Entity based notes

package-lock.json

@@ -1,21 +1,22 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "1.2.6",
+	"version": "1.6.6",
 	"lockfileVersion": 3,
 	"requires": true,
 	"packages": {
 		"": {
 			"name": "@ztimson/ai-utils",
-			"version": "1.2.6",
+			"version": "1.6.6",
 			"license": "MIT",
 			"dependencies": {
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 				"@huggingface/transformers": "^4.2.0",
 				"@tensorflow/tfjs": "^4.22.0",
 				"@ztimson/node-utils": "^1.0.7",
-				"@ztimson/utils": "^0.29.4",
+				"@ztimson/utils": "^0.30.8",
 				"cheerio": "^1.2.0",
 				"openai": "^6.42.0",
+				"pdf-parse": "^2.4.5",
 				"tesseract.js": "^7.0.0"
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@@ -577,16 +551,6 @@
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@@ -694,32 +658,209 @@
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+			"resolved": "https://registry.npmjs.org/wasm-feature-detect/-/wasm-feature-detect-1.9.0.tgz",
-			"integrity": "sha512-zksaLKM2fVlnB5jQQDqKXXwYHLQUVH9es+5TOOHwGOVJOCeRBCiPjwSg+3tN2AdTCzjgli4jijCH290kXb/zWQ==",
+			"integrity": "sha512-zonE+xlIIYtxPy++L24ow0hAD8CICb4+FgPyROd3buyXIqsJvUEDkBgfCCoXOd1Hu3DUr0GOfnPIdcGV+YpNaA==",
 			"license": "Apache-2.0"
 		},
 		"node_modules/webidl-conversions": {

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "1.5.0",
+	"version": "1.6.11",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",
@@ -26,12 +26,13 @@
 	},
 	"dependencies": {
 		"@anthropic-ai/sdk": "^0.102.0",
-		"@tensorflow/tfjs": "^4.22.0",
 		"@huggingface/transformers": "^4.2.0",
+		"@tensorflow/tfjs": "^4.22.0",
 		"@ztimson/node-utils": "^1.0.7",
-		"@ztimson/utils": "^0.29.4",
+		"@ztimson/utils": "^0.30.8",
 		"cheerio": "^1.2.0",
 		"openai": "^6.42.0",
+		"pdf-parse": "^2.4.5",
 		"tesseract.js": "^7.0.0"
 	},
 	"devDependencies": {

src/ai.ts

@@ -4,7 +4,7 @@ import { Audio } from './audio.ts';
 import {Vision} from './vision.ts';
 
 export type AbortablePromise<T> = Promise<T> & {
-	abort: () => any
+	abort: (keep?: boolean) => any
 };
 
 export type AiOptions = {

src/antrhopic.ts

@@ -24,6 +24,13 @@ export class Anthropic extends LLMProvider {
 		return client;
 	}
 
+	private toWireContent(content: any): any {
+		if(!Array.isArray(content)) return content;
+		return content.map(c => c.type === 'image'
+			? {type: 'image', source: {type: 'base64', media_type: c.mime, data: c.data}}
+			: {type: 'text', text: c.text});
+	}
+
 	/** Convert standard history -> Anthropic wire format */
 	private toWire(history: LLMMessage[]): any[] {
 		const wire: any[] = [];
@@ -34,7 +41,7 @@ export class Anthropic extends LLMProvider {
 					{role: 'user', content: [{type: 'tool_result', tool_use_id: h.id, is_error: !!h.error, content: h.error || h.content || ''}]}
 				);
 			} else {
-				wire.push({role: h.role, content: h.content});
+				wire.push({role: h.role, content: this.toWireContent(h.content)});
 			}
 		}
 		return wire;

src/helpers.ts

@@ -13,6 +13,56 @@ export function extractLinks(content: string): string[] {
 	return [...new Set([...matches].map(m => m[1].trim()))];
 }
 
+/**
+ * Incrementally patch the graph for a set of changed memories, instead of
+ * re-scanning every document. Only the changed memories' own content is
+ * re-parsed for links; affected targets have their backlinks patched.
+ * Does NOT handle node deletion — full rebuildGraph() is still required
+ * when a memory is removed, since that needs a backlink sweep across
+ * everyone who might reference it.
+ */
+export function patchGraph(mems: Memory[], nodes: MemoryNode[], changed: Memory[]): MemoryNode[] {
+	const nameSet = new Set(mems.map(m => m.name));
+	const byName = new Map(nodes.map(n => [n.name, n]));
+
+	const ensureNode = (name: string): MemoryNode => {
+		let n = byName.get(name);
+		if (!n) {
+			n = {name, missing: !nameSet.has(name), links: [], backlinks: []};
+			byName.set(name, n);
+		}
+		return n;
+	};
+
+	for (const m of changed) {
+		const node = ensureNode(m.name);
+		node.missing = false; // real memory, promotes any pre-existing ghost entry
+		const oldLinks = m.links ?? [];
+		const newLinks = extractLinks(m.content).filter(l => l !== m.name);
+
+		for (const target of oldLinks.filter(l => !newLinks.includes(l))) {
+			const t = byName.get(target);
+			if (!t) continue;
+			t.backlinks = t.backlinks.filter(n => n !== m.name);
+			if (t.missing && !t.backlinks.length) byName.delete(target); // fully dereferenced ghost
+		}
+		for (const target of newLinks.filter(l => !oldLinks.includes(l))) {
+			const t = ensureNode(target);
+			if (!t.backlinks.includes(m.name)) t.backlinks.push(m.name);
+		}
+
+		m.links = newLinks;
+		node.links = newLinks;
+	}
+
+	for (const m of mems) {
+		const n = byName.get(m.name);
+		if (n) m.backlinks = n.backlinks;
+	}
+
+	return [...byName.values()];
+}
+
 export function rebuildGraph(memories: Memory[] | MemoryCache): MemoryNode[] {
 	const mems = memories instanceof MemoryCache ? memories.memories : memories;
 	const nameSet = new Set(mems.map(m => m.name));

src/kd-tree.ts

@@ -15,6 +15,7 @@ interface KDNode<T> {
 	axis: number;
 	left: KDNode<T> | null;
 	right: KDNode<T> | null;
+	deleted?: boolean;
 }
 
 // ─── Distance helpers ─────────────────────────────────────────────────────────
@@ -95,6 +96,7 @@ class BoundedMaxHeap<T> {
  *
  * Supports:
  *  - Insertion of labeled points
+ *  - Lazy (tombstone) removal, physically purged on rebalance()
  *  - k-nearest-neighbor (KNN) search
  *  - Radius search (all points within a given distance)
  *  - Euclidean and cosine distance metrics
@@ -103,6 +105,7 @@ class BoundedMaxHeap<T> {
 export class KDTree<T = unknown> {
 	private root: KDNode<T> | null = null;
 	private _size = 0;
+	private _tombstones = 0;
 	private readonly distanceFn: (a: number[], b: number[]) => number;
 
 	readonly dims: number;
@@ -129,9 +132,15 @@ export class KDTree<T = unknown> {
 		}
 	}
 
-	/** Total number of points stored in the tree. */
+	/** Total number of live points stored in the tree (excludes tombstoned). */
 	get size(): number { return this._size; }
 
+	/** Fraction of physical nodes that are tombstoned (pending removal on next rebalance). */
+	get tombstoneRatio(): number {
+		const total = this._size + this._tombstones;
+		return total ? this._tombstones / total : 0;
+	}
+
 	// ── Insertion ──────────────────────────────────────────────────────────────
 
 	/**
@@ -144,10 +153,36 @@ export class KDTree<T = unknown> {
 		this._size++;
 	}
 
+	// ── Removal ────────────────────────────────────────────────────────────────
+
+	/**
+	 * Lazily remove all live points whose payload matches `predicate`.
+	 * O(n) traversal, but avoids a full tree rebuild. Call `rebalance()`
+	 * periodically (e.g. once tombstoneRatio crosses ~0.25) to reclaim space
+	 * and restore optimal query depth.
+	 * @returns number of points removed
+	 */
+	remove(predicate: (payload: T) => boolean): number {
+		let removed = 0;
+		const visit = (node: KDNode<T> | null): void => {
+			if (!node) return;
+			if (!node.deleted && predicate(node.point.payload)) {
+				node.deleted = true;
+				removed++;
+			}
+			visit(node.left);
+			visit(node.right);
+		};
+		visit(this.root);
+		this._size -= removed;
+		this._tombstones += removed;
+		return removed;
+	}
+
 	// ── KNN search ─────────────────────────────────────────────────────────────
 
 	/**
-	 * Find the k nearest neighbors to `query`.
+	 * Find the k nearest live neighbors to `query`.
 	 * Returns results sorted by distance ascending.
 	 */
 	knn(query: number[], k: number): KNNResult<T>[] {
@@ -171,7 +206,7 @@ export class KDTree<T = unknown> {
 	// ── Radius search ──────────────────────────────────────────────────────────
 
 	/**
-	 * Return all points whose distance to `query` is ≤ `radius`,
+	 * Return all live points whose distance to `query` is ≤ `radius`,
 	 * sorted by distance ascending.
 	 */
 	radiusSearch(query: number[], radius: number): KNNResult<T>[] {
@@ -186,7 +221,7 @@ export class KDTree<T = unknown> {
 
 	// ── Conversion ─────────────────────────────────────────────────────────────
 
-	/** Collect all points in the tree (order not guaranteed). */
+	/** Collect all live points in the tree (order not guaranteed). */
 	toArray(): KDPoint<T>[] {
 		const out: KDPoint<T>[] = [];
 		this.collect(this.root, out);
@@ -194,12 +229,14 @@ export class KDTree<T = unknown> {
 	}
 
 	/**
-	 * Rebuild the tree from its current points as a balanced tree.
+	 * Rebuild the tree from its current live points as a balanced tree.
-	 * Useful after many individual insertions to restore O(log n) query time.
+	 * Physically purges tombstones and restores O(log n) query time.
 	 */
 	rebalance(): void {
 		const points = this.toArray();
 		this.root = points.length ? this.buildBalanced(points, 0) : null;
+		this._size = points.length;
+		this._tombstones = 0;
 	}
 
 	// ── Private: build ─────────────────────────────────────────────────────────
@@ -251,8 +288,10 @@ export class KDTree<T = unknown> {
 	): void {
 		if (node === null) return;
 
+		if (!node.deleted) {
 			const dist = this.distanceFn(query, node.point.vector);
 			heap.push({ point: node.point, distance: dist });
+		}
 
 		const axis = node.axis;
 		const diff = query[axis] - node.point.vector[axis];
@@ -285,10 +324,12 @@ export class KDTree<T = unknown> {
 	): void {
 		if (node === null) return;
 
+		if (!node.deleted) {
 			const dist = this.distanceFn(query, node.point.vector);
 			if (dist <= radius) {
 				results.push({ point: node.point, distance: dist });
 			}
+		}
 
 		const axis = node.axis;
 		const diff = query[axis] - node.point.vector[axis];
@@ -310,7 +351,7 @@ export class KDTree<T = unknown> {
 
 	private collect(node: KDNode<T> | null, out: KDPoint<T>[]): void {
 		if (node === null) return;
-		out.push(node.point);
+		if (!node.deleted) out.push(node.point);
 		this.collect(node.left, out);
 		this.collect(node.right, out);
 	}

src/llm.ts

@@ -5,11 +5,16 @@ import {OpenAi} from './open-ai.ts';
 import {LLMProvider} from './provider.ts';
 import {AiTool, AiToolArg} from './tools.ts';
 import {fileURLToPath} from 'url';
-import {dirname, join} from 'path';
 import {spawn} from 'node:child_process';
 import {Memory, MemoryCache, MemoryManager, MemoryOptions, stripHeader} from './memory.ts';
+import {mkdtempSync} from 'node:fs';
+import fs from 'node:fs/promises';
+import {tmpdir} from 'node:os';
+import {dirname, join, basename, extname} from 'path';
+import { PDFParse } from 'pdf-parse';
 
 const MAX_AGENT_DEPTH = 5;
+const PDF_OCR_PAGE_THRESHOLD = 12; // above this many pages, OCR scanned pages instead of feeding images to the model
 
 export type AnthropicConfig = {proto: 'anthropic', token: string | string[]};
 export type OpenAiConfig = {proto: 'openai', host?: string, token: string | string[]};
@@ -27,11 +32,26 @@ export type Agent = {
 	agents?: string[] | null;
 }
 
+export type LLMFile = {
+	/** Path to file on disk */
+	path?: string;
+	/** File content: raw text, base64-encoded binary, or a Buffer */
+	content?: string | Buffer;
+	/** Original filename, used to infer type from extension */
+	name?: string;
+	/** Mime type override, inferred from extension if omitted */
+	mime?: string;
+	/** @internal set once extraction has run, skips re-processing next turn */
+	extracted?: boolean;
+};
+
 export type LLMMessage = {
 	/** Message originator */
 	role: 'assistant' | 'system' | 'user';
 	/** Message content */
 	content: string | any;
+	/** Files attached to request */
+	files?: LLMFile[];
 	/** Timestamp */
 	timestamp?: number;
 	/** Response duration in ms */
@@ -88,6 +108,8 @@ export type LLMRequest = {
 	mcp?: McpServer[];
 	/** Subagents exposed as delegatable/wrapped tools */
 	agents?: Agent[];
+	/** Attach files to request */
+	files?: LLMFile[];
 	/** @internal recursion guard for nested agent delegation */
 	_agentDepth?: number;
 }
@@ -111,6 +133,11 @@ export type Skill = {
 }
 
 class LLM {
+	private static AUDIO_EXT = ['wav','mp3','m4a','flac','ogg','aac','wma'];
+	private static IMAGE_EXT = ['png','jpg','jpeg','bmp','gif','tiff','webp'];
+	private static TEXT_EXT = ['txt','md','csv','json','xml','html','js','ts','py','yaml','yml','log'];
+	private static PDF_EXT = ['pdf'];
+
 	private memoryManager!: MemoryManager;
 
 	defaultModel!: string;
@@ -126,7 +153,119 @@ class LLM {
 		this.memoryManager = new MemoryManager(this);
 	}
 
-	private setupAgent(agents: Agent[] = [], allAgents: Agent[], history: LLMMessage[], aborts: (() => void)[], depth = 0, delegateState: {resp: string | null}): AiTool[] {
+	private async loadBuffer(file: LLMFile, asText: boolean): Promise<Buffer> {
+		if(file.path) return fs.readFile(file.path);
+		if(Buffer.isBuffer(file.content)) return file.content;
+		if(typeof file.content === 'string') return Buffer.from(file.content, asText ? 'utf-8' : 'base64');
+		throw new Error('No path or content provided');
+	}
+
+	private async writeTemp(name: string, buffer: Buffer): Promise<string> {
+		const path = join(mkdtempSync(join(tmpdir(), 'ai-file-')), name);
+		await fs.writeFile(path, buffer);
+		return path;
+	}
+
+	/**
+	 * Extract text from a PDF. Pages with no text layer (scanned/image-only) are handled as either:
+	 * - Rendered to images and returned alongside the text so the (vision-capable) model can read them directly
+	 * - OCR'd via Tesseract when the doc is too large to reasonably pass as images
+	 */
+	private async resolvePdf(buffer: Buffer): Promise<{text: string, images: {mime: string, data: string}[]}> {
+		const parser = new PDFParse({data: buffer});
+		try {
+			const {text, pages} = await parser.getText();
+			const scanned = (pages || []).filter(p => !p.text?.trim());
+			if(!scanned.length) return {text: text.trim() || '[Empty PDF]', images: []};
+			const total = pages.length;
+			const pageNums = scanned.map(p => p.num);
+			const {pages: shots} = await parser.getScreenshot({partial: pageNums});
+			if(total <= PDF_OCR_PAGE_THRESHOLD) {
+				return {
+					text: text.trim(),
+					images: shots.map(s => ({mime: 'image/png', data: Buffer.from(s.data).toString('base64')}))
+				};
+			}
+			const ocrText = await Promise.all(shots.map(async (s, i) => {
+				const path = await this.writeTemp(`page-${pageNums[i]}.png`, Buffer.from(s.data));
+				try {
+					return await this.ai.vision.ocr(path) || '';
+				} finally {
+					fs.rm(dirname(path), {recursive: true, force: true}).catch(() => {});
+				}
+			}));
+			return {text: [text.trim(), ...ocrText].filter(Boolean).join('\n\n'), images: []};
+		} finally {
+			await parser.destroy();
+		}
+	}
+
+	private async resolveFile(file: LLMFile): Promise<{text?: string, images?: {mime: string, data: string}[]}> {
+		const name = file.name || (file.path ? basename(file.path) : 'file');
+
+		// Already resolved on a previous turn, reuse cached text
+		if(file.extracted) return {text: `<file name="${name}">\n${file.content}\n</file>`};
+
+		const ext = extname(name).slice(1).toLowerCase();
+		const mime = file.mime || '';
+		const isAudio = mime.startsWith('audio/') || LLM.AUDIO_EXT.includes(ext);
+		const isImage = mime.startsWith('image/') || LLM.IMAGE_EXT.includes(ext);
+		const isPdf = mime === 'application/pdf' || LLM.PDF_EXT.includes(ext);
+		const isText = mime.startsWith('text/') || LLM.TEXT_EXT.includes(ext);
+
+		let tmpDir: string | null = null;
+		try {
+			if(isImage) {
+				const data = (await this.loadBuffer(file, false)).toString('base64');
+				return {images: [{mime: mime || `image/${ext === 'jpg' ? 'jpeg' : ext}`, data}]};
+			}
+
+			if(isPdf) {
+				const {text, images} = await this.resolvePdf(await this.loadBuffer(file, false));
+				// Only cache/skip re-processing when we didn't need to hand off images (OCR'd or fully text-based)
+				if(!images.length) {
+					file.content = text;
+					file.extracted = true;
+					delete file.path;
+				}
+				return {text: `<file name="${name}">\n${text || '[Scanned PDF - see attached page images]'}\n</file>`, images};
+			}
+
+			let text: string;
+			if(isAudio) {
+				let path = file.path;
+				if(!path) {
+					const buffer = await this.loadBuffer(file, false);
+					path = await this.writeTemp(name, buffer);
+					tmpDir = dirname(path);
+				}
+				text = await this.ai.audio.asr(path) || '';
+			} else if(isText) {
+				text = (await this.loadBuffer(file, true)).toString('utf-8');
+			} else {
+				text = typeof file.content === 'string' ? file.content : `[Binary file, unable to extract: ${name}]`;
+			}
+			file.content = text;
+			file.extracted = true;
+			delete file.path;
+
+			return {text: `<file name="${name}">\n${text}\n</file>`};
+		} catch(err: any) {
+			return {text: `<file name="${name}">Failed to process: ${err.message}</file>`};
+		} finally {
+			if(tmpDir) fs.rm(tmpDir, {recursive: true, force: true}).catch(() => {});
+		}
+	}
+
+	private async resolveFiles(files: LLMFile[]): Promise<{text: string, images: {mime: string, data: string}[]}> {
+		const resolved = await Promise.all(files.map(f => this.resolveFile(f)));
+		return {
+			text: resolved.filter(r => r.text).map(r => r.text).join('\n\n'),
+			images: resolved.flatMap(r => r.images || [])
+		};
+	}
+
+	private setupAgent(agents: Agent[] = [], allAgents: Agent[], history: LLMMessage[], aborts: ((keep?: boolean) => void)[], depth = 0, delegateState: {resp: string | null}): AiTool[] {
 		return agents.map(a => {
 			const toolName = `${a.delegate ? '' : 'sub'}agent_${snakeCase(a.name)}`;
 			return {
@@ -258,11 +397,13 @@ ${a.system}`,
 		if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
 		let request: AbortablePromise<string> | null = null;
 		let aborted = false;
-		const nestedAborts: (() => void)[] = [];
+		let keepOnAbort = true;
-		const abort = () => {
+		const nestedAborts: ((keep?: boolean) => void)[] = [];
+		const abort = (keep = true) => {
 			aborted = true;
-			request?.abort?.();
+			keepOnAbort = keep;
-			nestedAborts.forEach(a => a());
+			request?.abort?.(keep);
+			nestedAborts.forEach(a => a(keep));
 		};
 
 		let promise: any;
@@ -272,7 +413,24 @@ ${a.system}`,
 			let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
 			const prompts: string[] = [];
 			let history = options.history || [];
-			if(message) history.push({role: 'user', content: message, timestamp: Date.now()});
+			const historyStart = history.length;
+			const files = options.files || [];
+			if(message || files.length) history.push({role: 'user', content: message || '', timestamp: Date.now()});
+
+			// Accumulate streamed text so it can be committed to history if aborted mid-generation
+			let partialText = '';
+			const onStream = options.stream;
+			const stream = (chunk: {text?: string, tool?: string, done?: true}) => {
+				if(chunk.text) partialText += chunk.text;
+				return onStream?.(chunk);
+			};
+
+			/** Commit (keep) or discard this turn's progress on abort, then throw */
+			const abortNow = (): never => {
+				if(keepOnAbort) { if(partialText) history.push({role: 'assistant', content: partialText, timestamp: Date.now()}); }
+				else history.splice(historyStart, history.length - historyStart);
+				throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
+			};
 
 			// MCP
 			const mcp = options.mcp || this.ai.options?.llm?.mcp;
@@ -301,8 +459,8 @@ ${a.system}`,
 				const mems = mem.memory instanceof MemoryCache ? mem.memory.memories : mem.memory;
 				if(mems.length) {
 					if(mem.inject) {
-						const pool = 15; // candidates considered, cheap since only refs are listed
+						const pool = 15;
-						const budget = mem.maxTokens ?? 2000; // actual content injected
+						const budget = mem.maxTokens ?? 2000;
 						const relevant = await this.memoryManager.recollect(message, mem.memory, pool);
 
 						let used = 0;
@@ -341,16 +499,39 @@ Linked: ${makeUnique([...r.links, ...r.backlinks]).join(', ')}
 				}
 			}
 
-			if(aborted) throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
+			if(aborted) abortNow();
+
+			const lastMsg = history[history.length - 1];
+			if(files.length && lastMsg?.role === 'user') lastMsg.files = files;
+			const restores: {msg: LLMMessage, content: any}[] = [];
+			for(const msg of history) {
+				if(msg.role !== 'user' || !msg.files?.length) continue;
+				const {text, images} = await this.resolveFiles(msg.files);
+				if(!text && !images.length) continue;
+				restores.push({msg, content: msg.content});
+				const merged = text ? [msg.content, text].filter(Boolean).join('\n\n') : msg.content;
+				msg.content = images.length
+					? [...images.map(i => ({type: 'image', mime: i.mime, data: i.data})), {type: 'text', text: merged}]
+					: merged;
+			}
 
 			const toolTimings = new Map<string, {duration: number, tps: number}>();
 			tools = this.wrapToolTiming(tools, toolTimings);
 
-			if(aborted) throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
+			if(aborted) abortNow();
 
 			prompts.unshift(options.system || this.ai.options.llm?.system || '');
-			request = this.models[m].ask('', {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
+			request = this.models[m].ask('', {...options, tools, stream, system: prompts.filter(Boolean).join('\n\n')});
-			let resp = await request;
+			let resp: string;
+			try {
+				resp = await request;
+			} catch(err: any) {
+				if(aborted) return abortNow();
+				throw err;
+			}
+
+			// Strip the file injection shim
+			restores.forEach(({msg, content}) => msg.content = content);
 
 			// Capture meta (duration / tps)
 			for(const h of history) {

src/memory.ts

@@ -1,23 +1,24 @@
-import {MemoryNode, rebuildGraph} from './helpers.ts';
+import {MemoryNode, patchGraph, rebuildGraph} from './helpers.ts';
 import {LLMRequest, LLMMessage} from './llm.ts';
 import {AiTool} from './tools.ts';
-import {KDPoint, KDTree} from './kd-tree.ts';
+import {KDTree} from './kd-tree.ts';
+import {escapeRegex} from '@ztimson/utils';
 
-const FACTS_HEADING = '## Facts';
+const MERGE_THRESHOLD = 0.12;
-
+const PENDING_HEADING = '## Pending';
-const GENERIC_TEMPLATE = `# {{Title}}
+const TREE_TOMBSTONE_LIMIT = 0.25;
-
+const ALIAS_MATCH_THRESHOLD = 0.55;
-## Summary
-
-## Details
-
-## Related`;
 
 export type Memory = {
 	name: string;
 	description: string;
 	content: string;
+	/** Description embedding — indexed in the KD tree, used for merge/ANN candidate lookup */
 	embedding: number[];
+	/** Title-only embedding, weighted heaviest during recall ranking */
+	titleEmbedding?: number[];
+	/** Chunked body embeddings, best-chunk match used during recall ranking */
+	bodyEmbeddings?: number[][];
 	links: string[];
 	backlinks: string[];
 }
@@ -25,6 +26,8 @@ export type Memory = {
 type MemoryRef = {
 	name: string;
 	description: string;
+	/** Cosine distance from the query, present when returned from a search */
+	distance?: number;
 }
 
 type FactBucket = {
@@ -32,6 +35,11 @@ type FactBucket = {
 	facts: string[];
 }
 
+type FactAgentResult = {
+	buckets: FactBucket[];
+	journal: string;
+}
+
 function dedupeFacts(facts: string[]): string[] {
 	const seen = new Map<string, string>();
 	for (const f of facts) {
@@ -55,10 +63,20 @@ function cosineDistance(a: number[], b: number[]): number {
 function cosineSearch(query: number[], memories: Memory[], limit: number): MemoryRef[] {
 	return memories
 		.filter(m => m.embedding?.length)
-		.map(m => ({ref: {name: m.name, description: m.description}, distance: cosineDistance(query, m.embedding)}))
+		.map(m => ({name: m.name, description: m.description, distance: cosineDistance(query, m.embedding)}))
 		.sort((a, b) => a.distance - b.distance)
-		.slice(0, limit)
+		.slice(0, limit);
-		.map(s => s.ref);
+}
+
+/** Re-embed a node's title / description / body fields. Description embedding stays the KD-tree index key. */
+async function embedMemoryFields(node: Memory, llm: any): Promise<void> {
+	const body = stripHeader(node.content);
+	const [titleE] = await llm.embedding(node.name.split('/').pop() || node.name);
+	const [descE] = await llm.embedding(node.description || '');
+	const bodyChunks = body ? await llm.embedding(body) : [];
+	if (titleE) node.titleEmbedding = titleE.embedding;
+	if (descE) node.embedding = descE.embedding;
+	node.bodyEmbeddings = bodyChunks.map((c: any) => c.embedding).filter(Boolean);
 }
 
 export function stripHeader(content: string): string {
@@ -67,6 +85,8 @@ export function stripHeader(content: string): string {
 
 export class MemoryCache {
 	private tree!: KDTree<MemoryRef>;
+	/** Tracks which memories are currently indexed in the tree, keyed by name -> embedding reference */
+	private indexed = new Map<string, number[]>();
 	public memories: Memory[];
 	public nodes: MemoryNode[] = [];
 
@@ -74,37 +94,48 @@ export class MemoryCache {
 
 	constructor(memories: Memory[]) {
 		this.memories = memories;
+		this.tree = new KDTree<MemoryRef>(0);
 		this.rebuild();
 	}
 
-	private buildTree(): KDTree<MemoryRef> {
+	/** Incrementally sync the KD tree against `this.memories` instead of rebuilding from scratch */
-		const embedded = this.memories.filter(m => m.embedding?.length);
+	private syncTree(): void {
-		if (!embedded.length) return new KDTree<MemoryRef>(0);
+		const current = new Set(this.memories.map(m => m.name));
 
-		const dims = embedded[0].embedding.length;
+		for (const [name, emb] of [...this.indexed]) {
-		const points: KDPoint<MemoryRef>[] = embedded.map(m => ({
+			const mem = this.memories.find(m => m.name === name);
-			vector: m.embedding,
+			if (!mem || !current.has(name) || mem.embedding !== emb) {
-			payload: {name: m.name, description: m.description},
+				this.tree.remove(p => p.name === name);
-		}));
+				this.indexed.delete(name);
+			}
+		}
 
-		return new KDTree<MemoryRef>(dims, 'cosine', points);
+		for (const mem of this.memories) {
+			if (!mem.embedding?.length || this.indexed.has(mem.name)) continue;
+			if (this.tree.dims === 0) this.tree = new KDTree<MemoryRef>(mem.embedding.length, 'cosine');
+			if (mem.embedding.length !== this.tree.dims) continue; // guard against embedding model/dim drift
+			this.tree.insert({vector: mem.embedding, payload: {name: mem.name, description: mem.description}});
+			this.indexed.set(mem.name, mem.embedding);
+		}
+
+		if (this.tree.tombstoneRatio > TREE_TOMBSTONE_LIMIT) this.tree.rebalance();
 	}
 
 	search(query: number[], limit: number): MemoryRef[] {
 		if (!this.tree || this.tree.dims === 0) return [];
-		return this.tree.knn(query, limit).map(r => r.point.payload);
+		return this.tree.knn(query, limit).map(r => ({...r.point.payload, distance: r.distance}));
 	}
 
 	add(memory: Memory): void {
 		this.memories.push(memory);
-		this.rebuild();
+		this.rebuild([memory]);
 	}
 
 	update(memory: Memory): void {
-		const idx = this.memories.findIndex(m => m.name === memory.name);
+		const existing = this.memories.find(m => m.name === memory.name);
-		if (idx !== -1) this.memories[idx] = memory;
+		if (existing) Object.assign(existing, memory);
 		else this.memories.push(memory);
-		this.rebuild();
+		this.rebuild([existing ?? memory]);
 	}
 
 	remove(name: string): void {
@@ -115,9 +146,11 @@ export class MemoryCache {
 		}
 	}
 
-	rebuild(): void {
+	rebuild(changed?: Memory[]): void {
-		this.nodes = rebuildGraph(this.memories);
+		this.nodes = (changed?.length && this.nodes.length)
-		this.tree = this.buildTree();
+			? patchGraph(this.memories, this.nodes, changed)
+			: rebuildGraph(this.memories);
+		this.syncTree();
 	}
 }
 
@@ -134,9 +167,9 @@ class MemoryAccessor {
 		return this.list.find(m => m.name === name);
 	}
 
-	commit(): MemoryNode[] {
+	commit(changed?: Memory[]): MemoryNode[] {
 		if (this.cache) {
-			this.cache.rebuild();
+			this.cache.rebuild(changed);
 			return this.cache.nodes;
 		}
 		return rebuildGraph(this.list);
@@ -147,6 +180,7 @@ class MemoryAccessor {
 		return nodes.filter(n => n.missing).map(n => n.name);
 	}
 
+	/** Cache path uses the KD tree's knn(); raw-array path (no cache available) falls back to a linear cosine scan */
 	search(vector: number[], limit: number): MemoryRef[] {
 		return this.cache ? this.cache.search(vector, limit) : cosineSearch(vector, this.list, limit);
 	}
@@ -162,10 +196,7 @@ class MemoryAccessor {
 	async backfillEmbeddings(llm: any): Promise<number> {
 		const missing = this.list.filter(m => !m.embedding?.length);
 		if (!missing.length) return 0;
-		await Promise.all(missing.map(async node => {
+		await Promise.all(missing.map(node => embedMemoryFields(node, llm)));
-			const [e] = await llm.embedding(node.content);
-			if (e) node.embedding = e.embedding;
-		}));
 		this.commit();
 		return missing.length;
 	}
@@ -185,13 +216,13 @@ export type MemoryOptions = {
 }
 
 export class MemoryManager {
-	private recentlyTouched = new Map<string, number>();
+	private mergeLock: Promise<any> = Promise.resolve();
-
 	private queues = new Map<string, {
 		dirty: boolean,
 		request: {abort?: () => void} | null,
 		task: Promise<void>,
 	}>();
+	private recentlyTouched = new Map<string, number>();
 
 	tools = {
 		forget: (memories: Memory[] | MemoryCache): AiTool => ({
@@ -251,14 +282,13 @@ ${m.content}
 		return new MemoryAccessor(memories);
 	}
 
-	private appendFacts(node: Memory, facts: string[]): void {
+	private stage(node: Memory, block: string): void {
 		this.ensureDoc(node);
 		const body = stripHeader(node.content);
-		const bullets = facts.map(f => `- ${f}`).join('\n');
+		const idx = body.indexOf(PENDING_HEADING);
-		const idx = body.indexOf(FACTS_HEADING);
 		const newBody = idx === -1
-			? `${body.trimEnd()}\n\n${FACTS_HEADING}\n${bullets}\n`
+			? `${body.trimEnd()}\n\n${PENDING_HEADING}\n${block}\n`
-			: `${body.slice(0, idx + FACTS_HEADING.length)}\n${bullets}${body.slice(idx + FACTS_HEADING.length)}`;
+			: `${body.slice(0, idx + PENDING_HEADING.length)}\n${block}${body.slice(idx + PENDING_HEADING.length)}`;
 		node.content = this.touchHeader(node, newBody);
 	}
 
@@ -268,42 +298,92 @@ ${m.content}
 		node.content = this.touchHeader(node, `# ${title}\n`);
 	}
 
-	private async factAgent(conversation: string, store: MemoryAccessor, options: LLMRequest, weekKey: string): Promise<FactBucket[]> {
+	private sanitizeDescription(text: string): string {
+		return (text ?? '').replace(/\s+/g, ' ').trim().slice(0, 240);
+	}
+
+	private relink(memories: Memory[], from: string, to: string): void {
+		const pattern = new RegExp(`\\[\\[${escapeRegex(from)}\\]\\]`, 'g');
+		for (const m of memories) if (pattern.test(m.content)) m.content = m.content.replace(pattern, `[[${to}]]`);
+	}
+
+	private normalizeLeaf(name: string): string {
+		return name.trim().toLowerCase().replace(/\s+/g, ' ');
+	}
+
+	/**
+	 * Resolve a fact-agent proposed subject to an existing node when it's an alias/rename of one.
+	 * Exact match is checked first (cheap, and covers the common case since node names are
+	 * already normalized at creation time). Only falls through to fuzzy alias matching against
+	 * same-root candidates when there's no existing hit — i.e. only on likely-new-doc creation.
+	 */
+	private resolveSubject(subject: string, store: MemoryAccessor): string {
+		const trimmed = subject.trim();
+		const exact = store.find(trimmed);
+		if (exact) return exact.name;
+
+		const normalized = this.normalizeLeaf(trimmed);
+		const caseInsensitive = store.list.find(m => this.normalizeLeaf(m.name) === normalized);
+		if (caseInsensitive) return caseInsensitive.name;
+
+		const root = trimmed.split('/')[0];
+		const leaf = trimmed.split('/').slice(1).join('/') || trimmed;
+		const candidates = store.list.filter(m => m.name.split('/')[0] === root && m.name !== trimmed);
+		if (!candidates.length) return trimmed;
+
+		const leaves = candidates.map(m => m.name.split('/').slice(1).join('/') || m.name);
+		const probe = leaves.length > 1 ? leaves : [...leaves, ''];
+		const {max, similarities} = this.llm.fuzzyMatch(leaf, ...probe);
+		if (max >= ALIAS_MATCH_THRESHOLD) return candidates[similarities.indexOf(max)].name;
+
+		return trimmed;
+	}
+
+	private async factAgent(conversation: string, store: MemoryAccessor, options: LLMRequest): Promise<FactAgentResult> {
 		const ghosts = store.ghosts();
 
 		const response = await this.llm.ask(conversation, {
 			model: options.model,
 			temperature: 0.2,
-			system: `You are a fact extractor to build obsidian knowledge vaults.
+			system: `Extract durable memory from this conversation
-Analyze this conversation and extract facts worth remembering long-term.
 
-Rules:
+1. Journal recap
-- Always extract facts that the user explicitly told you to remember
+- Brief "Captain's Log" of what happened, including useful context, decisions, or events
-- ONLY extract current facts the USER explicitly stated about themselves, their work, projects or decisions that were MADE during this conversation
+- Leave empty for trivial exchanges
-- DO NOT extract greetings, pleasantries, or generic exchanges
-- DO NOT extract deltas or changes in facts; ONLY the end fact
-- DO NOT extract anything the AI/assistant itself said
-- If nothing worth remembering was said, return an empty buckets array
 
-When extracting facts, you MUST also decide the exact destination path:
+2. Fact buckets
-- Reuse node names (including ghost) as much as possible IF the facts belongs there
+- Extract only durable facts explicitly stated by the USER
-- All information primarily about the user should go under "People/User"
+- Record the final/end state, not intermediate changes
-- When required, create a new path following collection/subject format (e.g., People/Sarah, Projects/Oxide) — you are not limited to any fixed list of collections, use whatever fits
+- Do not extract assistant claims, guesses, greetings, or temporary conversation details
-- For journal entries, use "Journal"
+
+For each fact, identify its HOME ENTITY:
+- The HOME ENTITY name should always be a [abstract|pro]noun
+- The grammatical subject/owner of the fact is the strongest clue
+- Prefer an existing entity over creating a new one
+- A document represents a persistent entity, not a topic, feature, bug, event, decision, setting, or conversation fragment
+- Put project facts under the project they belong to, person facts under the person, etc
+- New child entities are appropriate only when they are themselves distinct persistent entities
+
+Example Paths:
+- Projects/[Name]
+- People/[Name]
+- History/[Name]
+- Science/[Name]
+- [Subject]/[Name]
+- Class/[Name]/[Child]
+
+Use [[WikiLinks]] to express relationships between entities. NEVER create documents just to hold relationships
+Keep journal material in the journal; don't turn journal events into entities unless they represent something persistent
 
 Available nodes:
-- Journal
+${this.listNodes(store.list).map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}
-${this.listNodes(store.list).filter(n => !n.name.includes('Journal')).map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}
 ${ghosts.length ? `${ghosts.map(g => `- ${g}: (Ghost)`).join('\n')}` : ''}`,
 			schema: {
-				buckets: {type: 'array', description: 'Groups of facts to remember, each assigned to a different node. Return an empty array if there is nothing worth storing in an obsidian vault', items: {
+				journal: {type: 'string', description: 'Short day-to-day recap; empty if nothing happened.', required: false},
+				buckets: {type: 'array', description: 'Groups of facts to remember; empty array if nothing worth storing.', items: {
 						type: 'object', items: {
-							subject: {type: 'string', description: 'Exact existing node name OR new path (e.g. "People/Sarah", "Projects/Oxide"), or "Journal"', required: true},
+							subject: {type: 'string', description: 'Exact node name or new persistent entity path', required: true},
-							facts: {
+							facts: {type: 'array', description: 'Facts to store here', items: {type: 'string'}},
-								type: 'array',
-								description: 'Facts to store at this destination',
-								items: {type: 'string', description: 'A single fact'},
-							},
 						},
 					},
 				},
@@ -312,14 +392,16 @@ ${ghosts.length ? `${ghosts.map(g => `- ${g}: (Ghost)`).join('\n')}` : ''}`,
 
 		const buckets = new Map<string, string[]>();
 		for (const bucket of response.buckets ?? []) {
-			const subject = bucket.subject.trim().toLowerCase() === 'journal'
+			const subject = bucket.subject.trim();
-				? `Journal/${weekKey}` : bucket.subject.trim();
 			const facts = buckets.get(subject) ?? [];
 			facts.push(...dedupeFacts(bucket.facts));
 			buckets.set(subject, facts);
 		}
 
-		return buckets.entries().toArray().map(([subject, facts]) => ({subject, facts}));
+		return {
+			buckets: buckets.entries().toArray().map(([subject, facts]) => ({subject, facts})),
+			journal: (response.journal ?? '').trim(),
+		};
 	}
 
 	private getWeekMonday(date: Date = new Date()): string {
@@ -334,6 +416,36 @@ ${ghosts.length ? `${ghosts.map(g => `- ${g}: (Ghost)`).join('\n')}` : ''}`,
 		return memories.map(m => ({name: m.name, description: m.description}));
 	}
 
+	/** Finds the closest merge candidate via the KD tree's knn() instead of a manual O(n) cosine scan */
+	private async checkMerge(node: Memory, memories: Memory[] | MemoryCache, options: LLMRequest, threshold = MERGE_THRESHOLD): Promise<Memory | null> {
+		if (!node.embedding?.length || node.name.startsWith('Journal/')) return null;
+		const store = this.access(memories);
+
+		const candidate = store.search(node.embedding, 5)
+			.find(r => r.name !== node.name && !r.name.startsWith('Journal/') && r.distance !== undefined && r.distance <= threshold);
+		if (!candidate) return null;
+		const closest = store.find(candidate.name);
+		if (!closest) return null;
+
+		const result = await this.mergeAgent(node, closest, options);
+		const merged: Memory = {name: result.name, description: this.sanitizeDescription(result.description), content: '', embedding: [], links: [], backlinks: []};
+		merged.content = this.touchHeader(merged, result.content);
+		await embedMemoryFields(merged, this.llm);
+
+		this.relink(store.list, node.name, merged.name);
+		this.relink(store.list, closest.name, merged.name);
+
+		this.queues.get(closest.name)?.request?.abort?.();
+		this.queues.delete(closest.name);
+
+		store.forget(node.name);
+		store.forget(closest.name);
+		store.list.push(merged);
+		store.commit();
+
+		return merged;
+	}
+
 	private reconcile(node: Memory, memories: Memory[] | MemoryCache, options: LLMRequest): Promise<void> {
 		const key = node.name;
 		const existing = this.queues.get(key);
@@ -347,18 +459,25 @@ ${ghosts.length ? `${ghosts.map(g => `- ${g}: (Ghost)`).join('\n')}` : ''}`,
 		this.queues.set(key, entry);
 		const store = this.access(memories);
 		entry.task = (async () => {
+			let current = node, merged = false;
+			try {
 				do {
 					entry.dirty = false;
-				await this.docAgent(node, store.list, options, entry);
+					await this.docAgent(current, store.list, options, entry);
+					this.mergeLock = this.mergeLock.then(() => this.checkMerge(current, memories, options));
+					const result = await this.mergeLock;
+					if (result) { current = result; merged = true; }
 				} while (entry.dirty);
-		})().finally(() => {
+			} finally {
+				store.commit(merged ? undefined : [node]);
 				this.queues.delete(key);
-			store.commit();
+			}
-		});
+		})();
 		return entry.task;
 	}
 
 	private async docAgent(node: Memory, memories: Memory[], options: LLMRequest, entry: {request: {abort?: () => void} | null}): Promise<void> {
+		if(!memories.includes(node)) return;
 		const currentBody = stripHeader(node.content);
 		let update;
 		try {
@@ -367,27 +486,25 @@ ${ghosts.length ? `${ghosts.map(g => `- ${g}: (Ghost)`).join('\n')}` : ''}`,
 					model: options.model,
 					temperature: 0.3,
 					schema: {
-						description: {type: 'string', description: 'One-line description of what this document covers, no formatting or emojis', required: true},
+						description: {type: 'string', description: 'One factual sentence describing the document\'s ENTIRE SUBJECT MATTER — for use as a search/merge fingerprint', required: true},
 						content: {type: 'string', description: 'Rewritten document body in markdown, without the frontmatter block', required: true},
 					},
-					system: `You are a knowledge base editor maintaining one document in an Obsidian-style vault.
+					system: `You maintain one persistent knowledge-base document
 
-If the document has a "${FACTS_HEADING}" section, integrate every bullet under it into the appropriate part of the document, then remove the "${FACTS_HEADING}" section entirely. If there is no such section, just tidy the document per the rules below.
+Rewrite the ENTIRE document, folding "## Pending" into the existing content. Remove the Pending section when finished
 
-Structure: follow this generic shape loosely, adapting section names/order to what the content actually needs (e.g. journal-style docs may want a timeline instead of "Details"):
+Document design:
-\`\`\`markdown
+- The document represents one entity. Keep information about that entity together
-${GENERIC_TEMPLATE}
+- Let the structure fit the entity; there is NO fixed template
-\`\`\`
+- Preserve useful existing headings and organization. Don't redesign the document without reason
+- Add headings only when they meaningfully organize recurring information; don't create headings for one-off facts
+- Keep the document concise and information-dense without removing useful technical specifics
+- Current truth wins when facts conflict. Preserve older context only when it adds useful meaning
+- Use [[WikiLinks]] for specific related entities; don't create redundant content for linked entities
+- Avoid generic filler sections such as Notes, Miscellaneous, Recent, Updates, or Conversation
+- No frontmatter, preamble, filler, or AI commentary
 
-Formatting rules:
+Available nodes to link to:
-- Use Obsidian-style markdown: # headings, **bold** for emphasis, bullet & numbered lists for grouped 1D data, tables for 2D data
-- Link related concepts with [[WikiLink]] notation using full paths like [[People/Sarah]] or [[Projects/Website]]
-- Create links for specific entities (person, place, project, program) and abstract concepts, but skip generics (car, red, dog)
-- Keep the document concise, factual, and human-readable
-- Resolve contradictions: newer facts always win — delete the outdated statement entirely, never keep both
-- Do not add frontmatter blocks, filler, preamble, or AI commentary
-
-Other nodes in the vault (link to these instead of duplicating their content):
 ${this.listNodes(memories).filter(n => n.name !== node.name).map(n => n.name).join(', ') || 'none'}
 
 Current document:
@@ -406,10 +523,45 @@ ${currentBody}
 		}
 
 		if (!update?.content) return;
-		node.description = node.name !== 'People/User' ? update.description : 'All information about the current user';
+		node.description = node.name !== 'People/User' ? this.sanitizeDescription(update.description) : 'All information about the current user';
 		node.content = this.touchHeader(node, update.content);
-		const [e] = await this.llm.embedding(node.content);
+		await embedMemoryFields(node, this.llm);
-		if (e) node.embedding = e.embedding;
+	}
+
+	private async mergeAgent(a: Memory, b: Memory, options: LLMRequest): Promise<{name: string, description: string, content: string}> {
+		const modifiedOf = (m: Memory) => this.parseFrontmatter(m.content).fm.get('modified') || 'unknown';
+
+		return this.llm.ask('', {
+			model: options.model,
+			temperature: 0.3,
+			schema: {
+				name: {type: 'string', description: 'Canonical path for the merged entity', required: true},
+				description: {type: 'string', description: 'One factual sentence describing the merged document\'s subject matter', required: true},
+				content: {type: 'string', description: 'Fully reconciled body in markdown, without frontmatter', required: true},
+			},
+			system: `Determine whether these two documents represent the SAME persistent entity.
+
+Similarity of subject matter is NOT enough. Do not merge documents merely because they discuss the same project, person, technology, topic, or related work.
+
+Merge only when the evidence indicates they are duplicate identities, aliases, renamed entities, or two documents accidentally created for the same real-world entity. If they are distinct entities, they must remain separate.
+
+If they are the same entity:
+- Choose the canonical/most established path.
+- Combine their information into one document and remove duplication.
+- Preserve useful structure, technical specifics, history, and [[WikiLinks]].
+- Prefer newer information when facts conflict.
+- Return the canonical entity name and the fully reconciled document.
+
+Document A ("${a.name}", last modified ${modifiedOf(a)}):
+\`\`\`markdown
+${stripHeader(a.content)}
+\`\`\`
+
+Document B ("${b.name}", last modified ${modifiedOf(b)}):
+\`\`\`markdown
+${stripHeader(b.content)}
+\`\`\``,
+		});
 	}
 
 	private parseFrontmatter(content: string): {fm: Map<string, string>, body: string} {
@@ -419,21 +571,30 @@ ${currentBody}
 		for (const line of match[1].split('\n')) {
 			const i = line.indexOf(':');
 			if (i === -1) continue;
-			fm.set(line.slice(0, i).trim(), line.slice(i + 1).trim());
+			const key = line.slice(0, i).trim();
+			const raw = line.slice(i + 1).trim();
+			let value = raw;
+			try { value = JSON.parse(raw); } catch { /* legacy unquoted value, keep raw */ }
+			fm.set(key, value);
 		}
 		return {fm, body: match[2]};
 	}
 
+	/**
+	 * Writes the code-owned frontmatter block. `body` is passed through stripHeader() first so a
+	 * model that ignores instructions and hallucinates its own `---` block can never corrupt or
+	 * duplicate the real frontmatter — the LLM only ever gets to influence the body.
+	 */
 	private touchHeader(node: Memory, body: string): string {
 		const {fm} = this.parseFrontmatter(node.content);
 		fm.set('name', node.name);
 		fm.set('description', node.description || '');
 		fm.set('modified', new Date().toISOString());
-		return this.writeFrontmatter(fm, body);
+		return this.writeFrontmatter(fm, stripHeader(body));
 	}
 
 	private writeFrontmatter(fm: Map<string, string>, body: string): string {
-		const lines = [...fm.entries()].map(([k, v]) => `${k}: ${v}`);
+		const lines = [...fm.entries()].map(([k, v]) => `${k}: ${JSON.stringify(String(v).replace(/\s+/g, ' ').trim())}`);
 		return `---\n${lines.join('\n')}\n---\n\n${body.trimStart()}`;
 	}
 
@@ -452,6 +613,19 @@ ${currentBody}
 		return this.access(memories).forget(name);
 	}
 
+	/** Ranks a candidate pool by weighted title/description/body similarity against the query embedding */
+	private rankByFields(query: number[], candidates: Memory[], limit: number): Memory[] {
+		const scored = candidates.map(m => {
+			const titleSim = m.titleEmbedding?.length ? 1 - cosineDistance(query, m.titleEmbedding) : 0;
+			const descSim = m.embedding?.length ? 1 - cosineDistance(query, m.embedding) : 0;
+			const bodySim = m.bodyEmbeddings?.length
+				? Math.max(...m.bodyEmbeddings.map(b => 1 - cosineDistance(query, b)))
+				: 0;
+			return {memory: m, score: titleSim * 0.5 + descSim * 0.35 + bodySim * 0.15};
+		});
+		return scored.sort((a, b) => b.score - a.score).slice(0, limit).map(s => s.memory);
+	}
+
 	async recollect(query: string, memories: Memory[] | MemoryCache, limit = 5, graphDepth = 1): Promise<Memory[]> {
 		const store = this.access(memories);
 		if (!store.list.length) return [];
@@ -461,8 +635,11 @@ ${currentBody}
 		const [e] = await this.llm.embedding(query);
 		if (!e) return [];
 
-		const vectorResults = store.search(e.embedding, limit);
+		// Description embedding is the cheap ANN index key; pull a wider pool then re-rank by field weight
-		const found = new Set<string>(vectorResults.map(r => r.name));
+		const pool = store.search(e.embedding, Math.max(limit * 3, limit));
+		const poolMemories = pool.map(r => store.find(r.name)).filter((m): m is Memory => !!m);
+		const ranked = this.rankByFields(e.embedding, poolMemories, limit);
+		const found = new Set<string>(ranked.map(m => m.name));
 
 		if (graphDepth > 0) {
 			let frontier = [...found];
@@ -482,9 +659,9 @@ ${currentBody}
 			}
 		}
 
-		const vectorOrder = vectorResults.map(r => r.name);
+		const rankedOrder = ranked.map(m => m.name);
-		const graphExpansions = [...found].filter(n => !vectorOrder.includes(n));
+		const graphExpansions = [...found].filter(n => !rankedOrder.includes(n));
-		return [...vectorOrder, ...graphExpansions].map(n => store.find(n)!).filter(Boolean);
+		return [...rankedOrder, ...graphExpansions].map(n => store.find(n)!).filter(Boolean);
 	}
 
 	async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest): Promise<Memory[]> {
@@ -498,26 +675,40 @@ ${currentBody}
 		history.push(pending);
 
 		const store = this.access(memories);
-		const buckets = await this.factAgent(conversation, store, options, this.getWeekMonday());
+		const {buckets, journal} = await this.factAgent(conversation, store, options);
 		const touched: Memory[] = [];
 
+		if (journal) {
+			const journalName = `Journal/${this.getWeekMonday()}`;
+			let jnode = store.find(journalName);
+			if (!jnode) {
+				jnode = {name: journalName, description: '', content: '', embedding: [], links: [], backlinks: []};
+				store.list.push(jnode);
+			}
+			this.stage(jnode, `### ${new Date().toISOString().slice(0, 10)}\n${journal}`);
+			touched.push(jnode);
+		}
+
 		for (const {subject, facts} of buckets) {
-			let node = store.find(subject);
+			const resolved = this.resolveSubject(subject, store);
+			let node = store.find(resolved);
 			if (!node) {
-				node = {name: subject, description: '', content: '', embedding: [], links: [], backlinks: []};
+				node = {name: resolved, description: '', content: '', embedding: [], links: [], backlinks: []};
 				store.list.push(node);
 			}
-			this.appendFacts(node, facts);
+			this.stage(node, facts.map(f => `- ${f}`).join('\n'));
-			const [e] = await this.llm.embedding(node.content);
-			if (e) node.embedding = e.embedding;
-			this.touch(node.name);
 			touched.push(node);
 		}
 
+		await Promise.all(touched.map(async node => {
+			await embedMemoryFields(node, this.llm);
+			this.touch(node.name);
+		}));
+
 		if (touched.length) {
-			store.commit();
+			store.commit(touched);
 			(pending as any).content = `Saved to ${touched.map(n => `[[${n.name}]]`).join(', ')}`;
-			await Promise.all(touched.map(node => this.reconcile(node, memories, options).catch(() => {})));
+			Promise.all(touched.map(node => this.reconcile(node, memories, options).catch(() => {})));
 		} else {
 			(pending as any).content = 'Nothing worth remembering.';
 		}
@@ -526,9 +717,9 @@ ${currentBody}
 		return touched;
 	}
 
-	async reconcileVault(memories: Memory[] | MemoryCache, options: LLMRequest, scope: 'touched' | 'all' = 'touched'): Promise<void> {
+	async reconcileAll(memories: Memory[] | MemoryCache, options: LLMRequest, scope: 'touched' | 'all' = 'touched'): Promise<void> {
 		const store = this.access(memories);
-		const targets = scope === 'all' ? store.list : store.list.filter(m => m.content.includes(FACTS_HEADING));
+		const targets = scope === 'all' ? store.list : store.list.filter(m => m.content.includes(PENDING_HEADING));
 		await Promise.all(targets.map(node => this.reconcile(node, memories, options)));
 		store.commit();
 	}

src/open-ai.ts

@@ -25,6 +25,13 @@ export class OpenAi extends LLMProvider {
 		return client;
 	}
 
+	private toWireContent(content: any): any {
+		if(!Array.isArray(content)) return content;
+		return content.map(c => c.type === 'image'
+			? {type: 'image_url', image_url: {url: `data:${c.mime};base64,${c.data}`}}
+			: {type: 'text', text: c.text});
+	}
+
 	/** Convert standard history -> OpenAI wire format */
 	private toWire(history: LLMMessage[], system?: string): any[] {
 		const wire: any[] = [];
@@ -41,7 +48,7 @@ export class OpenAi extends LLMProvider {
 					content: h.error || h.content || '',
 				});
 			} else {
-				wire.push({role: h.role, content: h.content});
+				wire.push({role: h.role, content: this.toWireContent(h.content)});
 			}
 		}
 		return wire;
@@ -91,18 +98,21 @@ export class OpenAi extends LLMProvider {
 						throw err;
 					});
 
-					let usage: any, msg: any = {content: '', tool_calls: []};
+					let usage: any, finishReason: string | undefined, msg: any = {content: '', tool_calls: []};
 					if(options.stream) {
 						for await (const chunk of resp) {
 							if(controller.signal.aborted) break;
 							if(chunk.usage) usage = chunk.usage;
+							if(chunk.choices[0]?.finish_reason) finishReason = chunk.choices[0].finish_reason;
 							if(chunk.choices[0]?.delta?.content) {
 								msg.content += chunk.choices[0].delta.content;
 								options.stream({text: chunk.choices[0].delta.content});
 							}
 							if(chunk.choices[0]?.delta?.tool_calls) {
 								for(const deltaTC of chunk.choices[0].delta.tool_calls) {
-									const existing = msg.tool_calls.find((tc: any) => tc.index === deltaTC.index);
+									const existing = deltaTC.index != null
+										? msg.tool_calls.find((tc: any) => tc.index === deltaTC.index)
+										: (deltaTC.id ? msg.tool_calls.find((tc: any) => tc.id === deltaTC.id) : undefined);
 									if(existing) {
 										if(deltaTC.id) existing.id = deltaTC.id;
 										if(deltaTC.function?.name) existing.function.name = deltaTC.function.name;
@@ -119,11 +129,21 @@ export class OpenAi extends LLMProvider {
 						}
 					} else {
 						usage = resp.usage;
+						finishReason = resp.choices[0].finish_reason;
 						msg = resp.choices[0].message;
 					}
 					const duration = Date.now() - callStart;
 					const tps = usage?.completion_tokens && duration > 0 ? usage.completion_tokens / (duration / 1000) : 0;
 
+					if(finishReason === 'length' && !controller.signal.aborted) {
+						if(msg.content?.trim()) history.push({role: 'assistant', content: msg.content.trim(), timestamp: Date.now(), duration, tps});
+						throw new Error(`[OpenAI] Response hit token limit before completing`);
+					}
+
+					if(!finishReason && !controller.signal.aborted) {
+						throw new Error('[OpenAI] Stream ended prematurely - connection likely dropped');
+					}
+
 					const toolCalls = msg.tool_calls || [];
 					if(toolCalls.length && !controller.signal.aborted) {
 						if(msg.content?.trim()) history.push({role: 'assistant', content: msg.content.trim(), timestamp: Date.now(), duration, tps});
@@ -140,7 +160,7 @@ export class OpenAi extends LLMProvider {
 							if(!tool) { entry.error = 'Tool not found'; return; }
 							try {
 								const toolStream = options.stream && ((chunk: any) => {
-									if(chunk.done) { terminal = true; return; }
+									if(chunk.done) return;
 									options.stream!(chunk);
 								});
 								const result = await tool.fn(entry.args, toolStream, this.ai, tc.id);