Proper error handling for OCR

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
 

main.mjs

@@ -0,0 +1,25 @@
+import {Ai} from './dist/index.mjs';
+
+const ai = new Ai({
+	path: './',
+	llm: {
+		system: 'You are a testbed for developing an AI library',
+		models: {
+			'qwen/qwen3.5-9b': {proto: 'openai', host: 'http://127.0.0.1:1234/v1'}
+		}
+	}
+});
+
+const skills = [{
+	name: 'Momentum',
+	description: 'Learn how to use the Momentum API',
+	content: 'You can initialize it with: new Momentum(url);'
+}];
+
+const history = [], memory = [];
+await ai.language.ask('My favorite color is red', {history, memory});
+await ai.language.updateMemory(history, memory);
+
+history.splice(0, history.length);
+console.log(await ai.language.ask('Whats my favorite color?', {history, memory}));
+console.log(history);

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "0.8.16",
+	"version": "1.0.5",
 	"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"

src/ai.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;

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';
 
@@ -155,7 +154,7 @@ 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 +225,11 @@ 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});

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();
 });

src/index.ts

@@ -2,6 +2,7 @@ export * from './ai';
 export * from './antrhopic';
 export * from './audio';
 export * from './llm';
+export * from './memory';
 export * from './open-ai';
 export * from './provider';
 export * from './tools';

src/llm.ts

@@ -1,5 +1,3 @@
-import {sum} from '@tensorflow/tfjs';
-import {JSONAttemptParse} from '@ztimson/utils';
 import {AbortablePromise, Ai} from './ai.ts';
 import {Anthropic} from './antrhopic.ts';
 import {OpenAi} from './open-ai.ts';
@@ -7,7 +5,8 @@ import {LLMProvider} from './provider.ts';
 import {AiTool} from './tools.ts';
 import {fileURLToPath} from 'url';
 import {dirname, join} from 'path';
-import { spawn } from 'node:child_process';
+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};
@@ -37,16 +36,6 @@ 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 = {
 	/** System prompt */
 	system?: string;
@@ -63,17 +52,39 @@ 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[];
+	/** 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} = {};
 
@@ -85,14 +96,67 @@ class LLM {
 			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: 'read_skill',
+				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: '',
@@ -106,85 +170,67 @@ class LLM {
 		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 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}: ${m.fact}`).join('\n');
+				const relevant = await this.memoryManager.recollect(message, options.memory, 1);
+				prompts.unshift(`You have access to the following memory files:
+${options.memory.map(m => `- ${m.name}: ${m.description}`).join('\n')}
+${relevant.length ? `
+The closest memory has been added primitively:
+\`\`\`
+Name: ${relevant[0].name}
+Description: ${relevant[0].description}
+${relevant[0].content}
+\`\`\`
+`: ''}`.trim());
+				tools.push(this.memoryManager.tools.read(<Memory[]>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 || []];
+			prompts.unshift(options.system || this.ai.options.llm?.system || '');
+			const resp = await this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
+
+			// 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, 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});
 	}
 
-	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[], options: LLMRequest = {}): Promise<void> {
+		await this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
 	}
 
 	/**
@@ -273,7 +319,7 @@ 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 +327,6 @@ 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 +335,6 @@ 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 +344,7 @@ 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 +364,7 @@ class LLM {
 			}
 			return results;
 		})();
-		return Object.assign(p, { abort });
+		return <any>Object.assign(p, {abort});
 	}
 
 	/**
@@ -346,8 +390,8 @@ 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}
+		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 +461,31 @@ class LLM {
 			if(!done) reject(`AI failed to create summary:\n${resp}`);
 		});
 	}
+
+	addModel(name: string, config: AnthropicConfig | OllamaConfig | OpenAiConfig, setDefault = false) {
+		if(config.proto == 'anthropic') this.models[name] = new Anthropic(this.ai, config.token, name);
+		else if(config.proto == 'ollama') this.models[name] = new OpenAi(this.ai, config.host, 'not-needed', 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 | OllamaConfig | 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 == '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.defaultModel = Object.keys(this.models)[0] ?? '';
+	}
 }
 
 export default LLM;

src/memory.ts

@@ -0,0 +1,177 @@
+// memory.ts
+import {LLMRequest, LLMMessage} from './llm.ts';
+import {AiTool} from './tools.ts';
+
+/** Background information the AI will be fed as a knowledge document */
+export type Memory = {
+	/** Memory subject */
+	name: string;
+	/** Short description of what this document contains - used for RAG retrieval */
+	description: string;
+	/** Full markdown content of the document */
+	content: string;
+	/** Embedding vector of the description - used for similarity search */
+	embedding: number[];
+}
+
+export type MemoryCollection = {
+	/** Memory subject */
+	name: string;
+	/** Short description - required if isNew */
+	description?: string;
+	/** Extracted facts to merge */
+	facts: string[];
+}
+
+export class MemoryManager {
+
+	tools = {
+		edit: (memory: Memory): AiTool => ({
+			name: 'edit_memory',
+			description: 'Edit a memory. Omit start/end to append. Pass start only to replace from that line on (Note line 0 = first line of content / line AFTER description). Pass start+end to replace a specific range. start=0 replaces the whole document. Returns updated document',
+			args: {
+				content: {type: 'string', description: 'New content', required: true},
+				start: {type: 'number', description: 'First line to replace (0-indexed, inclusive). Omit to append.'},
+				end: {type: 'number', description: 'Last line to replace (0-indexed, inclusive). Omit to replace from start to end of doc.'},
+			},
+			fn: (args: any) => {
+				const lines = memory.content ? memory.content.split('\n') : [];
+				const newLines = args.content.split('\n');
+				if(args.start === undefined) lines.push(...newLines);
+				else if(args.end === undefined) lines.splice(args.start, lines.length - args.start, ...newLines);
+				else lines.splice(args.start, args.end - args.start + 1, ...newLines);
+				memory.content = lines.join('\n');
+				return memory.content;
+			}
+		}),
+		extract: (pools: MemoryCollection[]): AiTool => ({
+			name: 'extract_facts',
+			description: 'Extract a list of facts to group into a single memory',
+			args: {
+				name: {type: 'string', description: 'Exact name of an existing memory, or a new name if none fits ([pro]nouns only)', required: true},
+				description: {type: 'string', description: 'One sentence description of the memory subject', required: true},
+				facts: {type: 'string', description: 'Comma separated list of extracted facts', required: true},
+			},
+			fn: (args: any) => {
+				pools.push({
+					name: args.name,
+					description: args.description,
+					facts: args.facts.split(',').map((f: string) => f.trim()).filter(Boolean),
+				});
+				return 'Success';
+			}}),
+		read: (memories: Memory[]): AiTool => ({
+			name: 'read_memory',
+			description: 'Read entire memory',
+			args: {
+				name: {type: 'string', description: 'Exact memory name', required: true},
+			},
+			fn: (args: any) => {
+				const mem = memories.find(m => m.name === args.name);
+				if(!mem) return 'Document not found';
+				return `Name: ${mem.name}\nDescription: ${mem.description}\n\n${mem.content}`;
+			}
+		}),
+	}
+
+	constructor(private llm: any, private model?: string) {}
+
+	/**
+	 * Extracts facts from conversation and groups them into individual memories
+	 * @param {string} conversation Full conversation formatted as [role]: content
+	 * @param {Memory[]} memories The user's memory documents
+	 * @param {LLMRequest} options LLM options
+	 * @returns {Promise<MemoryCollection[]>} Fact pools grouped by target document
+	 */
+	private async extract(conversation: string, memories: Memory[], options: LLMRequest): Promise<MemoryCollection[]> {
+		const existingDocs = memories.map(m => `Name: ${m.name}\nDescription: ${m.description}`).join('\n\n');
+		const pools: MemoryCollection[] = [];
+		await this.llm.ask(conversation, {
+			model: this.model || options.model,
+			temperature: 0.2,
+			system: `You are a fact extractor. Analyze this conversation and extract facts worth remembering long term.
+Rules:
+- ONLY extract facts the USER explicitly stated about themselves or their business
+- ONLY extract decisions that were MADE during this conversation
+- DO NOT extract anything the AI said, its name, capabilities, or how it introduced itself
+- DO NOT extract greetings, pleasantries or generic exchanges
+- If nothing worth remembering was said, dont do anything, skip calling tools
+
+For each fact decide whether it belongs in an existing document or needs a new one, then call the \`extract_facts\` tool.
+
+Existing documents:\n${existingDocs || 'None yet.'}`,
+			tools: [this.tools.extract(pools)]
+		});
+		return pools;
+	}
+
+	/**
+	 * Bot 2 - Editor: merges a pool of facts into a specific document using surgical line-based edits.
+	 * Receives full document content and uses read + amend tools to make precise edits.
+	 * @param {MemoryCollection} newMem The fact pool to merge
+	 * @param {Memory[]} memories The user's memory documents
+	 * @param {LLMRequest} options LLM options
+	 */
+	private async edit(newMem: MemoryCollection, memories: Memory[], options: LLMRequest): Promise<void> {
+		const existing = memories.find(m => m.name === newMem.name);
+		const mem: Memory = existing || {name: newMem.name, description: newMem.description || '', content: '', embedding: []};
+		const isNew = !existing;
+
+		await this.llm.ask(newMem.facts.map(f => `- ${f}`).join('\n'),
+			{
+				model: this.model || options.model,
+				temperature: 0.2,
+				system: `You are a document editor. Merge the users list of facts into the following document using the \`edit_memory\` tool; call it as many times as necessary:
+\`\`\`
+${mem.content}
+\`\`\``,
+				tools: [this.tools.edit(mem)]
+			}
+		);
+
+		if(isNew || mem.description !== existing?.description) {
+			const e = await this.llm.embedding(mem.description);
+			mem.embedding = e?.[0]?.embedding;
+		}
+
+		if(isNew) memories.push(mem);
+		else {
+			const idx = memories.findIndex(m => m.name === newMem.name);
+			if(idx >= 0) memories[idx] = mem;
+		}
+	}
+
+	/**
+	 * Find relevant memory documents for a query using description embeddings
+	 * @param {string} query The query to search against
+	 * @param {Memory[]} memories The user's memory documents
+	 * @param {number} limit Max number of results to return
+	 * @returns {Promise<Memory[]>} The most relevant memory documents
+	 */
+	async recollect(query: string, memories: Memory[], limit = 5): Promise<Memory[]> {
+		const [e] = await this.llm.embedding(query);
+		return memories
+			.filter(m => m.embedding?.length)
+			.map(m => ({...m, score: this.llm.cosineSimilarity(m.embedding, e.embedding)}))
+			.toSorted((a: any, b: any) => b.score - a.score)
+			.slice(0, limit);
+	}
+
+	/**
+	 * Two-stage memory pipeline: classify facts from conversation history then surgically merge them into documents.
+	 * Bot 1 (classify) extracts and groups facts cheaply. Bot 2 (edit) runs per-document in parallel with full content access.
+	 * @param {LLMMessage[]} history Full conversation history to digest
+	 * @param {Memory[]} memories The user's memory documents — mutated in place
+	 * @param {LLMRequest} options LLM options
+	 */
+	async memorize(history: LLMMessage[], memories: Memory[], options: LLMRequest): Promise<void> {
+		const conversation = history
+			.filter(h => h.role === 'user' || h.role === 'assistant')
+			.map(h => `[${h.role}]: ${h.content}`)
+			.join('\n\n');
+		if(!conversation.trim()) return;
+		const pools = await this.extract(conversation, memories, options);
+		if(!pools.length) return;
+		await Promise.all(pools.map(pool => this.edit(pool, memories, options)));
+	}
+}

src/open-ai.ts

@@ -138,6 +138,7 @@ 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);
@@ -157,7 +158,7 @@ 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 || ''});
+			history.push({role: 'assistant', content: resp.choices[0].message.content.trim() || ''});
 			history = this.toStandard(history);
 
 			if(options.stream) options.stream({done: true});

src/vision.ts

@@ -12,12 +12,16 @@ export class Vision {
 	 */
 	ocr(path: string): AbortablePromise<string | null> {
 		let worker: any;
-		const p = new Promise<string | null>(async res => {
+		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);
+			worker.setParameters({}).catch(() => {}); // force error handler attachment
+			return await new Promise<string | null>((res, rej) => {
+				worker.on?.('error', rej); // catch worker-level throws
+				worker.recognize(path)
+					.then(({data}: any) => res(data.text.trim() || null))
+					.catch(rej);
 			});
+		})().finally(() => worker?.terminate());
 		return Object.assign(p, {abort: () => worker?.terminate()});
 	}
 }