Bump 1.2.8

Added Geo, FS and flarescraperr tools
More memory fixes
2026-07-29 22:35:29 -04:00 · 2026-07-29 22:34:51 -04:00 · 2026-07-29 22:11:09 -04:00 · 2026-07-27 22:47:48 -04:00 · 2026-07-27 22:39:06 -04:00 · 2026-07-27 22:34:36 -04:00
18 changed files with 3744 additions and 2626 deletions
--- a/README.md
+++ b/README.md
@@ -103,7 +103,125 @@ A TypeScript library that provides a unified interface for working with multiple

 ## Documentation

-[Available Here](https://ai-utils.docs.zakscode.com/)
+### Setup
+```javascript
+const ai = new Ai({
+    path: '/ai-models',
+    
+    // Setup audio
+    whisper: '/path/to/binary', // Required for ASR
+    hfToken: '...', // Required for diarization
+    asr: 'ggml-base.en.bin', // Override default ASR model
+    
+    // Setup LLM
+    embedder: 'bge-small-en-v1.5', // Override default embedder model
+    llm: {
+        system: 'You are a helpful assistant.',
+        compress: {max: 90_000, min: 50_000}, // Compress chat history to min tokens when max is reached
+        temperature: 0.8,
+        max_tokens: 100_000,
+        memoryModel: 'gpt-4o', // Cheap model for managing memories in background, defaults to current model
+        models: {
+            'claude-3-5-sonnet': {proto: 'anthropic', token: process.env.ANTHROPIC_TOKEN},
+            'gpt-4o':            {proto: 'openai',    token: process.env.OPENAI_TOKEN},
+            'llama3':            {proto: 'ollama',    host: 'http://localhost:11434'},
+        },
+        mcp: [
+            {name: 'files', url: 'https://mcp.example.com', token: process.env.MCP_TOKEN}
+        ],
+        skills: [
+            {name: 'Tone of voice', description: 'Brand writing guidelines', content: '# Tone of Voice\n\nAlways be concise and friendly...'}
+        ],
+        tools: [{
+            name: 'Marco?',
+            description: 'Where is marco polo?',
+            args: {
+                shout: {type: 'boolean', default: 'Shout into the void?', description: false, required: false}
+            },
+            fn: (args: any, stream: LLMRequest['stream'], ai: Ai) => {
+                const {shout} = args;
+                return shout ? 'Polo!' : 'Polo';
+            }
+        }],
+    },
+	
+    // Setup Vision
+    ocr: 'eng' // Override default OCR model
+});
+
+```
+
+### Audio
+
+```javascript
+// Crate audio transcript
+const text = await ai.audio.asr('./path/to/audio.mp3');
+console.log(text);
+
+// Break transcript into speakers
+const text = await ai.audio.asr('./path/to/audio.mp3', {diarization: true});
+console.log(text);
+
+// Break transcript into named speakers
+const text = await ai.audio.asr('./path/to/audio.mp3', {diarization: 'llm'});
+console.log(text);
+```
+
+### Language
+
+```javascript
+const history = [], memory = [];
+
+// Wait for entire response
+const text = await ai.language.ask('My favorite color is blue, whats yours?', {history, memory});
+console.log(text);
+
+// Stream response
+const chunks = '';
+await ai.language.ask('Write me a poem', {
+	history, memory,
+	stream: chunk => chunks += chunk,
+});
+console.log(chunks);
+
+// Manually compile history into memories at end of conversation
+// Happens automatically when coverstaions are compressed
+await ai.language.updateMemory(history, memory);
+
+// Summarize text
+const summary = await ai.language.summarize(longText, 200);
+
+// Code response (no conversation or extra BS)
+const code = await ai.language.code('Write a fibonacci function');
+
+// Structured JSON response
+const data = await ai.language.json('Extract the name and age', `{
+    "name": "string",
+    "age": "number"
+}`, {system: 'Extract from user input'});
+```
+
+#### Premade LLM Tools:
+- `cli`: Run a shell command, returns its output
+- `get_datetime`: Returns local date/time
+- `get_datetime_utc`: Returns current UTC date/time
+- `exec`: Execute code in cli, node, or python
+- `fetch`: Make HTTP requests (GET/POST/PUT/DELETE)
+- `exec_javascript`: Execute CommonJS JavaScript
+- `exec_python`: Execute Python via python -c
+- `read_webpage`: Scrape & clean content from a URL, handles HTML, JSON, CSV, media, PDFs etc.
+- `web_search`: Anonymous DuckDuckGo search, returns a list of URLs
+- `wikipedia_lookup`: Fetch a Wikipedia article (intro or full)
+- `wikipedia_search`: Search Wikipedia and return matching articles
+- `get_weather`: Fetch current weather + forecast for a location (just built!)
+
+### Vision
+
+```javascript
+// Extract text from image
+const text = await ai.vision.ocr('./path/to/image.png');
+console.log(text);
+```

 ## License

--- a/package-lock.json
+++ b/package-lock.json
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "0.9.0",
+	"version": "1.2.8",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",
@@ -25,21 +25,21 @@
 		"watch": "npx vite build --watch"
 	},
 	"dependencies": {
-		"@anthropic-ai/sdk": "^0.78.0",
+		"@anthropic-ai/sdk": "^0.102.0",
 		"@tensorflow/tfjs": "^4.22.0",
-		"@xenova/transformers": "^2.17.2",
+		"@huggingface/transformers": "^4.2.0",
 		"@ztimson/node-utils": "^1.0.7",
-		"@ztimson/utils": "^0.28.16",
+		"@ztimson/utils": "^0.29.4",
 		"cheerio": "^1.2.0",
-		"openai": "^6.22.0",
+		"openai": "^6.42.0",
 		"tesseract.js": "^7.0.0"
 	},
 	"devDependencies": {
-		"@types/node": "^24.8.1",
+		"@types/node": "^24.13.1",
 		"typedoc": "^0.26.7",
-		"typescript": "^5.3.3",
-		"vite": "^7.2.7",
-		"vite-plugin-dts": "^4.5.3"
+		"typescript": "^5.6.3",
+		"vite": "^8.0.16",
+		"vite-plugin-dts": "^5.0.2"
 	},
 	"files": [
 		"dist"
--- a/src/ai.ts
+++ b/src/ai.ts
@@ -1,5 +1,5 @@
 import * as os from 'node:os';
-import LLM, {AnthropicConfig, OllamaConfig, OpenAiConfig, LLMRequest} from './llm';
+import LLM, {AnthropicConfig, OpenAiConfig, LLMRequest} from './llm';
 import { Audio } from './audio.ts';
 import {Vision} from './vision.ts';

@@ -8,7 +8,7 @@ export type AbortablePromise<T> = Promise<T> & {
 };

 export type AiOptions = {
-	/** Token to pull models from hugging face */
+	/** Token to pull diarization models from hugging face */
 	hfToken?: string;
 	/** Path to models */
 	path?: string;
@@ -18,7 +18,7 @@ export type AiOptions = {
 	embedder?: string;
 	/** Large language models, first is default */
 	llm?: Omit<LLMRequest, 'model'> & {
-		models: {[model: string]: AnthropicConfig | OllamaConfig | OpenAiConfig};
+		models: {[model: string]: AnthropicConfig | OpenAiConfig};
 	}
 	/** OCR model: eng, eng_best, eng_fast */
 	ocr?: string;
--- a/src/antrhopic.ts
+++ b/src/antrhopic.ts
@@ -3,6 +3,7 @@ import {findByProp, objectMap, JSONSanitize, JSONAttemptParse} from '@ztimson/ut
 import {AbortablePromise, Ai} from './ai.ts';
 import {LLMMessage, LLMRequest} from './llm.ts';
 import {LLMProvider} from './provider.ts';
+import {convertSchema} from './tools.ts';

 export class Anthropic extends LLMProvider {
 	client!: anthropic;
@@ -48,7 +49,7 @@ export class Anthropic extends LLMProvider {
 		return history.map(({timestamp, ...h}) => h);
 	}

-	ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
+	ask(message: string, options: LLMRequest = {}): AbortablePromise<string | any> {
 		const controller = new AbortController();
 		return Object.assign(new Promise<any>(async (res) => {
 			let history = this.fromStandard([...options.history || [], {role: 'user', content: message, timestamp: Date.now()}]);
@@ -57,7 +58,7 @@ export class Anthropic extends LLMProvider {
 				model: options.model || this.model,
 				max_tokens: options.max_tokens || this.ai.options.llm?.max_tokens || 4096,
 				system: options.system || this.ai.options.llm?.system || '',
-				temperature: options.temperature || this.ai.options.llm?.temperature || 0.7,
+				temperature: options.temperature || this.ai.options.llm?.temperature || undefined,
 				tools: tools.map(t => ({
 					name: t.name,
 					description: t.description,
@@ -72,6 +73,16 @@ export class Anthropic extends LLMProvider {
 				stream: !!options.stream,
 			};

+			// Add structured output support
+			if(options.schema) {
+				requestParams.output_config = {
+					format: {
+						type: 'json_schema',
+						schema: convertSchema(options.schema)
+					}
+				};
+			}
+
 			let resp: any, isFirstMessage = true;
 			do {
 				resp = await this.client.messages.create(requestParams).catch(err => {
@@ -128,12 +139,17 @@ export class Anthropic extends LLMProvider {
 					requestParams.messages = history;
 				}
 			} while (!controller.signal.aborted && resp.content.some((c: any) => c.type === 'tool_use'));
-			history.push({role: 'assistant', content: resp.content.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n')});
+
+			const textContent = resp.content.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n');
+			history.push({role: 'assistant', content: textContent});
 			history = this.toStandard(history);

 			if(options.stream) options.stream({done: true});
 			if(options.history) options.history.splice(0, options.history.length, ...history);
-			res(history.at(-1)?.content);
+
+			// Return parsed JSON if schema provided
+			const finalContent = history.at(-1)?.content;
+			res(options.schema ? JSONAttemptParse(finalContent, finalContent) : finalContent);
 		}), {abort: () => controller.abort()});
 	}
 }
--- a/src/audio.ts
+++ b/src/audio.ts
@@ -2,7 +2,6 @@ import {execSync, spawn} from 'node:child_process';
 import {mkdtempSync} from 'node:fs';
 import fs from 'node:fs/promises';
 import {tmpdir} from 'node:os';
-import * as path from 'node:path';
 import Path, {join} from 'node:path';
 import {AbortablePromise, Ai} from './ai.ts';

@@ -142,11 +141,18 @@ print(json.dumps(segments))
 		if(!llm) return transcript;
 		let chunks = this.ai.language.chunk(transcript, 500, 0);
 		if(chunks.length > 4) chunks = [...chunks.slice(0, 3), <string>chunks.at(-1)];
-		const names = await this.ai.language.json(chunks.join('\n'), '{1: "Detected Name", 2: "Second Name"}', {
-			system: 'Use the following transcript to identify speakers. Only identify speakers you are positive about, dont mention speakers you are unsure about in your response',
+		await this.ai.language.ask(chunks.join('\n'), {
+			system: 'Read the following transcript and attempt to identify every speaker. For every positively identified speaker, call the \`identify\` tool with the speaker\'s ID number & the identified name exactly once.',
 			temperature: 0.1,
+			tools: [
+				{name: 'identify', description: 'Identify a speaker', args: {
+					speaker: {type: 'number', description: 'Speaker number', required: true},
+					name: {type: 'string', description: 'Inferred name', required: true},
+				}, fn: ({speaker, name}) => {
+					transcript = transcript.replaceAll(`[Speaker ${speaker}]`, `[${name}]`);
+				}}
+			]
 		});
-		Object.entries(names).forEach(([speaker, name]) => transcript = transcript.replaceAll(`[Speaker ${speaker}]`, `[${name}]`));
 		return transcript;
 	}

@@ -155,7 +161,7 @@ print(json.dumps(segments))
 		const p = new Promise<any>((resolve, reject) => {
 			this.downloadAsrModel(opts.model).then(m => {
 				if(opts.diarization) {
-					let output = path.join(path.dirname(file), 'transcript');
+					let output = join(Path.dirname(file), 'transcript');
 					proc = spawn(<string>this.ai.options.whisper,
 						['-m', m, '-f', file, '-np', '-ml', '1', '-oj', '-of', output],
 						{stdio: ['ignore', 'ignore', 'pipe']}
@@ -226,11 +232,11 @@ print(json.dumps(segments))
 		return <any>Object.assign(p, {abort});
 	}

-	asr(file: string, options: { model?: string; diarization?: boolean | 'llm' } = {}): AbortablePromise<string | null> {
+	asr(path: string, options: { model?: string; diarization?: boolean | 'llm' } = {}): AbortablePromise<string | null> {
 		if(!this.ai.options.whisper) throw new Error('Whisper not configured');

 		const tmp = join(mkdtempSync(join(tmpdir(), 'audio-')), 'converted.wav');
-		execSync(`ffmpeg -i "${file}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' });
+		execSync(`ffmpeg -i "${path}" -ar 16000 -ac 1 -f wav "${tmp}"`, { stdio: 'ignore' });
 		const clean = () => fs.rm(Path.dirname(tmp), {recursive: true, force: true}).catch(() => {});

 		if(!options.diarization) return this.runAsr(tmp, {model: options.model});
--- a/src/embedder.ts
+++ b/src/embedder.ts
@@ -1,13 +1,13 @@
-import { pipeline } from '@xenova/transformers';
+import { pipeline } from '@huggingface/transformers';

 const [modelDir, model] = process.argv.slice(2);

 let text = '';
 process.stdin.on('data', chunk => text += chunk);
 process.stdin.on('end', async () => {
-	const embedder = await pipeline('feature-extraction', 'Xenova/' + model, {quantized: true, cache_dir: modelDir});
+	const embedder = await pipeline('feature-extraction', 'Xenova/' + model, {cache_dir: modelDir});
 	const output = await embedder(text, { pooling: 'mean', normalize: true });
 	const embedding = Array.from(output.data);
-	console.log(JSON.stringify({embedding}));
+	process.stdout.write(JSON.stringify({embedding}));
 	process.exit();
 });
--- a/src/index.ts
+++ b/src/index.ts
@@ -2,6 +2,9 @@ export * from './ai';
 export * from './antrhopic';
 export * from './audio';
 export * from './llm';
+export * from './memory';
+export * from './memory-cache';
+export * from './memory-graph';
 export * from './open-ai';
 export * from './provider';
 export * from './tools';
--- a/src/kd-tree.ts
+++ b/src/kd-tree.ts
@@ -0,0 +1,334 @@
+export type DistanceMetric = "euclidean" | "cosine";
+
+export interface KDPoint<T = unknown> {
+	vector: number[];
+	payload: T;
+}
+
+export interface KNNResult<T = unknown> {
+	point: KDPoint<T>;
+	distance: number;
+}
+
+interface KDNode<T> {
+	point: KDPoint<T>;
+	axis: number;
+	left: KDNode<T> | null;
+	right: KDNode<T> | null;
+}
+
+// ─── Distance helpers ─────────────────────────────────────────────────────────
+
+function euclidean(a: number[], b: number[]): number {
+	let sum = 0;
+	for (let i = 0; i < a.length; i++) {
+		const d = a[i] - b[i];
+		sum += d * d;
+	}
+	return Math.sqrt(sum);
+}
+
+function cosine(a: number[], b: number[]): number {
+	let dot = 0, normA = 0, normB = 0;
+	for (let i = 0; i < a.length; i++) {
+		dot   += a[i] * b[i];
+		normA += a[i] * a[i];
+		normB += b[i] * b[i];
+	}
+	const denom = Math.sqrt(normA) * Math.sqrt(normB);
+	return denom === 0 ? 1 : 1 - dot / denom; // distance = 1 - similarity
+}
+
+/**
+ * Keeps the k closest candidates in memory, evicts the furthest when full
+ */
+class BoundedMaxHeap<T> {
+	private heap: KNNResult<T>[] = [];
+
+	constructor(private readonly k: number) {}
+
+	get size(): number { return this.heap.length; }
+
+	get worstDistance(): number {
+		return this.heap.length < this.k ? Infinity : this.heap[0].distance;
+	}
+
+	push(item: KNNResult<T>): void {
+		if (this.heap.length < this.k) {
+			this.heap.push(item);
+			this.bubbleUp(this.heap.length - 1);
+		} else if (item.distance < this.heap[0].distance) {
+			this.heap[0] = item;
+			this.sinkDown(0);
+		}
+	}
+
+	toSortedArray(): KNNResult<T>[] {
+		return [...this.heap].sort((a, b) => a.distance - b.distance);
+	}
+
+	private bubbleUp(i: number): void {
+		while (i > 0) {
+			const parent = (i - 1) >> 1;
+			if (this.heap[parent].distance >= this.heap[i].distance) break;
+			[this.heap[parent], this.heap[i]] = [this.heap[i], this.heap[parent]];
+			i = parent;
+		}
+	}
+
+	private sinkDown(i: number): void {
+		const n = this.heap.length;
+		while (true) {
+			let largest = i;
+			const l = 2 * i + 1, r = 2 * i + 2;
+			if (l < n && this.heap[l].distance > this.heap[largest].distance) largest = l;
+			if (r < n && this.heap[r].distance > this.heap[largest].distance) largest = r;
+			if (largest === i) break;
+			[this.heap[largest], this.heap[i]] = [this.heap[i], this.heap[largest]];
+			i = largest;
+		}
+	}
+}
+
+/**
+ * K-D Tree for efficient nearest-neighbor search over high-dimensional vectors / embeddings.
+ *
+ * Supports:
+ *  - Insertion of labeled points
+ *  - k-nearest-neighbor (KNN) search
+ *  - Radius search (all points within a given distance)
+ *  - Euclidean and cosine distance metrics
+ *  - Bulk construction (balanced tree) for best query performance
+ */
+export class KDTree<T = unknown> {
+	private root: KDNode<T> | null = null;
+	private _size = 0;
+	private readonly dims: number;
+	private readonly distanceFn: (a: number[], b: number[]) => number;
+
+	/**
+	 * @param dims           Dimensionality of all vectors (must be consistent).
+	 * @param metric         Distance metric to use. Default: "euclidean".
+	 * @param points         Optional initial set of points. Builds a balanced tree
+	 *                       in O(n log² n) — prefer this over inserting one-by-one
+	 *                       when you have a large corpus.
+	 */
+	constructor(
+		dims: number,
+		metric: DistanceMetric = "euclidean",
+		points?: KDPoint<T>[]
+	) {
+		this.dims = dims;
+		this.distanceFn = metric === "cosine" ? cosine : euclidean;
+
+		if (points && points.length > 0) {
+			this.validateAll(points);
+			this.root = this.buildBalanced([...points], 0);
+			this._size = points.length;
+		}
+	}
+
+	/** Total number of points stored in the tree. */
+	get size(): number { return this._size; }
+
+	// ── Insertion ──────────────────────────────────────────────────────────────
+
+	/**
+	 * Insert a single point. O(log n) average, O(n) worst case on skewed data.
+	 * For bulk loading prefer passing points to the constructor.
+	 */
+	insert(point: KDPoint<T>): void {
+		this.validate(point);
+		this.root = this.insertNode(this.root, point, 0);
+		this._size++;
+	}
+
+	// ── KNN search ─────────────────────────────────────────────────────────────
+
+	/**
+	 * Find the k nearest neighbors to `query`.
+	 * Returns results sorted by distance ascending.
+	 */
+	knn(query: number[], k: number): KNNResult<T>[] {
+		if (k <= 0) throw new RangeError("k must be a positive integer");
+		this.validateVector(query);
+
+		const heap = new BoundedMaxHeap<T>(k);
+		this.searchKNN(this.root, query, k, heap, 0);
+		return heap.toSortedArray();
+	}
+
+	/**
+	 * Nearest single neighbor. Convenience wrapper around knn(query, 1).
+	 * Returns null if the tree is empty.
+	 */
+	nearest(query: number[]): KNNResult<T> | null {
+		const results = this.knn(query, 1);
+		return results[0] ?? null;
+	}
+
+	// ── Radius search ──────────────────────────────────────────────────────────
+
+	/**
+	 * Return all points whose distance to `query` is ≤ `radius`,
+	 * sorted by distance ascending.
+	 */
+	radiusSearch(query: number[], radius: number): KNNResult<T>[] {
+		if (radius < 0) throw new RangeError("radius must be non-negative");
+		this.validateVector(query);
+
+		const results: KNNResult<T>[] = [];
+		this.searchRadius(this.root, query, radius, results, 0);
+		results.sort((a, b) => a.distance - b.distance);
+		return results;
+	}
+
+	// ── Conversion ─────────────────────────────────────────────────────────────
+
+	/** Collect all points in the tree (order not guaranteed). */
+	toArray(): KDPoint<T>[] {
+		const out: KDPoint<T>[] = [];
+		this.collect(this.root, out);
+		return out;
+	}
+
+	/**
+	 * Rebuild the tree from its current points as a balanced tree.
+	 * Useful after many individual insertions to restore O(log n) query time.
+	 */
+	rebalance(): void {
+		const points = this.toArray();
+		this.root = points.length ? this.buildBalanced(points, 0) : null;
+	}
+
+	// ── Private: build ─────────────────────────────────────────────────────────
+
+	private buildBalanced(points: KDPoint<T>[], depth: number): KDNode<T> {
+		const axis = depth % this.dims;
+		points.sort((a, b) => a.vector[axis] - b.vector[axis]);
+
+		const mid = Math.floor(points.length / 2);
+		return {
+			point: points[mid],
+			axis,
+			left:  points.slice(0, mid).length
+				? this.buildBalanced(points.slice(0, mid), depth + 1)
+				: null,
+			right: points.slice(mid + 1).length
+				? this.buildBalanced(points.slice(mid + 1), depth + 1)
+				: null,
+		};
+	}
+
+	// ── Private: insert ────────────────────────────────────────────────────────
+
+	private insertNode(
+		node: KDNode<T> | null,
+		point: KDPoint<T>,
+		depth: number
+	): KDNode<T> {
+		if (node === null) {
+			return { point, axis: depth % this.dims, left: null, right: null };
+		}
+		const axis = depth % this.dims;
+		if (point.vector[axis] < node.point.vector[axis]) {
+			node.left = this.insertNode(node.left, point, depth + 1);
+		} else {
+			node.right = this.insertNode(node.right, point, depth + 1);
+		}
+		return node;
+	}
+
+	// ── Private: KNN traversal ─────────────────────────────────────────────────
+
+	private searchKNN(
+		node: KDNode<T> | null,
+		query: number[],
+		k: number,
+		heap: BoundedMaxHeap<T>,
+		depth: number
+	): void {
+		if (node === null) return;
+
+		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];
+		const [near, far] = diff <= 0
+			? [node.left, node.right]
+			: [node.right, node.left];
+
+		this.searchKNN(near, query, k, heap, depth + 1);
+
+		// Only explore the far side if it could contain a closer point.
+		// For cosine distance we can't prune by axis gap alone, so always explore.
+		const shouldExplore =
+			this.distanceFn === cosine
+				? true
+				: Math.abs(diff) < heap.worstDistance;
+
+		if (shouldExplore) {
+			this.searchKNN(far, query, k, heap, depth + 1);
+		}
+	}
+
+	// ── Private: radius traversal ──────────────────────────────────────────────
+
+	private searchRadius(
+		node: KDNode<T> | null,
+		query: number[],
+		radius: number,
+		results: KNNResult<T>[],
+		depth: number
+	): void {
+		if (node === null) return;
+
+		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];
+		const [near, far] = diff <= 0
+			? [node.left, node.right]
+			: [node.right, node.left];
+
+		this.searchRadius(near, query, radius, results, depth + 1);
+
+		const shouldExplore =
+			this.distanceFn === cosine ? true : Math.abs(diff) <= radius;
+
+		if (shouldExplore) {
+			this.searchRadius(far, query, radius, results, depth + 1);
+		}
+	}
+
+	// ── Private: collect ───────────────────────────────────────────────────────
+
+	private collect(node: KDNode<T> | null, out: KDPoint<T>[]): void {
+		if (node === null) return;
+		out.push(node.point);
+		this.collect(node.left, out);
+		this.collect(node.right, out);
+	}
+
+	// ── Private: validation ────────────────────────────────────────────────────
+
+	private validateVector(v: number[]): void {
+		if (v.length !== this.dims) {
+			throw new TypeError(
+				`Vector length ${v.length} does not match tree dimensionality ${this.dims}`
+			);
+		}
+	}
+
+	private validate(point: KDPoint<T>): void {
+		this.validateVector(point.vector);
+	}
+
+	private validateAll(points: KDPoint<T>[]): void {
+		for (const p of points) this.validate(p);
+	}
+}
--- a/src/llm.ts
+++ b/src/llm.ts
@@ -1,16 +1,15 @@
-import {sum} from '@tensorflow/tfjs';
-import {JSONAttemptParse} from '@ztimson/utils';
 import {AbortablePromise, Ai} from './ai.ts';
 import {Anthropic} from './antrhopic.ts';
+import {MemoryCache} from './memory-cache.ts';
 import {OpenAi} from './open-ai.ts';
 import {LLMProvider} from './provider.ts';
-import {AiTool} from './tools.ts';
+import {AiTool, AiToolArg} from './tools.ts';
 import {fileURLToPath} from 'url';
 import {dirname, join} from 'path';
 import {spawn} from 'node:child_process';
+import {Memory, MemoryManager} from './memory.ts';

 export type AnthropicConfig = {proto: 'anthropic', token: string};
-export type OllamaConfig = {proto: 'ollama', host: string};
 export type OpenAiConfig = {proto: 'openai', host?: string, token: string};

 export type LLMMessage = {
@@ -37,17 +36,9 @@ export type LLMMessage = {
 	timestamp?: number;
 }

-/** Background information the AI will be fed */
-export type LLMMemory = {
-	/** What entity is this fact about */
-	owner: string;
-	/** The information that will be remembered */
-	fact: string;
-	/** Owner and fact embedding vector */
-	embeddings: [number[], number[]];
-}
-
 export type LLMRequest = {
+	/** Return a parsed JSON object that matches the schema */
+	schema?: AiToolArg;
 	/** System prompt */
 	system?: string;
 	/** Message history */
@@ -63,17 +54,39 @@ export type LLMRequest = {
 	/** Stream response */
 	stream?: (chunk: {text?: string, tool?: string, done?: true}) => any;
 	/** Compress old messages in the chat to free up context */
-	compress?: {
-		/** Trigger chat compression once context exceeds the token count */
-		max: number;
-		/** Compress chat until context size smaller than */
-		min: number
-	},
-	/** Background information the AI will be fed */
-	memory?: LLMMemory[],
+	compress?: {max: number; min: number};
+	/** User's memory documents - RAG injected automatically each turn */
+	memory?: Memory[] | MemoryCache;
+	/** Model to use for memory operations */
+	memoryModel?: string;
+	/** Skill documents the AI can browse and read on demand */
+	skills?: Skill[];
+	/** MCP servers to connect and expose as tools */
+	mcp?: McpServer[];
 }

+export type McpServer = {
+	/** MCP server name for humans */
+	name: string;
+	/** Host URL */
+	host: string;
+	/** Server access token */
+	token?: string;
+}
+
+export type Skill = {
+	/** Name of skill for humans */
+	name: string;
+	/** Description LLM will use to decide to learn a skill */
+	description: string;
+	/** Skill instructions */
+	content: string;
+}
+
+
 class LLM {
+	private memoryManager!: MemoryManager;
+
 	defaultModel!: string;
 	models: {[model: string]: LLMProvider} = {};

@@ -82,21 +95,72 @@ class LLM {
 		Object.entries(ai.options.llm.models).forEach(([model, config]) => {
 			if(!this.defaultModel) this.defaultModel = model;
 			if(config.proto == 'anthropic') this.models[model] = new Anthropic(this.ai, config.token, model);
-			else if(config.proto == 'ollama') this.models[model] = new OpenAi(this.ai, config.host, 'not-needed', model);
 			else if(config.proto == 'openai') this.models[model] = new OpenAi(this.ai, config.host || null, config.token, model);
 		});
+		this.memoryManager = new MemoryManager(this);
+	}
+
+	private async setupMcp(servers: McpServer[] = []): Promise<{prompt: string, tools: AiTool[]}> {
+		if(!servers?.length) return {prompt: '', tools: []};
+		const allTools: AiTool[] = [];
+		await Promise.all(servers.map(async server => {
+			const res = await fetch(`${server.host}/tools`, {headers: server.token ? {Authorization: `Bearer ${server.token}`} : {}});
+			const mcp: any = await res.json();
+			if(!mcp?.tools) return;
+			for(const t of mcp.tools) {
+				const args: Record<string, any> = {};
+				if(t.inputSchema?.properties) {
+					for(const [key, val] of Object.entries<any>(t.inputSchema.properties)) {
+						args[key] = {type: val.type || 'string', description: val.description || '', required: t.inputSchema.required?.includes(key)};
+					}
+				}
+				allTools.push({
+					name: `${server.name}_${t.name}`,
+					description: t.description || '',
+					args,
+					fn: async (a: any) => {
+						const r = await fetch(`${server.host}/tools/call`, {
+							method: 'POST',
+							headers: {'Content-Type': 'application/json', ...(server.token ? {Authorization: `Bearer ${server.token}`} : {})},
+							body: JSON.stringify({name: t.name, arguments: a})
+						});
+						const data: any = await r.json();
+						return data?.content?.[0]?.text ?? JSON.stringify(data);
+					}
+				});
+			}
+		}));
+
+		const list = allTools.map(t => `- ${t.name}: ${t.description}`).join('\n');
+		return {
+			prompt: `You have access to the following MCP tools:\n${list}`,
+			tools: allTools
+		};
+	}
+
+	private setupSkills(skills: Skill[] = []): {prompt: string, tools: AiTool[]} {
+		if(!skills?.length) return {prompt: '', tools: []};
+		const list = skills.map(s => `- ${s.name}: ${s.description}`).join('\n');
+		return {
+			prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`,
+			tools: [{
+				name: 'skill_read',
+				description: 'Read the full content of a skill/knowledge document',
+				args: {
+					name: {type: 'string', description: 'Exact skill name', required: true}
+				},
+				fn: (args: any) => {
+					const skill = skills.find(s => s.name === args.name);
+					if(!skill) return `Skill not found. Available:\n${list}`;
+					return `# ${skill.name}\n${skill.content}`;
+				}
+			}]
+		}
 	}

-	/**
-	 * Chat with LLM
-	 * @param {string} message Question
-	 * @param {LLMRequest} options Configuration options and chat history
-	 * @returns {{abort: () => void, response: Promise<string>}} Function to abort response and chat history
-	 */
 	ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
 		options = <any>{
 			system: '',
-			temperature: 0.8,
 			...this.ai.options.llm,
 			models: undefined,
 			history: [],
@@ -104,87 +168,83 @@ class LLM {
 		}
 		const m = options.model || this.defaultModel;
 		if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
-		let abort = () => {};
-		return Object.assign(new Promise<string>(async res => {
-			if(!options.history) options.history = [];
-			// If memories were passed, find any relevant ones and add a tool for ADHOC lookups
+		let request: AbortablePromise<string> | null = null;
+		let aborted = false;
+		const abort = () => {
+			aborted = true;
+			request?.abort?.();
+		};
+
+		const promise = (async () => {
+			let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
+			const prompts: string[] = [];
+			let history = options.history || [];
+
+			// MCP
+			const mcp = options.mcp || this.ai.options?.llm?.mcp;
+			if(mcp?.length) {
+				const m = await this.setupMcp(mcp);
+				prompts.unshift(m.prompt);
+				tools.push(...m.tools);
+			}
+
+			// Skills
+			const skills = options.skills || this.ai.options?.llm?.skills;
+			if(skills?.length) {
+				const s = this.setupSkills(skills);
+				prompts.unshift(s.prompt);
+				tools.push(...s.tools);
+			}
+
+			// Memory
 			if (options.memory) {
-				const search = async (query?: string | null, subject?: string | null, limit = 10) => {
-					const [o, q] = await Promise.all([
-						subject ? this.embedding(subject) : Promise.resolve(null),
-						query ? this.embedding(query) : Promise.resolve(null),
-					]);
-					return (options.memory || []).map(m => {
-						const score = (o ? this.cosineSimilarity(m.embeddings[0], o[0].embedding) : 0)
-							+ (q ? this.cosineSimilarity(m.embeddings[1], q[0].embedding) : 0);
-						return {...m, score};
-					}).toSorted((a: any, b: any) => a.score - b.score).slice(0, limit)
-						.map(m => `- ${m.owner}: ${m.fact}`).join('\n');
+				const mems = options.memory instanceof MemoryCache ? options.memory.memories : options.memory;
+				if(mems.length) {
+					const relevant = await this.memoryManager.recollect(message, options.memory, 5);
+					prompts.unshift(`You have access to the following memory files:
+	${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}
+	${relevant.length ? `
+	Relevant memories have been preloaded:
+	${relevant.map(r => `
+	**${r.name}**
+	${r.description}
+	${r.content}
+	`).join('\n---\n')}
+	` : ''}`.trim());
+					tools.push(this.memoryManager.tools.read(options.memory));
+				}
 			}

-				options.system += '\nYou have RAG memory and will be given the top_k closest memories regarding the users query. Save anything new you have learned worth remembering from the user message using the remember tool and feel free to recall memories manually.\n';
-				const relevant = await search(message);
-				if(relevant.length) options.history.push({role: 'tool', name: 'recall', id: 'auto_recall_' + Math.random().toString(), args: {}, content: `Things I remembered:\n${relevant}`});
-				options.tools = [{
-					name: 'recall',
-					description: 'Recall the closest memories you have regarding a query using RAG',
-					args: {
-						subject: {type: 'string', description: 'Find information by a subject topic, can be used with or without query argument'},
-						query: {type: 'string', description: 'Search memory based on a query, can be used with or without subject argument'},
-						topK: {type: 'number', description: 'Result limit, default 5'},
-					},
-					fn: (args) => {
-						if(!args.subject && !args.query) throw new Error('Either a subject or query argument is required');
-						return search(args.query, args.subject, args.topK);
-					}
-				}, {
-					name: 'remember',
-					description: 'Store important facts user shares for future recall',
-					args: {
-						owner: {type: 'string', description: 'Subject/person this fact is about'},
-						fact: {type: 'string', description: 'The information to remember'}
-					},
-					fn: async (args) => {
-						if(!options.memory) return;
-						const e = await Promise.all([
-							this.embedding(args.owner),
-							this.embedding(`${args.owner}: ${args.fact}`)
-						]);
-						const newMem = {owner: args.owner, fact: args.fact, embeddings: <any>[e[0][0].embedding, e[1][0].embedding]};
-						options.memory.splice(0, options.memory.length, ...[
-							...options.memory.filter(m => {
-								return !(this.cosineSimilarity(newMem.embeddings[0], m.embeddings[0]) >= 0.9 && this.cosineSimilarity(newMem.embeddings[1], m.embeddings[1]) >= 0.8);
-							}),
-							newMem
-						]);
-						return 'Remembered!';
-					}
-				}, ...options.tools || []];
+			if(aborted) throw Object.assign(new Error('Aborted'), {name: 'AbortError'});
+
+			prompts.unshift(options.system || this.ai.options.llm?.system || '');
+			request = this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
+			const resp = await request;
+
+			// Trim memory injections from history
+			if(options.memory) {
+				history.splice(0, history.length, ...history.filter(h => h.role !== 'tool' || h.name !== 'recall'));
 			}

-			// Ask
-			const resp = await this.models[m].ask(message, options);
-
-			// Remove any memory calls from history
-			if(options.memory) options.history.splice(0, options.history.length, ...options.history.filter(h => h.role != 'tool' || (h.name != 'recall' && h.name != 'remember')));
-
-			// Compress message history
-			if(options.compress) {
-				const compressed = await this.ai.language.compressHistory(options.history, options.compress.max, options.compress.min, options);
-				options.history.splice(0, options.history.length, ...compressed);
+			// Auto-memorize before compressing
+			if(options.compress && this.estimateTokens(history) >= options.compress.max) {
+				if(options.memory) await this.memoryManager.memorize(history, options.memory, {model: options.memoryModel || this.defaultModel, ...options});
+				const compressed = await this.compressHistory(history, options.compress.max, options.compress.min, options);
+				if(options.history) options.history.splice(0, options.history.length, ...compressed);
 			}

-			return res(resp);
-		}), {abort});
+			return resp;
+		})();
+
+		return Object.assign(promise, {abort});
 	}

-	async code(message: string, options?: LLMRequest): Promise<any> {
-		const resp = await this.ask(message, {...options, system: [
-			options?.system,
-			'Return your response in a code block'
-		].filter(t => !!t).join(('\n'))});
-		const codeBlock = /```(?:.+)?\s*([\s\S]*?)```/.exec(resp);
-		return codeBlock ? codeBlock[1].trim() : null;
+	/**
+	 * 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<Memory[]> {
+		return this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
 	}

 	/**
@@ -273,7 +333,7 @@ class LLM {
 	 * @param {maxTokens?: number, overlapTokens?: number} opts Options for embedding such as chunk sizes
 	 * @returns {Promise<Awaited<{index: number, embedding: number[], text: string, tokens: number}>[]>} Chunked embeddings
 	 */
-	embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number} = {}): AbortablePromise<any[]> {
+	embedding(target: object | string, opts: {maxTokens?: number, overlapTokens?: number} = {}): AbortablePromise<{index: number, embedding: number[], text: string, tokens: number}[]> {
 		let {maxTokens = 500, overlapTokens = 50} = opts;
 		let aborted = false;
 		const abort = () => { aborted = true; };
@@ -281,7 +341,6 @@ class LLM {
 		const embed = (text: string): Promise<number[]> => {
 			return new Promise((resolve, reject) => {
 				if(aborted) return reject(new Error('Aborted'));
-
 				const args: string[] = [
 					join(dirname(fileURLToPath(import.meta.url)), 'embedder.js'),
 					<string>this.ai.options.path,
@@ -290,7 +349,6 @@ class LLM {
 				const proc = spawn('node', args, {stdio: ['pipe', 'pipe', 'ignore']});
 				proc.stdin.write(text);
 				proc.stdin.end();
-
 				let output = '';
 				proc.stdout.on('data', (data: Buffer) => output += data.toString());
 				proc.on('close', (code: number) => {
@@ -300,7 +358,7 @@ class LLM {
 							const result = JSON.parse(output);
 							resolve(result.embedding);
 						} catch(err) {
-							reject(new Error('Failed to parse embedding output'));
+							reject(err);
 						}
 					} else {
 						reject(new Error(`Embedder process exited with code ${code}`));
@@ -320,7 +378,7 @@ class LLM {
 			}
 			return results;
 		})();
-		return Object.assign(p, { abort });
+		return <any>Object.assign(p, {abort});
 	}

 	/**
@@ -346,42 +404,8 @@ class LLM {
 				(char.charCodeAt(0) * (index + 1)) % dimensions / dimensions).slice(0, dimensions);
 		}
 		const v = vector(target);
-		const similarities = searchTerms.map(t => vector(t)).map(refVector => this.cosineSimilarity(v, refVector))
-		return {avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length, max: Math.max(...similarities), similarities}
-	}
-
-	/**
-	 * Ask a question with JSON response
-	 * @param {string} text Text to process
-	 * @param {string} schema JSON schema the AI should match
-	 * @param {LLMRequest} options Configuration options and chat history
-	 * @returns {Promise<{} | {} | RegExpExecArray | null>}
-	 */
-	async json(text: string, schema: string, options?: LLMRequest): Promise<any> {
-		let system =  `Your job is to convert input to JSON using tool calls. Call the \`submit\` tool at least once with JSON matching this schema:\n\`\`\`json\n${schema}\n\`\`\`\n\nResponses are ignored`;
-		if(options?.system) system += '\n\n' + options.system;
-		return new Promise(async (resolve, reject) => {
-			let done = false;
-			const resp = await this.ask(text, {
-				temperature: 0.3,
-				...options,
-				system,
-				tools: [{
-					name: 'submit',
-					description: 'Submit JSON',
-					args: {json: {type: 'string', description: 'Javascript parsable JSON string', required: true}},
-					fn: (args) => {
-						try {
-							const json = JSON.parse(args.json);
-							resolve(json);
-							done = true;
-						} catch { return 'Invalid JSON'; }
-						return 'Saved';
-					}
-				}, ...(options?.tools || [])],
-			});
-			if(!done) reject(`AI failed to create JSON:\n${resp}`);
-		});
+		const similarities = searchTerms.map(t => vector(t)).map(refVector => this.cosineSimilarity(v, refVector));
+		return {avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length, max: Math.max(...similarities), similarities};
 	}

 	/**
@@ -417,6 +441,29 @@ class LLM {
 			if(!done) reject(`AI failed to create summary:\n${resp}`);
 		});
 	}
+
+	addModel(name: string, config: AnthropicConfig | OpenAiConfig, setDefault = false) {
+		if(config.proto == 'anthropic') this.models[name] = new Anthropic(this.ai, config.token, name);
+		else if(config.proto == 'openai') this.models[name] = new OpenAi(this.ai, config.host || null, config.token, name);
+		if(setDefault || !this.defaultModel) this.defaultModel = name;
+	}
+
+	removeModel(name: string) {
+		delete this.models[name];
+		if(this.defaultModel === name) {
+			this.defaultModel = Object.keys(this.models)[0] ?? '';
+		}
+	}
+
+	setModels(models: {[model: string]: AnthropicConfig | OpenAiConfig}, replace = true) {
+		if(replace) this.models = {};
+		Object.entries(models).forEach(([model, config]) => {
+			if(!this.defaultModel) this.defaultModel = model;
+			if(config.proto == 'anthropic') this.models[model] = new Anthropic(this.ai, config.token, model);
+			else if(config.proto == 'openai') this.models[model] = new OpenAi(this.ai, config.host || null, config.token, model);
+		});
+		this.defaultModel = Object.keys(this.models)[0] ?? '';
+	}
 }

 export default LLM;
--- a/src/memory-cache.ts
+++ b/src/memory-cache.ts
@@ -0,0 +1,59 @@
+import {KDPoint, KDTree} from './kd-tree.ts';
+import {Memory, MemoryRef} from './memory.ts';
+
+export class MemoryCache {
+	private tree: KDTree<MemoryRef>;
+	public memories: Memory[];
+
+	get length() { return this.memories.length; }
+
+	constructor(memories: Memory[]) {
+		this.memories = memories;
+		this.tree = this.buildTree();
+	}
+
+	private buildTree(): KDTree<MemoryRef> {
+		const embedded = this.memories.filter(m => m.embedding?.length);
+		if (!embedded.length) return new KDTree<MemoryRef>(0);
+
+		const dims = embedded[0].embedding.length;
+		const points: KDPoint<MemoryRef>[] = embedded.map(m => ({
+			vector: m.embedding,
+			payload: {name: m.name, description: m.description},
+		}));
+
+		return new KDTree<MemoryRef>(dims, 'cosine', points);
+	}
+
+	search(query: number[], limit: number): MemoryRef[] {
+		const results = this.tree.knn(query, limit);
+		return results.map(r => r.point.payload);
+	}
+
+	add(memory: Memory): void {
+		this.memories.push(memory);
+		this.rebuild();
+	}
+
+	update(memory: Memory): void {
+		const idx = this.memories.findIndex(m => m.name === memory.name);
+		if (idx !== -1) {
+			this.memories[idx] = memory;
+		} else {
+			this.memories.push(memory);
+		}
+		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();
+	}
+}
--- a/src/memory-graph.ts
+++ b/src/memory-graph.ts
@@ -0,0 +1,69 @@
+import {MemoryCache} from './memory-cache.ts';
+import {extractMetadata, Memory, MemoryNode} from './memory.ts';
+
+export function buildMemoryGraph(memories: Memory[] | MemoryCache): MemoryNode[] {
+	const mems = memories instanceof MemoryCache ? memories.memories : memories;
+	const nameSet = new Set(mems.map(m => m.name));
+	const ghosts = new Set<string>();
+
+	const nodes: MemoryNode[] = mems.map(m => {
+		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 [
+		...nodes,
+		...[...ghosts].map(name => ({
+			name,
+			missing: true,
+			links: [],
+			backlinks: nodes
+				.filter(n => n.links.includes(name))
+				.map(n => n.name),
+		}))
+	];
+}
+
+export function renderMemoryGraph(nodes) {
+	if (!nodes.length) return 'No memories yet.';
+
+	const groups = new Map();
+	for (const node of nodes) {
+		const [prefix, ...rest] = node.name.split('/');
+		const group = rest.length ? prefix : 'Root';
+		const label = rest.length ? rest.join('/') : node.name;
+		if (!groups.has(group)) groups.set(group, []);
+		groups.get(group).push({...node, label});
+	}
+
+	const ghostCount = nodes.filter(n => n.missing).length;
+	const lines = [`Memory Graph (${nodes.length} nodes, ${ghostCount} ghost${ghostCount === 1 ? '' : 's'})`, ''];
+
+	for (const group of [...groups.keys()].sort()) {
+		const items = groups.get(group).sort((a, b) => a.label.localeCompare(b.label));
+		lines.push(`${group}/`);
+		items.forEach((n, i) => {
+			const last = i === items.length - 1;
+			const branch = last ? '└─' : '├─';
+			const pad = last ? '  ' : '│ ';
+			const tag = n.missing ? ' (ghost)' : '';
+			lines.push(`  ${branch} ${n.label}${tag}`);
+			if (n.links.length) lines.push(`  ${pad}   → ${n.links.join(', ')}`);
+			if (n.backlinks.length) lines.push(`  ${pad}   ← ${n.backlinks.join(', ')}`);
+		});
+		lines.push('');
+	}
+
+	return lines.join('\n').trimEnd();
+}
--- a/src/memory.ts
+++ b/src/memory.ts
@@ -0,0 +1,484 @@
+import {LLMRequest, LLMMessage} from './llm.ts';
+import {MemoryCache} from './memory-cache.ts';
+import {AiTool} from './tools.ts';
+
+export type Memory = {
+	name: string;
+	description: string;
+	content: string;
+	embedding: number[];
+}
+
+export type MemoryRef = {
+	name: string;
+	description: string;
+}
+
+export type FactBucket = {
+	subject: string;
+	facts: string[];
+}
+
+export type MemoryNode = {
+	name: string;
+	missing: boolean;
+	links: string[];
+	backlinks: string[];
+}
+
+function extractLinks(content: string): string[] {
+	if(!content) return [];
+	const matches = content.matchAll(/\[\[([^\]]+)\]\]/g);
+	return [...new Set([...matches].map(m => m[1].trim()))];
+}
+
+export function extractMetadata(content: string): {links: string[], backlinks: string[]} {
+	const match = content.match(/^---\n([\s\S]*?)\n---/);
+	if (!match) return {links: [], backlinks: []};
+
+	const fm = match[1];
+	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(/^"|"$/g, '')).filter(Boolean);
+	};
+
+	return {
+		links: getList('links'),
+		backlinks: getList('backlinks'),
+	};
+}
+
+function dedupeFacts(facts: string[]): string[] {
+	const seen = new Map<string, string>();
+	for (const f of facts) {
+		const clean = f.trim();
+		if (clean) seen.set(clean.toLowerCase(), clean);
+	}
+	return [...seen.values()];
+}
+
+function cosineDistance(a: number[], b: number[]): number {
+	let dot = 0, normA = 0, normB = 0;
+	for (let i = 0; i < a.length; i++) {
+		dot   += a[i] * b[i];
+		normA += a[i] * a[i];
+		normB += b[i] * b[i];
+	}
+	const denom = Math.sqrt(normA) * Math.sqrt(normB);
+	return denom === 0 ? 1 : 1 - dot / denom;
+}
+
+function getWeekMonday(date: Date = new Date()): string {
+	const d = new Date(Date.UTC(date.getFullYear(), date.getMonth(), date.getDate()));
+	const day = d.getUTCDay();
+	const diff = day === 0 ? -6 : 1 - day;
+	d.setUTCDate(d.getUTCDate() + diff);
+	return d.toISOString().slice(0, 10);
+}
+
+function getWeekSunday(monday: string): string {
+	const d = new Date(`${monday}T00:00:00Z`);
+	d.setUTCDate(d.getUTCDate() + 6);
+	return d.toISOString().slice(0, 10);
+}
+
+
+
+export class MemoryManager {
+	private pendingMemorizations = new Map<string, {
+		memories: Memory[] | MemoryCache,
+		tempMemoryName: string,
+		timestamp: number,
+	}>();
+
+	private queues = new Map<string, {
+		pending: string[],
+		request: {abort?: () => void} | null,
+		task: Promise<void>,
+	}>();
+
+	tools = {
+		read: (memories: Memory[] | MemoryCache): AiTool => ({
+			name: 'memory_recall',
+			description: 'Read the full content of a memory document',
+			args: {
+				name: {type: 'string', description: 'Exact memory name', required: true},
+			},
+			fn: (args: any) => {
+				const mems = memories instanceof MemoryCache ? memories.memories : memories;
+				const mem = mems.find(m => m.name === args.name);
+				if (!mem) return 'Document not found';
+				return mem.content;
+			},
+		}),
+
+		forget: (memories: Memory[] | MemoryCache): AiTool => ({
+			name: 'memory_forget',
+			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}
+			},
+			fn: (args: any) => {
+				const result = this.forget(args.name, memories);
+				return result ? `Forgotten: ${args.name}` : `Not found: ${args.name}`;
+			},
+		}),
+	};
+
+	constructor(private llm: any) {}
+
+	private async createTempMemory(conversation: string): Promise<Memory> {
+		const timestamp = Date.now();
+		const content = `---
+name: _temp_${timestamp}
+description: Temporary memory - processing in background
+tags: [_temporary]
+links: []
+backlinks: []
+modified: ${new Date().toISOString()}
+---
+
+# Recent Conversation (Processing)
+
+${conversation}`;
+		const [e] = await this.llm.embedding(content);
+		return {
+			name: `_temp_${timestamp}`,
+			description: 'Temporary memory - processing in background',
+			content,
+			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),
+			}))
+			.sort((a, b) => a.distance - b.distance)
+			.slice(0, limit);
+		return scored.map(s => s.ref);
+	}
+
+	private listNodes(memories: Memory[]): MemoryRef[] {
+		return memories.map(m => ({name: m.name, description: m.description}));
+	}
+
+	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 [];
+
+		let vectorResults: MemoryRef[];
+		if (memories instanceof MemoryCache) vectorResults = memories.search(e.embedding, limit);
+		else vectorResults = this.cosineSearch(e.embedding, mem, limit);
+		const found = new Set<string>(vectorResults.map(r => r.name));
+
+		if (graphDepth > 0) {
+			const frontier = [...found];
+			for (let depth = 0; depth < graphDepth; depth++) {
+				const next: string[] = [];
+				for (const name of frontier) {
+					const node = mem.find(m => m.name === name);
+					if (!node) continue;
+					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);
+						}
+					}
+				}
+				frontier.splice(0, frontier.length, ...next);
+				if (!frontier.length) break;
+			}
+		}
+
+		const vectorOrder = vectorResults.map(r => r.name);
+		const graphExpansions = [...found].filter(n => !vectorOrder.includes(n));
+		const ordered = [...vectorOrder, ...graphExpansions];
+		return ordered.map(n => mem.find(m => m.name === n)!).filter(Boolean);
+	}
+
+	async memorize(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest): Promise<Memory[]> {
+		const conversation = history
+			.filter(h => h.role === 'user' || h.role === 'assistant')
+			.map(h => `[${h.role}]: ${h.content}`).join('\n\n').trim();
+		if(!conversation) return [];
+
+		const trackingId = `${Date.now()}_${Math.random()}`;
+		const tempMemory = await this.createTempMemory(conversation);
+		const mem = memories instanceof MemoryCache ? memories.memories : memories;
+		mem.push(tempMemory);
+		if (memories instanceof MemoryCache) memories.rebuild();
+		this.pendingMemorizations.set(trackingId, {
+			memories,
+			tempMemoryName: tempMemory.name,
+			timestamp: Date.now(),
+		});
+
+		try {
+			await this._memorizeBackground(conversation, memories, options, tempMemory.name);
+			const finalMem = memories instanceof MemoryCache ? memories.memories : memories;
+			return finalMem.filter(m => !m.name.startsWith('_temp_'));
+		} finally {
+			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);
+		}
+	}
+
+	private async _memorizeBackground(conversation: string, memories: Memory[] | MemoryCache, options: LLMRequest, tempName: string): Promise<void> {
+		const mem = memories instanceof MemoryCache ? memories.memories : memories;
+		const monday = getWeekMonday();
+		const sunday = getWeekSunday(monday);
+		const buckets = await this.factAgent(conversation, mem, options, monday);
+		if(!buckets.length) return;
+		const jobs = [...buckets].map(({subject, facts}) => {
+			let node = mem.find(m => m.name === subject);
+			if(!node) {
+				node = {name: subject, description: '', content: '', embedding: [],};
+				mem.push(node);
+			}
+			const week = subject.startsWith('Journal/') ? {monday, sunday} : undefined;
+			return this.enqueue(node, facts, mem, options, tempName, week);
+		});
+		await Promise.all(jobs);
+	}
+
+	/**
+	 * Coalescing queue: if a doc is already compiling, abort the in-flight run, merge its
+	 * facts with the new ones and restart. Never blocks a pending update, never drops facts.
+	 */
+	private enqueue(node: Memory, facts: string[], memories: Memory[] | MemoryCache, options: LLMRequest, tempName: string, week?: {monday: string, sunday: string}): Promise<void> {
+		const key = node.name;
+		const existing = this.queues.get(key);
+		if (existing) {
+			existing.pending.push(...facts);
+			existing.request?.abort?.();
+			return existing.task;
+		}
+
+		const entry: {pending: string[], request: {abort?: () => void} | null, task: Promise<void>} = {pending: [...facts], request: null, task: Promise.resolve()};
+		this.queues.set(key, entry);
+		const m = memories instanceof MemoryCache ? memories.memories : memories;
+		entry.task = (async () => {
+			while (entry.pending.length) {
+				const batch = dedupeFacts(entry.pending.splice(0, entry.pending.length));
+				const written = await this.docAgent(node, batch, m, options, tempName, week, entry);
+				if (!written) entry.pending.unshift(...batch);
+			}
+		})().finally(() => {
+			this.queues.delete(key);
+			if(!this.queues.size && memories instanceof MemoryCache) memories.rebuild();
+		});
+		return entry.task;
+	}
+
+	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: ${new Date().toISOString()}`,
+			'---',
+		].filter(Boolean);
+		return lines.join('\n');
+	}
+
+	private applyHeader(content: string, header: string): string {
+		return `${header}\n\n${this.stripHeader(content)}`;
+	}
+
+	private updateFrontmatter(content: string, updates: {links?: string[], backlinks?: string[]}): string {
+		const match = content.match(/^---\n([\s\S]*?)\n---\n\n?([\s\S]*)$/);
+		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${body}`;
+	}
+
+	private stripHeader(content: string): string {
+		return content.replace(/^---[\s\S]*?\n---\n?/, '').trimStart();
+	}
+
+	private async docAgent(node: Memory, facts: string[], memories: Memory[], options: LLMRequest, tempName: string, week: {monday: string, sunday: string} | undefined, entry: {request: {abort?: () => void} | null}): Promise<boolean> {
+		const {links: oldLinks} = extractMetadata(node.content);
+		const currentBody = this.stripHeader(node.content);
+		let update;
+		try {
+			for(let i = 0; i < 3 && !update?.content; i++) {
+				const request = this.llm.ask(`New Facts:\n${facts.map(f => `- ${f}`).join('\n')}`, {
+					model: options.model,
+					temperature: 0.3,
+					schema: {
+						description: {type: 'string', description: 'One-line description of what this document covers, no formatting or emojis', required: true},
+						content: {type: 'string', description: 'Rewritten document in markdown, without the frontmatter block', required: true},
+					},
+					system: `You are a knowledge base editor. Rewrite the current document below so it incorporates the new facts.
+
+Formatting rules:
+- Use Obsidian-style markdown: # headings, **bold** to add emphasis, __italics__ for titles, terms, etc, bullet & numbered lists for grouped 1D data and tables for 2D data
+- Link related concepts with [[WikiLink]] notation using full paths like [[People/Sarah]] or [[Projects/Website]]
+- Create links for specific entities (person, place, project, program) and abstract concepts (quantum mechanics, entropy) but skip generics (car, red, dog)
+- Keep the document concise, factual, and human-readable
+- Resolve contradictions: the new facts always win — delete the outdated statement entirely, never keep both
+- Later facts in the list override earlier ones
+- Do not add frontmatter blocks, filler, preamble, or AI commentary
+${week ? '- This is a weekly journal entry.\n' : ''}
+All nodes:
+${this.listNodes(memories).map(n => n.name).join(', ') || 'none'}
+
+Current document:
+\`\`\`markdown
+${currentBody}
+\`\`\``}
+				);
+				entry.request = request;
+				update = await request;
+			}
+		} catch (err: any) {
+			if (err?.name === 'AbortError') return false;
+			throw err;
+		} finally {
+			entry.request = null;
+		}
+
+		if(!update?.content) return false;
+		const newLinks = extractLinks(update.content).filter(l => l !== node.name && l !== tempName);
+		const newLinkSet = new Set(newLinks);
+		const oldLinkSet = new Set(oldLinks);
+
+		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);
+		node.description = update.description;
+		node.content = this.applyHeader(update.content, this.buildHeader(node, week, newLinks, backlinks));
+		const [e] = await this.llm.embedding(node.content);
+		if(e) node.embedding = e.embedding;
+		return true;
+	}
+
+	private async factAgent(conversation: string, memories: Memory[], options: LLMRequest, weekKey: string): Promise<FactBucket[]> {
+		const buckets = new Map<string, string[]>();
+		await this.llm.ask(conversation, {
+			model: options.model,
+			temperature: 0.2,
+			system: `You are a fact extractor. Analyze this conversation and extract facts worth remembering long-term.
+
+Rules:
+- ONLY extract current facts the USER explicitly stated about themselves, their work, or their projects
+- ONLY extract decisions that were MADE during this conversation
+- DO NOT extract anything the AI said, its capabilities, or meta-conversation about the AI
+- DO NOT extract greetings, pleasantries, or generic exchanges
+- DO NOT extract deltas or changes in facts; ONLY the end fact
+- If nothing worth remembering was said, do not call any tools
+
+When extracting facts, you MUST also decide the exact destination path:
+- Use an existing node name if the facts clearly belong there
+- All information primary about the user should go under "Personal/..." (e.g., Personal/Info, Personal/Todos)
+- When required, create a new path following collection/subject format (e.g., People/Sarah, Projects/Oxide)
+- For journal entries, use "Journal"
+
+Available nodes:
+- Journal
+${this.listNodes(memories).filter(n => !n.name.includes('_temp_') && !n.name.includes('Journal')).map(n => `- ${n.name}: ${n.description}`).join('\n') || 'None yet.'}`,
+			tools: [{
+				name: 'facts_extract',
+				description: 'Submit facts with their destination',
+				args: {
+					destination: {type: 'string', description: 'Exact existing node name OR new path (e.g. "People/Sarah", "Projects/Oxide")', required: true},
+					facts: {type: 'string', description: 'Comma-separated facts', required: true},
+				},
+				fn: (args: any) => {
+					const subject = args.destination.trim().toLowerCase() === 'journal'
+						? `Journal/${weekKey}`
+						: args.destination.trim();
+					const facts = buckets.get(subject) ?? [];
+					facts.push(...dedupeFacts(String(args.facts).split(',')));
+					buckets.set(subject, facts);
+					return 'Recorded';
+				},
+			}],
+		});
+		return buckets.entries().toArray().map(([subject, facts]) => ({subject, facts}));
+	}
+}
--- a/src/open-ai.ts
+++ b/src/open-ai.ts
@@ -3,6 +3,7 @@ import {findByProp, objectMap, JSONSanitize, JSONAttemptParse, clean} from '@zti
 import {AbortablePromise, Ai} from './ai.ts';
 import {LLMMessage, LLMRequest} from './llm.ts';
 import {LLMProvider} from './provider.ts';
+import {convertSchema} from './tools.ts';

 export class OpenAi extends LLMProvider {
 	client!: openAI;
@@ -11,7 +12,7 @@ export class OpenAi extends LLMProvider {
 		super();
 		this.client = new openAI(clean({
 			baseURL: host,
-			apiKey: token || host ? 'ignored' : undefined
+			apiKey: token || (host ? 'ignored' : undefined)
 		}));
 	}

@@ -64,7 +65,7 @@ export class OpenAi extends LLMProvider {
 		}, [] as any[]);
 	}

-	ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
+	ask(message: string, options: LLMRequest = {}): AbortablePromise<string | any> {
 		const controller = new AbortController();
 		return Object.assign(new Promise<any>(async (res, rej) => {
 			if(options.system) {
@@ -77,8 +78,8 @@ export class OpenAi extends LLMProvider {
 				model: options.model || this.model,
 				messages: history,
 				stream: !!options.stream,
-				max_tokens: options.max_tokens || this.ai.options.llm?.max_tokens || 4096,
-				temperature: options.temperature || this.ai.options.llm?.temperature || 0.7,
+				max_completion_tokens: options.max_tokens || this.ai.options.llm?.max_tokens || undefined,
+				temperature: options.temperature || this.ai.options.llm?.temperature || undefined,
 				tools: tools.map(t => ({
 					type: 'function',
 					function: {
@@ -93,6 +94,18 @@ export class OpenAi extends LLMProvider {
 				}))
 			};

+			if(options.schema) {
+				const schema = convertSchema(options.schema);
+				requestParams.response_format = {
+					type: 'json_schema',
+					json_schema: {
+						name: 'response',
+						strict: true,
+						schema
+					}
+				};
+			}
+
 			let resp: any, isFirstMessage = true;
 			do {
 				resp = await this.client.chat.completions.create(requestParams).catch(err => {
@@ -138,6 +151,7 @@ export class OpenAi extends LLMProvider {
 					}
 				}

+				if(resp.error) throw new Error(resp.error);
 				const toolCalls = resp.choices[0].message.tool_calls || [];
 				if(toolCalls.length && !controller.signal.aborted) {
 					history.push(resp.choices[0].message);
@@ -157,12 +171,17 @@ export class OpenAi extends LLMProvider {
 					requestParams.messages = history;
 				}
 			} while (!controller.signal.aborted && resp.choices?.[0]?.message?.tool_calls?.length);
-			history.push({role: 'assistant', content: resp.choices[0].message.content || ''});
+
+			const textContent = resp.choices[0].message.content?.trim() || '';
+			history.push({role: 'assistant', content: textContent});
 			history = this.toStandard(history);

 			if(options.stream) options.stream({done: true});
 			if(options.history) options.history.splice(0, options.history.length, ...history);
-			res(history.at(-1)?.content);
+
+			// Return parsed JSON if schema provided
+			const finalContent = history.at(-1)?.content;
+			res(options.schema ? JSONAttemptParse(finalContent, finalContent) : finalContent);
 		}), {abort: () => controller.abort()});
 	}
 }
--- a/src/provider.ts
+++ b/src/provider.ts
@@ -1,5 +1,5 @@
 import {AbortablePromise} from './ai.ts';
-import {LLMMessage, LLMRequest} from './llm.ts';
+import {LLMRequest} from './llm.ts';

 export abstract class LLMProvider {
 	abstract ask(message: string, options: LLMRequest): AbortablePromise<string>;
--- a/src/tools.ts
+++ b/src/tools.ts
@@ -1,6 +1,6 @@
 import  * as cheerio from 'cheerio';
 import {$Sync} from '@ztimson/node-utils';
-import {ASet, consoleInterceptor, Http, fn as Fn, decodeHtml} from '@ztimson/utils';
+import {ASet, consoleInterceptor, Http, fn as Fn, decodeHtml, objectMap} from '@ztimson/utils';
 import * as os from 'node:os';
 import {Ai} from './ai.ts';
 import {LLMRequest} from './llm.ts';
@@ -44,25 +44,80 @@ export type AiTool = {
 	fn: (args: any, stream: LLMRequest['stream'], ai: Ai) => any | Promise<any>,
 };

-export const CliTool: AiTool = {
+export function convertSchema(schema: any): any {
+	if(!schema) return null;
+
+	const convertProp = (prop: any): any => {
+		const converted: any = {
+			type: prop.type || 'string',
+		};
+
+		if(prop.description) converted.description = prop.description;
+		if(prop.default !== undefined) converted.default = prop.default;
+		if(prop.enum) converted.enum = prop.enum;
+		if(prop.pattern) converted.pattern = prop.pattern;
+
+		// Handle array items
+		if(prop.type === 'array' && prop.items) {
+			converted.items = convertProp(prop.items);
+		}
+
+		// Handle object properties
+		if(prop.type === 'object' && prop.items) {
+			converted.properties = objectMap(prop.items, (key, value) => convertProp(value));
+			const required = Object.entries(prop.items).filter(([_, v]: any) => v.required).map(([k]) => k);
+			if(required.length) converted.required = required;
+			converted.additionalProperties = false;
+		}
+
+		// Handle min/max based on type
+		if(prop.min !== undefined) {
+			if(prop.type === 'string' || prop.type === 'array') converted.minLength = prop.min;
+			else converted.minimum = prop.min;
+		}
+		if(prop.max !== undefined) {
+			if(prop.type === 'string' || prop.type === 'array') converted.maxLength = prop.max;
+			else converted.maximum = prop.max;
+		}
+
+		return converted;
+	};
+
+	return {
+		type: 'object',
+		properties: objectMap(schema, (key, value) => convertProp(value)),
+		required: Object.entries(schema).filter(([_, v]: any) => v.required).map(([k]) => k),
+		additionalProperties: false
+	};
+}
+
+export const ExecCliTool: AiTool = {
 	name: 'cli',
 	description: 'Use the command line interface, returns any output',
 	args: {command: {type: 'string', description: 'Command to run', required: true}},
 	fn: (args: {command: string}) => $Sync`${args.command}`
 }

-export const DateTimeTool: AiTool = {
-	name: 'get_datetime',
-	description: 'Get local date / time',
-	args: {},
-	fn: async () => new Date().toString()
+export const ExecJSTool: AiTool = {
+	name: 'exec_javascript',
+	description: 'Execute commonjs javascript',
+	args: {
+		code: {type: 'string', description: 'CommonJS javascript', required: true}
+	},
+	fn: async (args: {code: string}) => {
+		const c = consoleInterceptor(null);
+		const resp = await Fn<any>({console: c}, args.code, true).catch((err: any) => c.output.error.push(err));
+		return {...c.output, return: resp, stdout: undefined, stderr: undefined};
+	}
 }

-export const DateTimeUTCTool: AiTool = {
-	name: 'get_datetime_utc',
-	description: 'Get current UTC date / time',
-	args: {},
-	fn: async () => new Date().toUTCString()
+export const ExecPythonTool: AiTool = {
+	name: 'exec_python',
+	description: 'Execute commonjs javascript',
+	args: {
+		code: {type: 'string', description: 'CommonJS javascript', required: true}
+	},
+	fn: async (args: {code: string}) => ({result: $Sync`python -c "${args.code}"`})
 }

 export const ExecTool: AiTool = {
@@ -76,11 +131,11 @@ export const ExecTool: AiTool = {
 		try {
 			switch(args.language) {
 				case 'cli':
-					return await CliTool.fn({command: args.code}, stream, ai);
+					return await ExecCliTool.fn({command: args.code}, stream, ai);
 				case 'node':
-					return await JSTool.fn({code: args.code}, stream, ai);
+					return await ExecJSTool.fn({code: args.code}, stream, ai);
 				case 'python':
-					return await PythonTool.fn({code: args.code}, stream, ai);
+					return await ExecPythonTool.fn({code: args.code}, stream, ai);
 				default:
 					throw new Error(`Unsupported language: ${args.language}`);
 			}
@@ -90,8 +145,390 @@ export const ExecTool: AiTool = {
 	}
 }

-export const FetchTool: AiTool = {
-	name: 'fetch',
+export const FsDeleteTool = (whitelist: null | string[] = null): AiTool => {
+	return {
+		name: 'fs_delete',
+		description: 'Delete a file or directory',
+		args: {
+			path: {type: 'string', description: 'Path to file or directory', required: true},
+			recursive: {type: 'boolean', description: 'Delete all children', required: false}
+		},
+		fn: async ({path, recursive = false}) => {
+			const {existsSync, rmSync} = await import('fs');
+			const normalizePath = p => p.replace(/\\/g, '/');
+
+			path = normalizePath(path);
+			if(whitelist && !whitelist.some(p => path.startsWith(p))) return {error: 'Permission denied'};
+			if(!existsSync(path)) return {error: 'Path does not exist'};
+
+			rmSync(path, {recursive, force: true});
+			return {success: true, path};
+		}
+	}
+}
+
+export const FsMoveTool = (whitelist: null | string[] = null): AiTool => {
+	return {
+		name: 'fs_move',
+		description: 'Move or rename a file or directory',
+		args: {
+			source: {type: 'string', description: 'Path to source file or directory', required: true},
+			destination: {type: 'string', description: 'Path to destination file or directory', required: true}
+		},
+		fn: async ({source, destination}) => {
+			const {existsSync, renameSync} = await import('fs');
+			const normalizePath = p => p.replace(/\\/g, '/');
+
+			source = normalizePath(source);
+			destination = normalizePath(destination);
+			if(whitelist && !whitelist.some(p => source.startsWith(p) && destination.startsWith(p))) return {error: 'Permission denied'};
+
+			if(!existsSync(source)) return {error: 'Source path does not exist'};
+			if(existsSync(destination)) return {error: 'Destination path already exists'};
+
+			renameSync(source, destination);
+			return {success: true, source, destination};
+		}
+	}
+}
+
+export const FsReadTool = (whitelist: null | string[] = null): AiTool => {
+	return {
+		name: 'fs_read',
+		description: 'Read the contents of a provided path. Works with files and directories',
+		args: {path: {type: 'string', description: 'Path to file or directory', required: true}},
+		fn: async ({path}) => {
+			const {existsSync, lstatSync, readdirSync, readFileSync} = await import('fs');
+			const {join} = await import('path');
+			const normalizePath = p => p.replace(/\\/g, '/');
+
+			path = normalizePath(path);
+			if(whitelist && !whitelist.some(p => path.startsWith(p))) return {error: 'Permission denied'};
+
+			if(!existsSync(path)) return {error: 'Path does not exist'};
+			const stats = lstatSync(path);
+			if(stats.isDirectory()) {
+				const children = readdirSync(path).map(name => {
+					const childPath = normalizePath(join(path, name));
+					const childStats = lstatSync(childPath);
+					return {name, type: childStats.isDirectory() ? 'directory' : 'file', size: childStats.size};
+				});
+				return {type: 'directory', children};
+			}
+			const content = readFileSync(path, 'utf-8');
+			return {type: 'file', content};
+		}
+	}
+}
+
+export const FsSearchTool = (whitelist: null | string[] = null): AiTool => {
+	return {
+		name: 'fs_search',
+		description: 'Scan a directory for matching glob patterns (e.g. "**/*.js", "src/**/*.test.ts")',
+		args: {
+			pattern: {type: 'string', description: 'Glob pattern to match against paths', required: true},
+			root: {type: 'string', description: 'Directory to search from', required: false, default: '.'}
+		},
+		fn: async ({pattern, root = '.'}) => {
+			const {existsSync, lstatSync, readdirSync} = await import('fs');
+			const {join, relative} = await import('path');
+			const normalizePath = p => p.replace(/\\/g, '/');
+
+			root = normalizePath(root);
+			if(!existsSync(root)) return {error: 'Root path does not exist'};
+			if(!lstatSync(root).isDirectory()) return {error: 'Root path is not a directory'};
+
+			if(whitelist && !whitelist.some(p => root.startsWith(p))) return {error: 'Permission denied'};
+
+			const globToRegex = (glob) => {
+				let re = '';
+				for(let i = 0; i < glob.length; i++) {
+					const c = glob[i];
+					if(c === '*') {
+						if(glob[i + 1] === '*') {
+							const isSlash = glob[i + 2] === '/';
+							re += '.*';
+							i += isSlash ? 2 : 1;
+						} else {
+							re += '[^/]*';
+						}
+					} else if(c === '?') {
+						re += '[^/]';
+					} else if('.+^$(){}|[]\\'.includes(c)) {
+						re += '\\' + c;
+					} else {
+						re += c;
+					}
+				}
+				return new RegExp('^' + re + '$');
+			};
+			const regex = globToRegex(pattern);
+
+			const results: any = [];
+			const walk = (dir) => {
+				for(const name of readdirSync(dir)) {
+					const fullPath = normalizePath(join(dir, name));
+					const stats = lstatSync(fullPath);
+					const relPath = normalizePath(relative(root, fullPath));
+					if(regex.test(relPath)) {
+						results.push({path: relPath, type: stats.isDirectory() ? 'directory' : 'file', size: stats.size});
+					}
+					if(stats.isDirectory()) walk(fullPath);
+				}
+			};
+			walk(root);
+
+			return results;
+		}
+	}
+}
+
+export const FsWriteTool = (whitelist: null | string[] = null): AiTool => {
+	return {
+		name: 'fs_write',
+		description: 'Create a directory, write content to a file or preform a find & replace',
+		args: {
+			path: {type: 'string', description: 'Path to file or directory', required: true},
+			content: {type: 'string', description: 'Content to write or replace (Omit to create a directory)'},
+			find: {type: 'string', description: 'Text or regex pattern to match (regex must match pattern: "/pattern/g")'}
+		},
+		fn: async ({path, content, find}) => {
+			const {existsSync, mkdirSync, readFileSync, writeFileSync} = await import('fs');
+			const {dirname} = await import('path');
+			const normalizePath = p => p.replace(/\\/g, '/');
+
+			path = normalizePath(path);
+			if(whitelist && !whitelist.some(p => path.startsWith(p))) return {error: 'Permission denied'};
+
+			if(content === undefined) {
+				mkdirSync(path, {recursive: true});
+				return {success: true, type: 'directory', path};
+			}
+
+			const dir = normalizePath(dirname(path));
+			if(!existsSync(dir)) mkdirSync(dir, {recursive: true});
+
+			if(find && existsSync(path)) {
+				const existing = readFileSync(path, 'utf-8');
+				const regexMatch = find.match(/^\/(.+)\/([gimuy]*)$/);
+				const pattern = regexMatch ? new RegExp(regexMatch[1], regexMatch[2]) : find;
+
+				if(!existing.match(pattern)) return {error: 'Find pattern not found in file'};
+
+				const updated = existing.replace(pattern, content);
+				writeFileSync(path, updated, 'utf-8');
+				return {success: true, type: 'file', path, replaced: true, content: updated};
+			}
+
+			writeFileSync(path, content, 'utf-8');
+			return {success: true, type: 'file', path, content};
+		}
+	}
+}
+
+export const GetPathsTool: AiTool = {
+	name: 'get_paths',
+	description: 'Get the current working directory, and paths to the users home directory',
+	fn: async () => {
+		return {
+			home: os.homedir(),
+			cwd: process.cwd()
+		};
+	}
+}
+
+export const GetDatetimeTool: AiTool = {
+	name: 'get_datetime',
+	description: 'Get local/UTC timestamp',
+	args: {
+		timezone: {type: 'string', description: 'Which timezone to return, defaults to local', enum: ['local', 'utc'], default: 'local'}
+	},
+	fn: ({timezone}) => new Date()[timezone === 'local' ? 'toString' : 'toUTCString']()
+}
+
+export const GetDevice: AiTool = {
+	name: 'get_device',
+	description: 'Get comprehensive system information including hostname, specs, load, storage, and network status',
+	args: {},
+	fn: async () => {
+		const platform = os.platform();
+		const hostname = os.hostname();
+
+		// CPU Info
+		const cpus = os.cpus();
+		const cpuModel = cpus[0].model;
+		const cpuCores = cpus.length;
+
+		// Memory Info
+		const totalMem: any = (os.totalmem() / 1024 / 1024 / 1024).toFixed(2);
+		const freeMem: any = (os.freemem() / 1024 / 1024 / 1024).toFixed(2);
+		const usedMem: any = (totalMem - freeMem).toFixed(2);
+		const memUsage: any = ((usedMem / totalMem) * 100).toFixed(1);
+
+		// Load Average (not available on Windows)
+		const loadAvg = platform === 'win32' ? ['N/A', 'N/A', 'N/A'] : os.loadavg().map(l => l.toFixed(2));
+
+		// Storage Usage
+		let storage = {};
+		if(platform === 'win32') {
+			const ps = $Sync`powershell "Get-PSDrive C | Select-Object Used,Free | ConvertTo-Json"`.trim();
+			const drive = JSON.parse(ps);
+			const used: any = (drive.Used / 1024 / 1024 / 1024).toFixed(2);
+			const free: any = (drive.Free / 1024 / 1024 / 1024).toFixed(2);
+			const total: any = (parseFloat(used) + parseFloat(free)).toFixed(2);
+			const usage: any = ((used / total) * 100).toFixed(1);
+			storage = {
+				filesystem: 'C:',
+				size: `${total} GB`,
+				used: `${used} GB`,
+				available: `${free} GB`,
+				usage: `${usage}%`
+			};
+		} else {
+			const df = $Sync`df -h / | tail -1`.trim();
+			const s = df.split(/\s+/);
+			storage = {
+				filesystem: s[0],
+				size: s[1],
+				used: s[2],
+				available: s[3],
+				usage: s[4]
+			};
+		}
+
+		// Network Status
+		const interfaces = os.networkInterfaces();
+		const activeIfaces = Object.entries(interfaces)
+			.filter(([name]) => name !== 'lo' && !name.includes('Loopback'))
+			.map(([name, addrs]) => {
+				const ipv4 = addrs?.find(a => a.family === 'IPv4');
+				return ipv4 ? {name, ip: ipv4.address} : null;
+			})
+			.filter(Boolean);
+
+		// Internet connectivity check
+		let internet = false;
+		try {
+			if(platform === 'win32') {
+				$Sync`powershell "Test-Connection -ComputerName 8.8.8.8 -Count 1 -Quiet"`;
+			} else {
+				$Sync`ping -c 1 -W 2 8.8.8.8 > /dev/null 2>&1`;
+			}
+			internet = true;
+		} catch {}
+
+		// Uptime
+		const uptime = os.uptime();
+		const days = Math.floor(uptime / 86400);
+		const hours = Math.floor((uptime % 86400) / 3600);
+		const minutes = Math.floor((uptime % 3600) / 60);
+
+		return {
+			hostname,
+			cpu: {
+				model: cpuModel,
+				cores: cpuCores
+			},
+			memory: {
+				total: `${totalMem} GB`,
+				used: `${usedMem} GB`,
+				free: `${freeMem} GB`,
+				usage: `${memUsage}%`
+			},
+			load: {
+				'1min': loadAvg[0],
+				'5min': loadAvg[1],
+				'15min': loadAvg[2]
+			},
+			storage,
+			network: {
+				interfaces: activeIfaces,
+				internet: internet ? 'connected' : 'disconnected'
+			},
+			uptime: `${days}d ${hours}h ${minutes}m`,
+			platform: `${os.type()} ${os.release()}`
+		};
+	}
+}
+
+export const GeoCodeTool: AiTool = {
+	name: 'geo_code',
+	description: 'Converts coordinates to address OR vice versa',
+	args: {
+		query: {type: 'string', description: 'Search query - coordinates (lat,lon) or address string', required: true},
+	},
+	fn: async ({query}) => {
+		const coordinates = /(-?\d+(?:\.\d+)?).*?,.*?(-?\d+(?:\.\d+)?)/.exec(query);
+		if(coordinates) { // Geolocate
+			const url = `https://nominatim.openstreetmap.org/reverse?format=json&lat=${encodeURIComponent(coordinates[1])}&lon=${encodeURIComponent(coordinates[2])}`;
+			const response = await fetch(url, {headers: {'User-Agent': 'OpenSight/1.0', 'Accept-Language': 'en'}});
+			const data = await response.json();
+			if(data.display_name) return {address: data.display_name, mode: 'geolocate'};
+		} else { // Geocode
+			const url = `https://nominatim.openstreetmap.org/search?format=json&q=${encodeURIComponent(query)}`;
+			const response = await fetch(url, {headers: {'User-Agent': 'OpenSight/1.0'}});
+			const data = await response.json();
+			if(data[0]) return {latitude: parseFloat(data[0].lat), longitude: parseFloat(data[0].lon), mode: 'geocode'};
+		}
+		return {error: 'Not found'};
+	},
+}
+
+export const GeoWeatherTool: AiTool = {
+	name: 'geo_weather',
+	description: 'Gets weather and air quality info for a location and time',
+	args: {
+		query: {type: 'string', description: 'Location - address or place name', required: true},
+		day: {type: 'string', description: 'Date to retrieve (YYYY-MM-DD), defaults to today'},
+	},
+	fn: async ({query, day}) => {
+		day = day || new Date().toISOString().slice(0, 10);
+
+		const geoUrl = `https://nominatim.openstreetmap.org/search?format=json&q=${encodeURIComponent(query)}`;
+		const geoResponse = await fetch(geoUrl, {headers: {'User-Agent': 'OpenSight/1.0'}});
+		const geoData = await geoResponse.json();
+		if(!geoData[0]) return {error: 'Location not found'};
+
+		const lat = parseFloat(geoData[0].lat);
+		const lon = parseFloat(geoData[0].lon);
+
+		const weatherUrl = `https://api.open-meteo.com/v1/forecast?latitude=${lat}&longitude=${lon}&start_date=${day}&end_date=${day}&daily=weathercode,temperature_2m_max,temperature_2m_min,apparent_temperature_max,apparent_temperature_min,precipitation_sum,precipitation_probability_max,windspeed_10m_max,winddirection_10m_dominant,uv_index_max,sunrise,sunset&timezone=auto`;
+		const airUrl = `https://air-quality-api.open-meteo.com/v1/air-quality?latitude=${lat}&longitude=${lon}&start_date=${day}&end_date=${day}&hourly=us_aqi,european_aqi,pm10,pm2_5&timezone=auto`;
+
+		const [weatherResponse, airResponse] = await Promise.all([fetch(weatherUrl), fetch(airUrl)]);
+		const weatherData = await weatherResponse.json();
+		const airData = await airResponse.json();
+
+		const avg = arr => (arr && arr.length) ? arr.reduce((a, b) => a + b, 0) / arr.length : null;
+
+		return {
+			location: geoData[0].display_name,
+			latitude: lat,
+			longitude: lon,
+			elevation: weatherData.elevation,
+			date: day,
+			weatherCode: weatherData.daily?.weathercode?.[0],
+			tempMax: weatherData.daily?.temperature_2m_max?.[0],
+			tempMin: weatherData.daily?.temperature_2m_min?.[0],
+			feelsLikeMax: weatherData.daily?.apparent_temperature_max?.[0],
+			feelsLikeMin: weatherData.daily?.apparent_temperature_min?.[0],
+			precipitation: weatherData.daily?.precipitation_sum?.[0],
+			precipitationChance: weatherData.daily?.precipitation_probability_max?.[0],
+			windSpeedMax: weatherData.daily?.windspeed_10m_max?.[0],
+			windDirection: weatherData.daily?.winddirection_10m_dominant?.[0],
+			uvIndexMax: weatherData.daily?.uv_index_max?.[0],
+			sunrise: weatherData.daily?.sunrise?.[0],
+			sunset: weatherData.daily?.sunset?.[0],
+			usAqi: avg(airData.hourly?.us_aqi),
+			europeanAqi: avg(airData.hourly?.european_aqi),
+			pm10: avg(airData.hourly?.pm10),
+			pm2_5: avg(airData.hourly?.pm2_5),
+		};
+	},
+}
+
+export const NetFetchTool: AiTool = {
+	name: 'net_fetch',
 	description: 'Make HTTP request to URL',
 	args: {
 		url: {type: 'string', description: 'URL to fetch', required: true},
@@ -107,30 +544,59 @@ export const FetchTool: AiTool = {
 	}) => new Http({url: args.url, headers: args.headers}).request({method: args.method || 'GET', body: args.body})
 }

-export const JSTool: AiTool = {
-	name: 'exec_javascript',
-	description: 'Execute commonjs javascript',
+export const NetFlareSolverTool = (host: string) => {
+	return {
+		name: 'net_flaresolverr',
+		description: 'Use a flaresolverr proxy to bypass cloudflare bot detection',
 		args: {
-		code: {type: 'string', description: 'CommonJS javascript', required: true}
+			url: {type: 'string', description: 'URL to fetch', required: true},
+			cmd: {type: 'string', description: 'Flaresolverr cmd', enum: ['request.get', 'request.post'], default: 'request.get'},
+			maxTimeout: {type: 'number', description: 'Fetch time limit', default: 60_000},
+			postData: {type: 'object', description: 'Data to send during request.post requests'},
 		},
-	fn: async (args: {code: string}) => {
-		const c = consoleInterceptor(null);
-		const resp = await Fn<any>({console: c}, args.code, true).catch((err: any) => c.output.error.push(err));
-		return {...c.output, return: resp, stdout: undefined, stderr: undefined};
+		fn: async ({url, cmd, maxTimeout, postData}) => {
+			function toFormUrlEncoded(obj, prefix = '') {
+				const pairs: any = [];
+				for (const key in obj) {
+					if (!obj.hasOwnProperty(key)) continue;
+
+					const value = obj[key];
+					const encodedKey = prefix
+						? `${prefix}[${encodeURIComponent(key)}]`
+						: encodeURIComponent(key);
+
+					if (value === null || value === undefined) {
+						pairs.push(`${encodedKey}=`);
+					} else if (typeof value === 'object' && !Array.isArray(value)) {
+						pairs.push(toFormUrlEncoded(value, encodedKey));
+					} else if (Array.isArray(value)) {
+						value.forEach(item => {
+							pairs.push(`${encodedKey}[]=${encodeURIComponent(item)}`);
+						});
+					} else {
+						pairs.push(`${encodedKey}=${encodeURIComponent(value)}`);
 					}
 				}

-export const PythonTool: AiTool = {
-	name: 'exec_javascript',
-	description: 'Execute commonjs javascript',
-	args: {
-		code: {type: 'string', description: 'CommonJS javascript', required: true}
-	},
-	fn: async (args: {code: string}) => ({result: $Sync`python -c "${args.code}"`})
+				return pairs.join('&');
 			}

-export const ReadWebpageTool: AiTool = {
-	name: 'read_webpage',
+			const res = await fetch(host + '/v1', {
+				method: 'POST',
+				headers: {'Content-Type': 'application/json'},
+				body: JSON.stringify({cmd, url, maxTimeout, postData: postData ? toFormUrlEncoded(postData) : undefined}),
+			});
+
+			if(!res.ok) throw new Error(`FlareSolverr HTTP error: ${res.status} ${res.statusText}`);
+			const data = await res.json();
+			if(data.status !== 'ok') throw new Error(`FlareSolverr error: ${data.message ?? data.status}`);
+			return data.solution.response;
+		}
+	}
+}
+
+export const NetReadTool: AiTool = {
+	name: 'net_read',
 	description: 'Extract clean content from webpages, or convert media/documents to accessible formats',
 	args: {
 		url: {type: 'string', description: 'URL to read', required: true},
@@ -233,8 +699,8 @@ export const ReadWebpageTool: AiTool = {
 	}
 };

-export const WebSearchTool: AiTool = {
-	name: 'web_search',
+export const NetSearchTool: AiTool = {
+	name: 'net_search',
 	description: 'Use duckduckgo (anonymous) to find find relevant online resources. Returns a list of URLs that works great with the `read_webpage` tool',
 	args: {
 		query: {type: 'string', description: 'Search string', required: true},
@@ -259,93 +725,95 @@ export const WebSearchTool: AiTool = {
 	}
 }

+export const WikipediaTool: AiTool = {
+	name: 'get_wikipedia',
+	description: 'Search Wikipedia for matching articles',
+	args: {
+		query: {type: 'string', description: 'Search term or article title', required: true},
+		mode: {type: 'string', description: 'search - look for articles, summary - intro of first found article (default), full - complete first found article', enum: ['search', 'summary', 'full'], default: 'summary'},
+		ua: {type: 'string', description: 'User Agent'},
+	},
+	fn: async ({query, mode, ua}) => {
 		class WikipediaClient {
-	private async get(url: string): Promise<any> {
-		const resp = await fetch(url, {headers: {'User-Agent': UA}});
+			useragent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)';
+
+			constructor(useragent: string) {
+				this.useragent = useragent;
+			}
+
+			async get(url) {
+				const resp = await fetch(url, {headers: {'User-Agent': this.useragent}});
 				return resp.json();
 			}

-	private api(params: Record<string, any>): Promise<any> {
+			api(params) {
 				const qs = new URLSearchParams({...params, format: 'json', utf8: '1'}).toString();
 				return this.get(`https://en.wikipedia.org/w/api.php?${qs}`);
 			}

-	private clean(text: string): string {
-		return text.replace(/\n{3,}/g, '\n\n').replace(/ {2,}/g, ' ').replace(/\[\d+\]/g, '').trim();
+			clean(text) {
+				const cutoffs = ['== See also ==', '== References ==', '== Bibliography ==', '== External links =='];
+				for (const marker of cutoffs) {
+					const idx = text.indexOf(marker);
+					if (idx !== -1) text = text.slice(0, idx);
 				}

-	private truncate(text: string, max: number): string {
-		if(text.length <= max) return text;
-		const cut = text.slice(0, max);
-		const lastPara = cut.lastIndexOf('\n\n');
-		return lastPara > max * 0.7 ? cut.slice(0, lastPara) : cut;
+				return text
+					.replace(/^={4}\s*(.+?)\s*={4}$/gm, '#### $1')
+					.replace(/^={3}\s*(.+?)\s*={3}$/gm, '### $1')
+					.replace(/^={2}\s*(.+?)\s*={2}$/gm, '## $1')
+					.replace(/\n{3,}/g, '\n\n')
+					.replace(/ {2,}/g, ' ')
+					.replace(/\[\d+]/g, '')
+					.trim();
 			}

-	private async searchTitles(query: string, limit = 6): Promise<any[]> {
+			async searchTitles(query: string, limit = 6) {
 				const data = await this.api({action: 'query', list: 'search', srsearch: query, srlimit: limit, srprop: 'snippet'});
 				return data.query?.search || [];
 			}

-	private async fetchExtract(title: string, intro = false): Promise<string> {
+			async fetchExtract(title: string, introOnly = false) {
 				const params: any = {action: 'query', prop: 'extracts', titles: title, explaintext: 1, redirects: 1};
-		if(intro) params.exintro = 1;
+				if(introOnly) params.exintro = 1;
 				const data = await this.api(params);
-		const page = Object.values(data.query?.pages || {})[0] as any;
+				const page: any = Object.values(data.query?.pages || {})[0];
 				return this.clean(page?.extract || '');
 			}

-	private pageUrl(title: string): string {
+			pageUrl(title: string) {
 				return `https://en.wikipedia.org/wiki/${encodeURIComponent(title.replace(/ /g, '_'))}`;
 			}

-	private stripHtml(text: string): string {
+			stripHtml(text: string) {
 				return text.replace(/<[^>]+>/g, '');
 			}

-	async lookup(query: string, detail: 'intro' | 'full' = 'intro'): Promise<string> {
+			async lookup(query: string, detail = 'summary') {
 				const results = await this.searchTitles(query, 6);
 				if(!results.length) return `❌ No Wikipedia articles found for "${query}"`;
 				const title = results[0].title;
 				const url = this.pageUrl(title);
-		const content = await this.fetchExtract(title, detail === 'intro');
-		const text = this.truncate(content, detail === 'intro' ? 2000 : 8000);
-		return `## ${title}\n🔗 ${url}\n\n${text}`;
+				const introOnly = detail !== 'full';
+				const content = await this.fetchExtract(title, introOnly);
+				return `## ${title}\n🔗 ${url}\n\n${content}`;
 			}

-	async search(query: string): Promise<string> {
+			async search(query: string) {
 				const results = await this.searchTitles(query, 8);
 				if(!results.length) return `❌ No results for "${query}"`;
 				const lines = [`### Search results for "${query}"\n`];
 				for(let i = 0; i < results.length; i++) {
 					const r = results[i];
-			const snippet = this.truncate(this.stripHtml(r.snippet || ''), 150);
+					const snippet = this.stripHtml(r.snippet || '').trim();
 					lines.push(`**${i + 1}. ${r.title}**\n${snippet}\n${this.pageUrl(r.title)}`);
 				}
 				return lines.join('\n\n');
 			}
 		}

-export const WikipediaLookupTool: AiTool = {
-	name: 'wikipedia_lookup',
-	description: 'Get Wikipedia article content',
-	args: {
-		query: {type: 'string', description: 'Topic or article title', required: true},
-		detail: {type: 'string', description: 'Content level: "intro" (summary, default) or "full" (complete article)', enum: ['intro', 'full'], default: 'intro'}
-	},
-	fn: async (args: {query: string; detail?: 'intro' | 'full'}) => {
-		const wiki = new WikipediaClient();
-		return wiki.lookup(args.query, args.detail || 'intro');
-	}
-};
-
-export const WikipediaSearchTool: AiTool = {
-	name: 'wikipedia_search',
-	description: 'Search Wikipedia for matching articles',
-	args: {
-		query: {type: 'string', description: 'Search terms', required: true}
-	},
-	fn: async (args: {query: string}) => {
-		const wiki = new WikipediaClient();
-		return wiki.search(args.query);
+		const wiki = new WikipediaClient(ua);
+		if(mode === 'search') return wiki.search(query);
+		return wiki.lookup(query, mode || 'summary');
 	}
 };
--- a/src/vision.ts
+++ b/src/vision.ts
@@ -12,12 +12,31 @@ export class Vision {
 	 */
 	ocr(path: string): AbortablePromise<string | null> {
 		let worker: any;
-		const p = new Promise<string | null>(async res => {
+		let reject: (err: any) => void;
+
+		const handler = (err: Error) => {
+			if(err.stack?.includes('tesseract.js')) {
+				process.off('uncaughtException', handler);
+				reject?.(err);
+				return;
+			}
+			throw err;
+		};
+		process.on('uncaughtException', handler);
+
+		const p = (async () => {
 			worker = await createWorker(this.ai.options.ocr || 'eng', 2, {cachePath: this.ai.options.path});
-			const {data} = await worker.recognize(path);
-			await worker.terminate();
-			res(data.text.trim() || null);
+			return await new Promise<string | null>((res, rej) => {
+				reject = rej;
+				worker.recognize(path)
+					.then(({data}: any) => res(data.text.trim() || null))
+					.catch(rej);
 			});
+		})().finally(() => {
+			process.off('uncaughtException', handler);
+			worker?.terminate();
+		});
+
 		return Object.assign(p, {abort: () => worker?.terminate()});
 	}
 }
--- a/tsconfig.json
+++ b/tsconfig.json
@@ -4,7 +4,10 @@
 		"target": "ESNext",
 		"useDefineForClassFields": true,
 		"module": "ESNext",
-		"lib": ["ESNext"],
+		"lib": [
+			"ESNext",
+			"dom"
+		],
 		"skipLibCheck": true,

 		/* Bundler mode */
Author	SHA1	Message	Date
ztimson	dda2d4c2a3	Bump 1.2.8 All checks were successful Publish Library / Build NPM Project (push) Successful in 49s Details Publish Library / Tag Version (push) Successful in 7s Details	2026-07-29 22:35:29 -04:00
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