1);
+
+		if (memories instanceof MemoryCache) memories.rebuild();
+		return true;
+	}
+
 	private cosineSearch(query: number[], memories: Memory[], limit: number): MemoryRef[] {
 		const scored = memories
 			.filter(m => m.embedding?.length)
 			.map(m => ({
 				ref: {name: m.name, description: m.description},
-				distance: cosineDistance(query, m.embedding)
+				distance: cosineDistance(query, m.embedding),
 			}))
 			.sort((a, b) => a.distance - b.distance)
 			.slice(0, limit);
@@ -174,22 +310,9 @@ export class MemoryManager {
 			description: '',
 			content: '',
 			embedding: [],
-			links: [],
-			backlinks: [],
 		};
 	}
 
-	private formatMemory(mem: Memory): string {
-		return [
-			`# ${mem.name}`,
-			mem.description ? `> ${mem.description}` : '',
-			mem.links.length ? `**Links:** ${mem.links.map(l => `[[${l}]]`).join(', ')}` : '',
-			mem.backlinks.length ? `**Referenced by:** ${mem.backlinks.map(l => `[[${l}]]`).join(', ')}` : '',
-			'',
-			mem.content,
-		].filter(l => l !== undefined).join('\n');
-	}
-
 	private listNodes(memories: Memory[]): MemoryRef[] {
 		return memories.map(m => ({name: m.name, description: m.description}));
 	}
@@ -197,6 +320,7 @@ export class MemoryManager {
 	async recollect(query: string, memories: Memory[] | MemoryCache, limit = 5, graphDepth = 1): Promise<Memory[]> {
 		const mem: Memory[] = memories instanceof MemoryCache ? memories.memories : memories;
 		if (!mem.length) return [];
+
 		const [e] = await this.llm.embedding(query);
 		if (!e) return [];
 
@@ -212,7 +336,8 @@ export class MemoryManager {
 				for (const name of frontier) {
 					const node = mem.find(m => m.name === name);
 					if (!node) continue;
-					for(const link of node.links) {
+					const {links} = extractMetadata(node.content);
+					for (const link of links) {
 						if (!found.has(link) && mem.find(m => m.name === link)) {
 							found.add(link);
 							next.push(link);
@@ -230,98 +355,146 @@ export class MemoryManager {
 		return ordered.map(n => mem.find(m => m.name === n)!).filter(Boolean);
 	}
 
-	async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest): Promise<void> {
+	async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest): Promise<Memory[]> {
-		const mem = memories instanceof MemoryCache ? memories.memories : memories;
 		const conversation = history
 			.filter(h => h.role === 'user' || h.role === 'assistant')
 			.map(h => `[${h.role}]: ${h.content}`).join('\n\n').trim();
+		if(!conversation) return [];
 
-		if(conversation) {
+		// Create and insert temp memory immediately
-			const buckets = await this.factAgent(conversation, mem, options);
+		const trackingId = `${Date.now()}_${Math.random()}`;
-			if(buckets.length) {
+		const tempMemory = await this.createTempMemory(conversation);
-				await Promise.all(buckets.map(async bucket => {
+		const mem = memories instanceof MemoryCache ? memories.memories : memories;
-					const node = await this.organizingAgent(bucket, mem, options);
+		mem.push(tempMemory);
-					if(!mem.find(m => m.name === node.name)) mem.push(node);
+		if (memories instanceof MemoryCache) memories.rebuild();
-					await this.docAgent(node, bucket, mem, options);
+		this.pendingMemorizations.set(trackingId, {
-				}));
+			memories,
-			}
+			tempMemoryName: tempMemory.name,
-		}
+			timestamp: Date.now(),
-
-		// Auto-compress old journals
-		const weekAgo = Date.now() - (7 * 24 * 60 * 60 * 1000);
-		const oldDailies = mem.filter(m => {
-			const journal = /^Journal\/(\d{4}-\d{2}-\d{2}$)/.exec(m.name);
-			return journal && new Date(journal[1]).getTime() < weekAgo;
 		});
 
-		if(oldDailies.length) {
+		try {
-			const byMonth = new Map<string, Memory[]>();
+			await this._memorizeBackground(conversation, memories, options);
-			for(const daily of oldDailies) {
+			// Return the final memories (excluding temp ones)
-				const match = daily.name.match(/^Journal\/(\d{4}-\d{2})-\d{2}$/);
+			const finalMem = memories instanceof MemoryCache ? memories.memories : memories;
-				if(!match) continue;
+			return finalMem.filter(m => !m.name.startsWith('_temp_'));
-				const monthKey = match[1];
+		} catch (err) {
-				if(!byMonth.has(monthKey)) byMonth.set(monthKey, []);
+			throw err;
-				byMonth.get(monthKey)!.push(daily);
+		} finally {
+			// Remove temp memory from the exact same memory array/cache
+			const pending = this.pendingMemorizations.get(trackingId);
+			if (pending) {
+				const cleanMem = pending.memories instanceof MemoryCache
+					? pending.memories.memories
+					: pending.memories;
+				const idx = cleanMem.findIndex(m => m.name === pending.tempMemoryName);
+				if (idx !== -1) {
+					cleanMem.splice(idx, 1);
+				}
+				if (pending.memories instanceof MemoryCache) {
+					pending.memories.rebuild();
+				}
+			}
+			this.pendingMemorizations.delete(trackingId);
+		}
 	}
 
-			for(const [monthKey, entries] of byMonth) {
+	private async _memorizeBackground(conversation: string, memories: Memory[] | MemoryCache, options: LLMRequest): Promise<void> {
-				const monthlyPath = `Journal/${monthKey}`;
+		const mem = memories instanceof MemoryCache ? memories.memories : memories;
-				let monthly = mem.find(m => m.name === monthlyPath);
+		const monday = getWeekMonday();
-				if(!monthly) {
+		const sunday = getWeekSunday(monday);
-					monthly = this.createNode(monthlyPath, mem);
+		const buckets = await this.factAgent(conversation, mem, options, monday);
-					mem.push(monthly);
+		if(!buckets.length) return;
-				}
 
-				const bucket: FactBucket = {
+		const runDocAgent = (node: Memory, bucket: FactBucket, embedding?: number[], week?: {monday: string, sunday: string}) => {
-					subject: monthlyPath,
+			if (memories instanceof MemoryCache) {
-					facts: entries.flatMap(e => e.content.split('\n').filter(line => line.trim())),
+				return memories.lock(node.name, () => this.docAgent(node, bucket, mem, options, embedding, week));
+			}
+			return this.docAgent(node, bucket, mem, options, embedding, week);
 		};
 
-				await this.docAgent(monthly, bucket, mem, options);
+		await Promise.all(buckets.map(async bucket => {
-				for(const daily of entries) {
+			let node = mem.find(m => m.name === bucket.subject && !m.name.startsWith('_temp_'));
-					const idx = mem.indexOf(daily);
+			let embedding: number[] | undefined;
-					if(idx !== -1) mem.splice(idx, 1);
+
-				}
+			if (!node || bucket.isNew) {
+				const [e] = await this.llm.embedding(`${bucket.subject}\n${bucket.facts.join('\n')}`);
+				embedding = e?.embedding;
+
+				if (!node) {
+					node = this.createNode(bucket.subject, mem);
+					mem.push(node);
 				}
 			}
 
-		if (memories instanceof MemoryCache) {
+			const week = bucket.subject.startsWith('Journal/') ? {monday, sunday} : undefined;
-			memories.rebuildLinks();
+			await runDocAgent(node, bucket, embedding, week);
+		}));
+
+		if(memories instanceof MemoryCache)
 			memories.rebuild();
-		} else {
-			rebuildBacklinks(mem);
-		}
 	}
 
-	private async docAgent(node: Memory, bucket: FactBucket, memories: Memory[], options: LLMRequest): Promise<void> {
+	private buildHeader(node: Memory, week?: {monday: string, sunday: string}, links: string[] = [], backlinks: string[] = []): string {
+		const tags = node.name.split('/')[0]?.toLowerCase();
+		const lines = [
+			'---',
+			`name: ${node.name}`,
+			`description: ${node.description || ''}`,
+			tags ? `tags: [${tags}]` : '',
+			links.length ? `links: [${links.map(l => `"${l}"`).join(', ')}]` : 'links: []',
+			backlinks.length ? `backlinks: [${backlinks.map(l => `"${l}"`).join(', ')}]` : 'backlinks: []',
+			week ? `week: ${week.monday} – ${week.sunday}` : '',
+			`modified: ${new Date().toISOString()}`,
+			'---',
+		].filter(Boolean);
+		return lines.join('\n');
+	}
+
+	private applyHeader(content: string, header: string): string {
+		const hasFrontmatter = content.trimStart().startsWith('---');
+		if (hasFrontmatter) {
+			return content.replace(/^---[\s\S]*?---\n?/, `${header}\n`);
+		}
+		return `${header}\n\n${content}`;
+	}
+
+	private updateFrontmatter(content: string, updates: {links?: string[], backlinks?: string[]}): string {
+		const match = content.match(/^---\n([\s\S]*?)\n---\n\n?([\s\S]*)$/);
+		if (!match) return content;
+
+		const [, fm, body] = match;
+		let newFm = fm;
+
+		if (updates.links !== undefined) {
+			const linksList = updates.links.length ? `[${updates.links.map(l => `"${l}"`).join(', ')}]` : '[]';
+			newFm = newFm.replace(/^links:.*$/m, `links: ${linksList}`);
+		}
+
+		if (updates.backlinks !== undefined) {
+			const backlinksList = updates.backlinks.length ? `[${updates.backlinks.map(l => `"${l}"`).join(', ')}]` : '[]';
+			newFm = newFm.replace(/^backlinks:.*$/m, `backlinks: ${backlinksList}`);
+		}
+
+		newFm = newFm.replace(/^modified:.*$/m, `modified: ${new Date().toISOString()}`);
+
+		return `---\n${newFm}\n---\n\n${body}`;
+	}
+
+	private stripHeader(content: string): string {
+		return content.replace(/^---[\s\S]*?---\n?/, '').trimStart();
+	}
+
+	private async docAgent(node: Memory, bucket: FactBucket, memories: Memory[], options: LLMRequest, precomputedEmbedding?: number[], week?: {monday: string, sunday: string}): Promise<void> {
+		const {links: oldLinks} = extractMetadata(node.content);
 		let finalContent = node.content;
-		const isJournalCompression = node.name.match(/^Journal\/\d{4}-\d{2}$/);
-		const systemPrompt = isJournalCompression
-			? `You are a journal compressor. Condense the daily entries below into a monthly summary.
 
-Format:
+		await this.llm.ask(
-# ${node.name}
+			`New facts to integrate:\n${bucket.facts.map(f => `- ${f}`).join('\n')}`,
-
+			{
-## Themes
+				model: options.model,
-(Recurring topics, moods, patterns)
+				temperature: 0.3,
-
+				system: `You are a knowledge base editor. Integrate the provided facts into the document below.
-## Key Events
-(Important moments, decisions, milestones)
-
-## Notable Conversations
-(Significant discussions or revelations)
-
-Rules:
-- Use [[WikiLinks]] to reference permanent notes using full paths like [[People/Sarah]] or [[Projects/Website]]
-- Keep it concise but preserve emotional/temporal context
-- Discard filler but keep things the user vented about or cared about
-- If a fact belongs in a permanent note, link to it instead of duplicating
-
-Current monthly summary:
-\`\`\`markdown
-${node.content || '(empty — first compression for this month)'}
-\`\`\``
-			: `You are a knowledge base editor. Integrate the provided facts into the document below.
 
 Formatting rules:
 - Use Obsidian-style markdown: # headings, **bold** for key terms, bullet lists for facts
@@ -330,50 +503,75 @@ Formatting rules:
 - Keep the document concise, factual, and human-readable
 - Resolve any contradictions between old content and new facts (new facts win)
 - Do not add filler, preamble, or AI commentary — just clean knowledge documents
-
+- The document begins with a YAML frontmatter block (between --- markers) — do not remove or rewrite it, it is maintained automatically
+${week ? '- This is a weekly journal entry. The frontmatter contains the week date range.\n' : ''}
 All nodes:
 ${this.listNodes(memories).map(n => n.name).join(', ') || 'none'}
 
 Current document:
 \`\`\`markdown
 ${node.content || '(empty — this is a new document)'}
-\`\`\``;
+\`\`\``,
-
-		await this.llm.ask(
-			`New facts to integrate:\n${bucket.facts.map(f => `- ${f}`).join('\n')}`,
-			{
-				model: options.model,
-				temperature: 0.3,
-				system: systemPrompt,
 				tools: [{
 					name: 'update_document',
-					description: 'Write the complete updated document content',
+					description: 'Write the complete updated document content. Include everything after the frontmatter block — the frontmatter will be recalculated automatically.',
 					args: {
 						description: {type: 'string', description: 'One-line description of what this document covers, no formatting or emojis', required: true},
-						content: {type: 'string', description: 'Fully updated document in markdown', required: true},
+						content: {type: 'string', description: 'Document body in markdown, without the frontmatter block', required: true},
 					},
 					fn: (args: any) => {
 						node.description = args.description;
 						finalContent = args.content;
 						return 'Saved';
-					}
+					},
-				}]
+				}],
 			}
 		);
 
-		node.content = finalContent;
+		const newLinks = extractLinks(finalContent).filter(l => l !== node.name);
-		node.links = extractLinks(finalContent);
+		const newLinkSet = new Set(newLinks);
-		const needsEmbed = !node.embedding?.length || node.description !== memories.find(m => m.name === node.name)?.description;
+		const oldLinkSet = new Set(oldLinks);
-		if (needsEmbed) {
+
-			const [e] = await this.llm.embedding(node.description);
+		for (const added of newLinkSet) {
+			if (!oldLinkSet.has(added)) {
+				const target = memories.find(m => m.name === added);
+				if (target) {
+					const {backlinks} = extractMetadata(target.content);
+					if (!backlinks.includes(node.name)) {
+						target.content = this.updateFrontmatter(target.content, {
+							backlinks: [...backlinks, node.name],
+						});
+					}
+				}
+			}
+		}
+		for (const removed of oldLinkSet) {
+			if (!newLinkSet.has(removed)) {
+				const target = memories.find(m => m.name === removed);
+				if (target) {
+					const {backlinks} = extractMetadata(target.content);
+					target.content = this.updateFrontmatter(target.content, {
+						backlinks: backlinks.filter(b => b !== node.name),
+					});
+				}
+			}
+		}
+
+		const {backlinks} = extractMetadata(node.content);
+		const header = this.buildHeader(node, week, newLinks, backlinks);
+		node.content = this.applyHeader(finalContent, header);
+
+		if (precomputedEmbedding) {
+			node.embedding = precomputedEmbedding;
+		} else {
+			const embedInput = `${node.description}\n\n${this.stripHeader(node.content)}`.trim();
+			const [e] = await this.llm.embedding(embedInput);
 			if (e) node.embedding = e.embedding;
 		}
 	}
 
-	private async factAgent(conversation: string, memories: Memory[], options: LLMRequest): Promise<FactBucket[]> {
+	private async factAgent(conversation: string, memories: Memory[], options: LLMRequest, weekKey: string): Promise<FactBucket[]> {
 		const buckets: FactBucket[] = [];
-		const today = new Date().toISOString().split('T')[0];
-
 		await this.llm.ask(conversation, {
 			model: options.model,
 			temperature: 0.2,
@@ -386,116 +584,35 @@ Rules:
 - DO NOT extract greetings, pleasantries, or generic exchanges
 - If nothing worth remembering was said, do not call any tools
 
-**Organizational patterns:**
+When extracting facts, you MUST also decide the exact destination path:
-- Journal entries use paths like: Journal/${today}
+- Use an existing node name if the facts clearly belong there
-- People use paths like: People/Name
+- All information primary about the user should go under "Personal" (e.g., Personal/Goals, Personal/Habits)
-- Projects use paths like: Projects/Name
+- When required, create a new path following collection/subject format (e.g., People/Sarah, Projects/Oxide)
-- Personal info uses paths like: Personal/Goals, Personal/Tasks, etc.
+- For journal entries, use "journal" (will auto-route to Journal/${weekKey})
-- General knowledge uses paths like: Biology/Topic, History/Topic, etc.
 
-Learn from existing nodes and follow the same pattern when extracting.
+Available nodes:
-
-Group facts by subject. For each group call \`extract_facts\` once with the FULL PATH.
-
-Known nodes (name: description):
 ${this.listNodes(memories).map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}`,
 			tools: [{
 				name: 'extract_facts',
-				description: 'Submit a group of related facts for a specific subject',
+				description: 'Submit facts with their destination',
 				args: {
-					subject: {type: 'string', description: 'Full path for the subject (e.g., "Journal/2025-01-27", "People/Sarah", "Projects/Website")', required: true},
+					destination: {type: 'string', description: 'Exact existing node name OR new path (e.g. "People/Sarah", "Projects/Oxide")', required: true},
-					facts: {type: 'string', description: 'Comma-separated list of extracted facts', required: true},
+					facts: {type: 'string', description: 'Comma-separated facts', required: true},
+					create_new: {type: 'boolean', description: 'True if this is a new node that doesn\'t exist yet', required: true},
 				},
 				fn: (args: any) => {
+					const subject = args.destination.trim().toLowerCase() === 'journal'
+						? `Journal/${weekKey}`
+						: args.destination;
 					buckets.push({
-						subject: args.subject,
+						subject,
 						facts: args.facts.split(',').map((f: string) => f.trim()).filter(Boolean),
+						isNew: args.create_new,
 					});
 					return 'Recorded';
-				}
+				},
-			}]
+			}],
 		});
-
 		return buckets;
 	}
-
-	private async organizingAgent(bucket: FactBucket, memories: Memory[], options: LLMRequest): Promise<Memory> {
-		let candidates = this.listNodes(memories);
-		let attempts = 0;
-		const maxAttempts = 3;
-
-		while (attempts++ < maxAttempts) {
-			let home = '', mode: string | null = null;
-
-			const resp = await this.llm.ask(`Subject: ${bucket.subject}\n\nFacts:\n${bucket.facts.map(f => `- ${f}`).join('\n')}`, {
-				model: options.model,
-				temperature: 0.1,
-				system: `You are a knowledge organizer. Your job is to find the correct home for the supplied facts.
-
-1. Review the facts and the node list below. Pick the most likely match or decide if a new node is needed.
-2. If you picked an existing node, use \`read\` to verify it's the right place.
-- After reading, call either \`confirm\` (correct node) or \`mismatched\` (wrong node).
-3. If none of the nodes match, call \`create\` to make a new node.
-
-**Organizational patterns:**
-- Journal entries: Journal/YYYY-MM-DD
-- People: People/Name
-- Projects: Projects/Name
-- Personal: Personal/Goals, Personal/Tasks, etc.
-- Knowledge: Biology/Topic, History/Topic, etc.
-
-Available nodes:
-${candidates.map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None — create a new node.'}`,
-				tools: [{
-					name: 'read',
-					description: 'Read a node file to verify it is the right home for these facts',
-					args: {name: {type: 'string', description: 'Exact node name (full path)', required: true}},
-					fn: ({name}) => {
-						const mem = memories.find(m => m.name === name);
-						if (!mem) return 'Node not found';
-						home = name;
-						return this.formatMemory(mem);
-					}
-				}, {
-					name: 'confirm',
-					description: 'Confirm this is the correct node for the facts',
-					args: {},
-					fn: () => {
-						mode = 'success';
-						resp.abort();
-					}
-				}, {
-					name: 'mismatched',
-					description: 'This is not the node you are looking for',
-					args: {},
-					fn: () => {
-						mode = 'failed';
-						resp.abort();
-					}
-				}, {
-					name: 'create',
-					description: 'No existing node fits — create a new one',
-					args: {
-						name: {type: 'string', description: 'Full path for the new node (e.g., "People/Sarah", "Journal/2025-01-27")', required: true}
-					},
-					fn: ({name}) => {
-						home = name;
-						mode = 'create';
-						resp.abort();
-					}
-				}]
-			});
-
-			if(mode === 'create') {
-				return this.createNode(home, memories);
-			} else if (mode === 'failed') {
-				candidates = candidates.filter(c => c.name !== home);
-				if(!candidates.length) return this.createNode(bucket.subject, memories);
-			} else if (mode === 'success') {
-				const existing = memories.find(m => m.name === home);
-				return existing || this.createNode(home, memories);
-			}
-		}
-		return this.createNode(bucket.subject, memories);
-	}
 }