Agent/subagent support

main.mjs

@@ -1,21 +0,0 @@
-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 = [];
-console.log(await ai.language.ask('Can you tell me how to use momentum?', {history, skills}));
-console.log(history, memory);

package-lock.json

@@ -1,12 +1,12 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "1.0.0",
+	"version": "1.2.6",
 	"lockfileVersion": 3,
 	"requires": true,
 	"packages": {
 		"": {
 			"name": "@ztimson/ai-utils",
-			"version": "1.0.0",
+			"version": "1.2.6",
 			"license": "MIT",
 			"dependencies": {
 				"@anthropic-ai/sdk": "^0.102.0",
@@ -57,34 +57,38 @@
 			}
 		},
 		"node_modules/@emnapi/core": {
-			"version": "1.10.0",
+			"version": "2.0.0-alpha.3",
-			"resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.10.0.tgz",
+			"resolved": "https://registry.npmjs.org/@emnapi/core/-/core-2.0.0-alpha.3.tgz",
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 			"dev": true,
 			"license": "MIT",
 			"optional": true,
+			"peer": true,
 			"dependencies": {
-				"@emnapi/wasi-threads": "1.2.1",
+				"@emnapi/wasi-threads": "2.0.1",
 				"tslib": "^2.4.0"
 			}
 		},
 		"node_modules/@emnapi/runtime": {
-			"version": "1.10.0",
+			"version": "2.0.0-alpha.3",
-			"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.10.0.tgz",
+			"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-2.0.0-alpha.3.tgz",
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+			"dev": true,
 			"license": "MIT",
 			"optional": true,
+			"peer": true,
 			"dependencies": {
 				"tslib": "^2.4.0"
 			}
 		},
 		"node_modules/@emnapi/wasi-threads": {
-			"version": "1.2.1",
+			"version": "2.0.1",
-			"resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.2.1.tgz",
+			"resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-2.0.1.tgz",
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 			}
@@ -573,6 +577,16 @@
 				"url": "https://opencollective.com/libvips"
 			}
 		},
+		"node_modules/@img/sharp-wasm32/node_modules/@emnapi/runtime": {
+			"version": "1.11.3",
+			"resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.3.tgz",
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+			"license": "MIT",
+			"optional": true,
+			"dependencies": {
+				"tslib": "^2.4.0"
+			}
+		},
 		"node_modules/@img/sharp-win32-arm64": {
 			"version": "0.34.5",
 			"resolved": "https://registry.npmjs.org/@img/sharp-win32-arm64/-/sharp-win32-arm64-0.34.5.tgz",
@@ -681,28 +695,31 @@
 			}
 		},
 		"node_modules/@napi-rs/wasm-runtime": {
-			"version": "1.1.4",
+			"version": "1.2.0",
-			"resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.1.4.tgz",
+			"resolved": "https://registry.npmjs.org/@napi-rs/wasm-runtime/-/wasm-runtime-1.2.0.tgz",
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 			"dev": true,
 			"license": "MIT",
 			"optional": true,
 			"dependencies": {
-				"@tybys/wasm-util": "^0.10.1"
+				"@tybys/wasm-util": "^0.10.3"
+			},
+			"engines": {
+				"node": "^20.19.0 || ^22.13.0 || >=23.5.0"
 			},
 			"funding": {
 				"type": "github",
 				"url": "https://github.com/sponsors/Brooooooklyn"
 			},
 			"peerDependencies": {
-				"@emnapi/core": "^1.7.1",
+				"@emnapi/core": "^2.0.0-alpha.3",
-				"@emnapi/runtime": "^1.7.1"
+				"@emnapi/runtime": "^2.0.0-alpha.3"
 			}
 		},
 		"node_modules/@oxc-project/types": {
-			"version": "0.133.0",
+			"version": "0.139.0",
-			"resolved": "https://registry.npmjs.org/@oxc-project/types/-/types-0.133.0.tgz",
+			"resolved": "https://registry.npmjs.org/@oxc-project/types/-/types-0.139.0.tgz",
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 			"license": "MIT",
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@@ -748,12 +765,6 @@
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 			"license": "BSD-3-Clause"
 		},
-		"node_modules/@protobufjs/inquire": {
-			"version": "1.1.2",
-			"resolved": "https://registry.npmjs.org/@protobufjs/inquire/-/inquire-1.1.2.tgz",
-			"integrity": "sha512-pa0vFRuws4wkvaXKK1uXZMAwAX4/t8ANaJo45iw/oQHNQ9q5xUzwgFmVJGXiga2BeN+zpX7Vf9vmsiIa2J+MUw==",
-			"license": "BSD-3-Clause"
-		},
 		"node_modules/@protobufjs/path": {
 			"version": "1.1.2",
 			"resolved": "https://registry.npmjs.org/@protobufjs/path/-/path-1.1.2.tgz",
@@ -767,15 +778,15 @@
 			"license": "BSD-3-Clause"
 		},
 		"node_modules/@protobufjs/utf8": {
-			"version": "1.1.1",
+			"version": "1.1.2",
-			"resolved": "https://registry.npmjs.org/@protobufjs/utf8/-/utf8-1.1.1.tgz",
+			"resolved": "https://registry.npmjs.org/@protobufjs/utf8/-/utf8-1.1.2.tgz",
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 			"license": "BSD-3-Clause"
 		},
 		"node_modules/@rolldown/binding-android-arm64": {
-			"version": "1.0.3",
+			"version": "1.1.5",
-			"resolved": "https://registry.npmjs.org/@rolldown/binding-android-arm64/-/binding-android-arm64-1.0.3.tgz",
+			"resolved": "https://registry.npmjs.org/@rolldown/binding-android-arm64/-/binding-android-arm64-1.1.5.tgz",
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 				"arm64"
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@@ -790,9 +801,9 @@
 			}
 		},
 		"node_modules/@rolldown/binding-darwin-arm64": {
-			"version": "1.0.3",
+			"version": "1.1.5",
-			"resolved": "https://registry.npmjs.org/@rolldown/binding-darwin-arm64/-/binding-darwin-arm64-1.0.3.tgz",
+			"resolved": "https://registry.npmjs.org/@rolldown/binding-darwin-arm64/-/binding-darwin-arm64-1.1.5.tgz",
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 			"cpu": [
 				"arm64"
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@@ -807,9 +818,9 @@
 			}
 		},
 		"node_modules/@rolldown/binding-darwin-x64": {
-			"version": "1.0.3",
+			"version": "1.1.5",
-			"resolved": "https://registry.npmjs.org/@rolldown/binding-darwin-x64/-/binding-darwin-x64-1.0.3.tgz",
+			"resolved": "https://registry.npmjs.org/@rolldown/binding-darwin-x64/-/binding-darwin-x64-1.1.5.tgz",
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 			"cpu": [
 				"x64"
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@@ -824,9 +835,9 @@
 			}
 		},
 		"node_modules/@rolldown/binding-freebsd-x64": {
-			"version": "1.0.3",
+			"version": "1.1.5",
-			"resolved": "https://registry.npmjs.org/@rolldown/binding-freebsd-x64/-/binding-freebsd-x64-1.0.3.tgz",
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@@ -841,9 +852,9 @@
 			}
 		},
 		"node_modules/@rolldown/binding-linux-arm-gnueabihf": {
-			"version": "1.0.3",
+			"version": "1.1.5",
-			"resolved": "https://registry.npmjs.org/@rolldown/binding-linux-arm-gnueabihf/-/binding-linux-arm-gnueabihf-1.0.3.tgz",
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@@ -858,9 +869,9 @@
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 		},
 		"node_modules/@rolldown/binding-linux-arm64-gnu": {
-			"version": "1.0.3",
+			"version": "1.1.5",
-			"resolved": "https://registry.npmjs.org/@rolldown/binding-linux-arm64-gnu/-/binding-linux-arm64-gnu-1.0.3.tgz",
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@@ -878,9 +889,9 @@
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 		"node_modules/@rolldown/binding-linux-arm64-musl": {
-			"version": "1.0.3",
+			"version": "1.1.5",
-			"resolved": "https://registry.npmjs.org/@rolldown/binding-linux-arm64-musl/-/binding-linux-arm64-musl-1.0.3.tgz",
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@@ -898,9 +909,9 @@
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 		"node_modules/@rolldown/binding-linux-ppc64-gnu": {
-			"version": "1.0.3",
+			"version": "1.1.5",
-			"resolved": "https://registry.npmjs.org/@rolldown/binding-linux-ppc64-gnu/-/binding-linux-ppc64-gnu-1.0.3.tgz",
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@@ -918,9 +929,9 @@
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 		},
 		"node_modules/@rolldown/binding-linux-s390x-gnu": {
-			"version": "1.0.3",
+			"version": "1.1.5",
-			"resolved": "https://registry.npmjs.org/@rolldown/binding-linux-s390x-gnu/-/binding-linux-s390x-gnu-1.0.3.tgz",
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@@ -938,9 +949,9 @@
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 		"node_modules/@rolldown/binding-linux-x64-gnu": {
-			"version": "1.0.3",
+			"version": "1.1.5",
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@@ -958,9 +969,9 @@
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@@ -978,9 +989,9 @@
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@@ -1005,18 +1016,52 @@
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 			"optional": true,
 			"dependencies": {
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 				"tinyglobby": "^0.2.17"
 			},
 			"bin": {
@@ -3991,7 +4047,7 @@
 			},
 			"peerDependencies": {
 				"@types/node": "^20.19.0 || >=22.12.0",
-				"@vitejs/devtools": "^0.1.18",
+				"@vitejs/devtools": "^0.3.0",
 				"esbuild": "^0.27.0 || ^0.28.0",
 				"jiti": ">=1.21.0",
 				"less": "^4.0.0",
@@ -4043,13 +4099,13 @@
 			}
 		},
 		"node_modules/vite-plugin-dts": {
-			"version": "5.0.2",
+			"version": "5.0.3",
-			"resolved": "https://registry.npmjs.org/vite-plugin-dts/-/vite-plugin-dts-5.0.2.tgz",
+			"resolved": "https://registry.npmjs.org/vite-plugin-dts/-/vite-plugin-dts-5.0.3.tgz",
-			"integrity": "sha512-lNeHS+dwGju6eRmNvZQt8Shwv9j3m98hbHse/lIbLq9q3yE2DcIOBBYQEVUF6tS0kOmv+VA9Z5FqmzFnGe4U8g==",
+			"integrity": "sha512-gIth6NdCEHWPiiRMCK3N6C8WjvdsrtEQrmsiG8h6Ov+lFP+b07Y+wcs9H0H7n146l0PDTYK4cQN1vgeG1pMdRQ==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
-				"unplugin-dts": "1.0.2"
+				"unplugin-dts": "1.0.3"
 			},
 			"peerDependencies": {
 				"@microsoft/api-extractor": ">=7",
@@ -4169,9 +4225,9 @@
 			}
 		},
 		"node_modules/yargs": {
-			"version": "16.2.0",
+			"version": "16.2.2",
-			"resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.0.tgz",
+			"resolved": "https://registry.npmjs.org/yargs/-/yargs-16.2.2.tgz",
-			"integrity": "sha512-D1mvvtDG0L5ft/jGWkLpG1+m0eQxOfaBvTNELraWj22wSVUMWxZUvYgJYcKh6jGGIkJFhH4IZPQhR4TKpc8mBw==",
+			"integrity": "sha512-Nt9ZJjXTv5R8MHbqby/wXQ6Gi0Bb3TcYZkR1bzuL4yB2OxWPkXknz513gEF0GoA6tn00UpbPvERW8rzCuWCA6w==",
 			"license": "MIT",
 			"dependencies": {
 				"cliui": "^7.0.2",

package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@ztimson/ai-utils",
-	"version": "1.0.1",
+	"version": "1.3.0",
 	"description": "AI Utility library",
 	"author": "Zak Timson",
 	"license": "MIT",

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';
 
@@ -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;

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;
@@ -20,10 +21,10 @@ export class Anthropic extends LLMProvider {
 				messages.push(<any>{timestamp, ...h});
 			} else {
 				const textContent = h.content?.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n');
-				if(textContent) messages.push({timestamp, role: h.role, content: textContent});
+				if(textContent) messages.push({role: h.role, content: textContent, timestamp: timestamp});
 				h.content.forEach((c: any) => {
 					if(c.type == 'tool_use') {
-						messages.push({timestamp, role: 'tool', id: c.id, name: c.name, args: c.input, content: undefined});
+						messages.push({role: 'tool', id: c.id, name: c.name, args: c.input, timestamp: c.timestamp, content: undefined});
 					} else if(c.type == 'tool_result') {
 						const m: any = messages.findLast(m => (<any>m).id == c.tool_use_id);
 						if(m) m[c.is_error ? 'error' : 'content'] = c.content;
@@ -45,10 +46,10 @@ export class Anthropic extends LLMProvider {
 				i++;
 			}
 		}
-		return history.map(({timestamp, ...h}) => h);
+		return history;
 	}
 
-	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,
@@ -72,8 +73,19 @@ export class Anthropic extends LLMProvider {
 				stream: !!options.stream,
 			};
 
-			let resp: any, isFirstMessage = true;
+			// Add structured output support
+			if(options.schema) {
+				requestParams.output_config = {
+					format: {
+						type: 'json_schema',
+						schema: convertSchema(options.schema)
+					}
+				};
+			}
+
+			let resp: any, isFirstMessage = true, terminal = false;
 			do {
+				requestParams.messages = history.map(({timestamp, ...m}) => m);
 				resp = await this.client.messages.create(requestParams).catch(err => {
 					err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
 					throw err;
@@ -102,7 +114,7 @@ export class Anthropic extends LLMProvider {
 							}
 						} else if(chunk.type === 'content_block_stop') {
 							const last = resp.content.at(-1);
-							if(last.input != null) last.input = last.input ? JSONAttemptParse(last.input, {}) : {};
+							if(last?.input != null) last.input = last.input ? JSONAttemptParse(last.input, {}) : {};
 						} else if(chunk.type === 'message_stop') {
 							break;
 						}
@@ -112,28 +124,37 @@ export class Anthropic extends LLMProvider {
 				// Run tools
 				const toolCalls = resp.content.filter((c: any) => c.type === 'tool_use');
 				if(toolCalls.length && !controller.signal.aborted) {
-					history.push({role: 'assistant', content: resp.content});
+					history.push({role: 'assistant', content: resp.content, timestamp: Date.now()});
 					const results = await Promise.all(toolCalls.map(async (toolCall: any) => {
 						const tool = tools.find(findByProp('name', toolCall.name));
 						if(options.stream) options.stream({tool: toolCall.name});
 						if(!tool) return {tool_use_id: toolCall.id, is_error: true, content: 'Tool not found'};
 						try {
-							const result = await tool.fn(toolCall.input, options?.stream, this.ai);
+							// Wrap stream so a tool's `done` ends turn gracefully
+							const toolStream = options.stream && ((chunk: any) => {
+								if(chunk.done) { terminal = true; return; }
+								options.stream!(chunk);
+							});
+							const result = await tool.fn(toolCall.input, toolStream, this.ai);
 							return {type: 'tool_result', tool_use_id: toolCall.id, content: typeof result == 'object' ? JSONSanitize(result) : result};
 						} catch (err: any) {
 							return {type: 'tool_result', tool_use_id: toolCall.id, is_error: true, content: err?.message || err?.toString() || 'Unknown'};
 						}
 					}));
-					history.push({role: 'user', content: results});
+					history.push({role: 'user', content: results, timestamp: Date.now()});
 					requestParams.messages = history;
 				}
-			} while (!controller.signal.aborted && resp.content.some((c: any) => c.type === 'tool_use'));
+			} while (!terminal && !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')});
-			history = this.toStandard(history);
 
+			if(!terminal) {
+				const textContent = resp.content.filter((c: any) => c.type == 'text').map((c: any) => c.text).join('\n\n');
+				history.push({role: 'assistant', content: textContent, timestamp: Date.now()});
+			}
+			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);
+			const finalContent = history.at(-1)?.content;
+			res(options.schema ? JSONAttemptParse(finalContent, finalContent) : finalContent);
 		}), {abort: () => controller.abort()});
 	}
 }

src/audio.ts

@@ -141,11 +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"}', {
+		await this.ai.language.ask(chunks.join('\n'), {
-			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',
+			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;
 	}
 

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);
+	}
+}

src/llm.ts

@@ -1,17 +1,31 @@
+import {snakeCase} from '@ztimson/utils';
 import {AbortablePromise, Ai} from './ai.ts';
 import {Anthropic} from './antrhopic.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';
+import {Memory, MemoryCache, MemoryManager, MemoryOptions} 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 Agent = {
+	name: string;
+	description?: string;
+	model?: string | null;
+	temperature?: number;
+	system: string;
+	delegate?: boolean;
+	skills?: Skill[] | null;
+	tools?: AiTool[] | null;
+	mcp?: McpServer[] | null;
+	/** Explicit whitelist of agents this agent may delegate to. Default: none - must opt-in, self is always excluded */
+	agents?: string[] | null;
+}
+
 export type LLMMessage = {
 	/** Message originator */
 	role: 'assistant' | 'system' | 'user';
@@ -37,6 +51,8 @@ export type LLMMessage = {
 }
 
 export type LLMRequest = {
+	/** Return a parsed JSON object that matches the schema */
+	schema?: AiToolArg;
 	/** System prompt */
 	system?: string;
 	/** Message history */
@@ -54,13 +70,17 @@ export type LLMRequest = {
 	/** Compress old messages in the chat to free up context */
 	compress?: {max: number; min: number};
 	/** User's memory documents - RAG injected automatically each turn */
-	memory?: Memory[];
+	memory?: Memory[] | MemoryCache | MemoryOptions;
 	/** 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[];
+	/** Subagents exposed as delegatable/wrapped tools */
+	agents?: Agent[];
+	/** @internal recursion guard for nested agent delegation */
+	_agentDepth?: number;
 }
 
 export type McpServer = {
@@ -81,6 +101,7 @@ export type Skill = {
 	content: string;
 }
 
+const MAX_AGENT_DEPTH = 5;
 
 class LLM {
 	private memoryManager!: MemoryManager;
@@ -93,12 +114,65 @@ 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);
 	}
 
+	/**
+	 * Wrap agents as tools. Nested delegation is opt-in only (empty by default, like
+	 * tools/skills/mcp) and an agent can never call itself even if explicitly whitelisted.
+	 * Delegate results are queued in `pending` and spliced into history by `ask()` after
+	 * the provider's own end-of-turn history sync has already run.
+	 */
+	private setupAgent(agents: Agent[] = [], allAgents: Agent[], pending: Map<string, {resp: string, subHistory: LLMMessage[]}[]>, aborts: (() => void)[], depth = 0): AiTool[] {
+		return agents.map(a => {
+			const toolName = `${a.delegate ? '' : 'sub'}agent_${snakeCase(a.name)}`;
+			return {
+				name: toolName,
+				description: `${a.delegate ? 'Delegate to ' : ''}Subagent: ${a.description || a.name}`,
+				args: {
+					context: {type: 'string', description: 'Summary of related messages, samples, files, etc...', required: true},
+					instructions: {type: 'string', description: 'Detailed instructions for subagent to complete', required: true},
+				},
+				fn: async (args: any, stream: any) => {
+					if(depth >= MAX_AGENT_DEPTH) return 'Max agent delegation depth exceeded';
+
+					const subHistory: LLMMessage[] = [];
+					// Opt-in only, self always excluded regardless of whitelist
+					const nested = (a.agents || [])
+						.map(name => allAgents.find(x => x.name === name))
+						.filter((x): x is Agent => !!x && x.name !== a.name);
+
+					const request = this.ask(`${args.instructions}${args.context ? `\n\n<context>${args.context}</context>` : ''}`, {
+						system: `You are a specialized subagent. ${a.delegate ? 'Your output streams directly to the user for the remainder of this turn.' : 'You are wrapped in a tool call that will be analysis by an LLM'}
+As a subagent, focus on executing your task completely using available tools and returning only the final result - no commentary, questions, or dialogue.
+
+${a.system}`,
+						model: a.model || undefined,
+						temperature: a.temperature,
+						stream: a.delegate ? stream : undefined,
+						history: subHistory,
+						mcp: a.mcp || undefined,
+						skills: a.skills || undefined,
+						tools: a.tools || undefined,
+						agents: nested,
+						_agentDepth: depth + 1,
+					} as any);
+					aborts.push(request.abort);
+					const resp = await request;
+
+					if(a.delegate) {
+						if(!pending.has(toolName)) pending.set(toolName, []);
+						pending.get(toolName)!.push({resp, subHistory});
+						return '';
+					}
+					return resp;
+				}
+			};
+		});
+	}
+
 	private async setupMcp(servers: McpServer[] = []): Promise<{prompt: string, tools: AiTool[]}> {
 		if(!servers?.length) return {prompt: '', tools: []};
 		const allTools: AiTool[] = [];
@@ -143,7 +217,7 @@ class LLM {
 		return {
 			prompt: `You have access to the following skill documents, use \`read_skill\` to access them:\n${list}`,
 			tools: [{
-				name: 'read_skill',
+				name: 'skill_read',
 				description: 'Read the full content of a skill/knowledge document',
 				args: {
 					name: {type: 'string', description: 'Exact skill name', required: true}
@@ -160,7 +234,6 @@ class LLM {
 	ask(message: string, options: LLMRequest = {}): AbortablePromise<string> {
 		options = <any>{
 			system: '',
-			temperature: 0.8,
 			...this.ai.options.llm,
 			models: undefined,
 			history: [],
@@ -168,11 +241,19 @@ class LLM {
 		}
 		const m = options.model || this.defaultModel;
 		if(!this.models[m]) throw new Error(`Model does not exist: ${m}`);
-		let abort = () => {};
+		let request: AbortablePromise<string> | null = null;
-		return Object.assign(new Promise<string>(async res => {
+		let aborted = false;
+		const nestedAborts: (() => void)[] = [];
+		const abort = () => {
+			aborted = true;
+			request?.abort?.();
+			nestedAborts.forEach(a => a());
+		};
+
+		const promise = (async () => {
 			let tools: AiTool[] = options.tools || this.ai.options.llm?.tools || [];
-			const prompts: string[] = [options.system || this.ai.options.llm?.system || ''];
+			const prompts: string[] = [];
-			if(!options.history) options.history = [];
+			let history = options.history || [];
 
 			// MCP
 			const mcp = options.mcp || this.ai.options?.llm?.mcp;
@@ -190,44 +271,96 @@ class LLM {
 				tools.push(...s.tools);
 			}
 
+			// Agents
+			const agents = options.agents || this.ai.options?.llm?.agents;
+			const pendingDelegates = new Map<string, {resp: string, subHistory: LLMMessage[]}[]>();
+			if(agents?.length) tools.push(...this.setupAgent(agents, agents, pendingDelegates, nestedAborts, options._agentDepth || 0));
+
 			// Memory
-			if(options.memory) {
+			const mem = MemoryManager.normalize(options.memory);
-				const relevant = await this.memoryManager.recollect(message, options.memory);
+			if(mem) {
-				if(relevant.length) {
+				const mems = mem.memory instanceof MemoryCache ? mem.memory.memories : mem.memory;
-					const context = relevant.map(m => `### ${m.name}\n${m.content}`).join('\n\n');
+				if(mems.length) {
-					options.history.push({
+					if(mem.inject) {
-						id: 'auto_recall_' + Math.random().toString(), role: 'tool', name: 'recall', args: {},
+						const pool = 15; // candidates considered, cheap since only refs are listed
-						content: `Knowledge Documents:\n\n${context}`
+						const budget = mem.maxTokens ?? 2000; // actual content injected
-					});
+						const relevant = await this.memoryManager.recollect(message, mem.memory, pool);
+
+						let used = 0;
+						const preloaded: typeof relevant = [];
+						const listed: typeof relevant = [];
+						for(const r of relevant) {
+							const t = this.estimateTokens(r.content);
+							if(used + t <= budget || preloaded.length === 0) {
+								preloaded.push(r);
+								used += t;
+							} else listed.push(r);
+						}
+
+						prompts.unshift(`You have access to the following memory files:
+${mems.map(m => `- ${m.name}: ${m.description}`).join('\n')}
+${preloaded.length ? `
+Relevant memories have been preloaded:
+${preloaded.map(r => `
+**${r.name}**
+${r.description}
+${r.content}
+`).join('\n---\n')}
+` : ''}${listed.length ? `
+Also relevant but not preloaded (use \`memory_recall\`): ${listed.map(r => r.name).join(', ')}
+` : ''}`.trim());
+					}
+					if(mem.tool) tools.push(this.memoryManager.tools.read(mem.memory));
 				}
-				prompts.unshift('You have access to a knowledge base. Relevant documents are injected automatically before each message. Use this knowledge to inform your responses.');
 			}
 
-			const resp = await this.models[m].ask(message, {...options, tools, system: prompts.filter(Boolean).join('\n\n')});
+			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')});
+			let resp = await request;
+
+			// Spice delegated agents response into history
+			let lastDelegateResp: string | null = null;
+			if(pendingDelegates.size) {
+				for(let i = 0; i < history.length; i++) {
+					const h = history[i];
+					if(h.role !== 'tool' || h.content !== '') continue;
+					const queue = pendingDelegates.get(h.name);
+					if(!queue?.length) continue;
+					const {resp: delegateResp, subHistory} = queue.shift()!;
+					const insert: LLMMessage[] = [...subHistory.filter(sh => sh.role === 'tool'), {role: 'assistant', content: delegateResp, timestamp: Date.now()}];
+					history.splice(i + 1, 0, ...insert);
+					lastDelegateResp = delegateResp;
+					i += insert.length;
+				}
+			}
+
+			// If the orchestrator added no commentary of its own, its answer IS the delegate's answer
+			if(typeof resp === 'string' && !resp.trim() && lastDelegateResp !== null) resp = lastDelegateResp;
 
 			// Trim memory injections from history
-			if(options.memory) {
+			if(mem?.tool) history.splice(0, history.length, ...history.filter(h => h.role !== 'tool' || h.name !== 'recall'));
-				options.history.splice(0, options.history.length, ...options.history.filter(h =>
-					h.role !== 'tool' || h.name !== 'recall'));
-			}
 
 			// Auto-memorize before compressing
-			if(options.compress) {
+			if(options.compress && this.estimateTokens(history) >= options.compress.max) {
-				if(options.memory) await this.memoryManager.memorize(options.history, options.memory, options);
+				if(mem?.update) await this.memoryManager.memorize(history, mem.memory, {model: options.memoryModel || this.defaultModel, ...options});
-				const compressed = await this.compressHistory(options.history, options.compress.max, options.compress.min, options);
+				const compressed = await this.compressHistory(history, options.compress.max, options.compress.min, options);
-				options.history.splice(0, options.history.length, ...compressed);
+				if(options.history) options.history.splice(0, options.history.length, ...compressed);
 			}
 
-			return res(resp);
+			return resp;
-		}), {abort});
+		})();
+
+		return Object.assign(promise, {abort});
 	}
 
 	/**
 	 * 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> {
+	async updateMemory(history: LLMMessage[], memories: Memory[] | MemoryCache, options: LLMRequest = {}): Promise<Memory[]> {
-		await this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
+		return this.memoryManager.memorize(history, memories, {model: this.defaultModel, ...options});
 	}
 
 	/**
@@ -380,49 +513,33 @@ class LLM {
 	 * @param {string} searchTerms Multiple search terms to check against target
 	 * @returns {{avg: number, max: number, similarities: number[]}} Similarity values 0-1: 0 = unique, 1 = identical
 	 */
-	fuzzyMatch(target: string, ...searchTerms: string[]) {
+	fuzzyMatch(target, ...searchTerms) {
-		if(searchTerms.length < 2) throw new Error('Requires at least 2 strings to compare');
+		if (searchTerms.length < 2) throw new Error('Requires at least 2 strings to compare');
-		const vector = (text: string, dimensions: number = 10): number[] => {
+		const levenshtein = (a, b) => {
-			return text.toLowerCase().split('').map((char, index) =>
+			const m = a.length, n = b.length;
-				(char.charCodeAt(0) * (index + 1)) % dimensions / dimensions).slice(0, dimensions);
+			if (!m) return n;
-		}
+			if (!n) return m;
-		const v = vector(target);
+			const dp = Array.from({length: m + 1}, (_, i) => [i, ...Array(n).fill(0)]);
-		const similarities = searchTerms.map(t => vector(t)).map(refVector => this.cosineSimilarity(v, refVector));
+			for (let j = 0; j <= n; j++) dp[0][j] = j;
-		return {avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length, max: Math.max(...similarities), similarities};
+			for (let i = 1; i <= m; i++) {
-	}
+				for (let j = 1; j <= n; j++) {
-
+					dp[i][j] = a[i - 1] === b[j - 1]
-	/**
+						? dp[i - 1][j - 1]
-	 * Ask a question with JSON response
+						: 1 + Math.min(dp[i - 1][j - 1], dp[i - 1][j], dp[i][j - 1]);
-	 * @param {string} text Text to process
+				}
-	 * @param {string} schema JSON schema the AI should match
+			}
-	 * @param {LLMRequest} options Configuration options and chat history
+			return dp[m][n];
-	 * @returns {Promise<{} | {} | RegExpExecArray | null>}
+		};
-	 */
+		const similarity = (a, b) => {
-	async json(text: string, schema: string, options?: LLMRequest): Promise<any> {
+			a = a.toLowerCase(); b = b.toLowerCase();
-		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${schema}\n\`\`\`\n\nResponses are ignored`;
+			return 1 - levenshtein(a, b) / Math.max(a.length, b.length, 1);
-		if(options?.system) system += '\n\n' + options.system;
+		};
-		return new Promise(async (resolve, reject) => {
+		const similarities = searchTerms.map(t => similarity(target, t));
-			let done = false;
+		return {
-			const resp = await this.ask(text, {
+			avg: similarities.reduce((acc, s) => acc + s, 0) / similarities.length,
-				temperature: 0.3,
+			max: Math.max(...similarities),
-				...options,
+			similarities
-				system,
+		};
-				tools: [{
-					name: 'submit',
-					description: 'Submit JSON',
-					args: {json: {type: 'string', description: 'Javascript parsable JSON string', required: true}},
-					fn: (args) => {
-						try {
-							const json = JSON.parse(args.json);
-							resolve(json);
-							done = true;
-						} catch { return 'Invalid JSON'; }
-						return 'Saved';
-					}
-				}, ...(options?.tools || [])],
-			});
-			if(!done) reject(`AI failed to create JSON:\n${resp}`);
-		});
 	}
 
 	/**
@@ -458,6 +575,29 @@ 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;

src/memory.ts

@@ -1,177 +1,624 @@
-// memory.ts
 import {LLMRequest, LLMMessage} from './llm.ts';
+import {AiTool} from './tools.ts';
+import {KDPoint, KDTree} from './kd-tree.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();
+}
+
+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();
+	}
+}
+
+export type MemoryOptions = {
+	/** Memory object */
+	memory: Memory[] | MemoryCache;
+	/** Inject N memories into the system prompt */
+	inject?: boolean;
+	/** expose recall tool to LLM */
+	tool?: boolean;
+	/** Update memory on compression */
+	update?: boolean;
+	/** Max context size of memories to inject to each call (removed immediately after use) */
+	maxTokens?: number;
+}
 
-/** 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 = {
+export type MemoryRef = {
-	/** Memory subject */
 	name: string;
-	/** Short description - required if isNew */
+	description: string;
-	description?: string;
+}
-	/** Extracted facts to merge */
+
+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 = {
-		edit: (memory: Memory) => ({
+		read: (memories: Memory[] | MemoryCache): AiTool => ({
-			name: 'edit',
+			name: 'memory_recall',
-			description: 'Edit a memory. Omit start/end to append. Pass start only to replace from that line on. Pass start+end to replace a specific range. start=0 replaces the whole document.',
+			description: 'Read the full content of a memory 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 `Updated memory:\n${memory.content}`;
-			}
-		}),
-		extract: (pools: MemoryCollection[]) => ({
-			name: 'extract',
-			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, only required if new'},
-				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[]) => ({
-			name: 'read',
-			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);
+				const mems = memories instanceof MemoryCache ? memories.memories : memories;
-				if(!mem) return 'Document not found';
+				const mem = mems.find(m => m.name === args.name);
-				return `Name: ${mem.name}\nDescription: ${mem.description}\n\n${mem.content}`;
+				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) {}
+
+	static normalize(m?: Memory[] | MemoryCache | MemoryOptions) {
+		if(!m) return null;
+		const raw = m instanceof MemoryCache || Array.isArray(m);
+		return raw ? {memory: <Memory[] | MemoryCache>m, inject: true, tool: true, update: true} : {inject: true, tool: true, update: true, ...m};
 	}
 
-	constructor(private llm: any, private model?: string) {}
+	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)
-	 * 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, call NO tools
 
-For each fact decide whether it belongs in an existing document or needs a new one, then call the \`extract\` tool.
+${conversation}`;
-
+		const [e] = await this.llm.embedding(content);
-Existing documents:\n${existingDocs || 'None yet.'}`,
+		return {
-			tools: [this.tools.extract(pools)]
+			name: `_temp_${timestamp}`,
-		});
+			description: 'Temporary memory - processing in background',
-		return pools;
+			content,
+			embedding: e?.embedding || [],
+		};
 	}
 
-	/**
+	forget(name: string, memories: Memory[] | MemoryCache): boolean {
-	 * Bot 2 - Editor: merges a pool of facts into a specific document using surgical line-based edits.
+		const mem = memories instanceof MemoryCache ? memories.memories : memories;
-	 * Receives full document content and uses read + amend tools to make precise edits.
+		const idx = mem.findIndex(m => m.name === name);
-	 * @param {MemoryCollection} newMem The fact pool to merge
+		if (idx === -1) return false;
-	 * @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'),
+		for (const node of mem) {
-			{
+			const {links, backlinks} = extractMetadata(node.content);
-				model: this.model || options.model,
+			const newBacklinks = backlinks.filter(b => b !== name);
-				temperature: 0.2,
+			const newLinks = links.filter(l => l !== name);
-				system: `You are a document editor. Merge the users list of facts into the following document using the \`edit\` tool; call it as many times as necessary.
 
-Name: ${mem.name}
+			if (newBacklinks.length !== backlinks.length || newLinks.length !== links.length) {
-Description: ${mem.description}
+				node.content = this.updateFrontmatter(node.content, {
-${mem.content}`,
+					links: newLinks,
-				tools: [this.tools.edit(mem)]
+					backlinks: newBacklinks,
+				});
 			}
-		);
-
-		if(isNew || mem.description !== existing?.description) {
-			const [e] = await this.llm.embedding(mem.description);
-			mem.embedding = e.embedding;
 		}
 
-		if(isNew) memories.push(mem);
+		mem.splice(idx, 1);
-		else {
+
-			const idx = memories.findIndex(m => m.name === newMem.name);
+		if (memories instanceof MemoryCache) memories.rebuild();
-			if(idx >= 0) memories[idx] = mem;
+		return true;
-		}
 	}
 
-	/**
+	private cosineSearch(query: number[], memories: Memory[], limit: number): MemoryRef[] {
-	 * Find relevant memory documents for a query using description embeddings
+		const scored = memories
-	 * @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)}))
+			.map(m => ({
-			.toSorted((a: any, b: any) => b.score - a.score)
+				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[] {
-	 * Two-stage memory pipeline: classify facts from conversation history then surgically merge them into documents.
+		return memories.map(m => ({name: m.name, description: m.description}));
-	 * 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
+	async recollect(query: string, memories: Memory[] | MemoryCache, limit = 5, graphDepth = 1): Promise<Memory[]> {
-	 * @param {LLMRequest} options LLM options
+		const mem: Memory[] = memories instanceof MemoryCache ? memories.memories : memories;
-	 */
+		if (!mem.length) return [];
-	async memorize(history: LLMMessage[], memories: Memory[], options: LLMRequest): Promise<void> {
+
+		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}]: ${h.content}`)
+			.map(h => `[${h.role}]: ${h.content}`).join('\n\n').trim();
-			.join('\n\n');
+		if(!conversation) return [];
-		if(!conversation.trim()) return;
+
-		const pools = await this.extract(conversation, memories, options);
+		const trackingId = `${Date.now()}_${Math.random()}`;
-		if(!pools.length) return;
+		const tempMemory = await this.createTempMemory(conversation);
-		await Promise.all(pools.map(pool => this.edit(pool, memories, options)));
+		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 = node.name !== 'Person/User' ? update.description : 'All information about the current user';
+		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 "People/User"
+- 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}));
 	}
 }

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)
 		}));
 	}
 
@@ -28,11 +29,11 @@ export class OpenAi extends LLMProvider {
 				}));
 				history.splice(i, 1, ...tools);
 				i += tools.length - 1;
-			} else if(h.role === 'tool' && h.content) {
+			} else if(h.role === 'tool') {
 				const record = history.find(h2 => h.tool_call_id == h2.id);
 				if(record) {
-					if(h.content.includes('"error":')) record.error = h.content;
+					if(h.content?.includes('"error":')) record.error = h.content;
-					else record.content = h.content;
+					else record.content = h.content || '';
 				}
 				history.splice(i, 1);
 				i--;
@@ -50,21 +51,22 @@ export class OpenAi extends LLMProvider {
 					content: null,
 					tool_calls: [{ id: h.id, type: 'function', function: { name: h.name, arguments: JSON.stringify(h.args) } }],
 					refusal: null,
-					annotations: []
+					annotations: [],
+					timestamp: h.timestamp,
 				}, {
 					role: 'tool',
 					tool_call_id: h.id,
-					content: h.error || h.content
+					content: h.error || h.content,
+					timestamp: h.timestamp,
 				});
 			} else {
-				const {timestamp, ...rest} = h;
+				result.push(h);
-				result.push(rest);
 			}
 			return result;
 		}, [] 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 +79,8 @@ 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,
+				max_completion_tokens: options.max_tokens || this.ai.options.llm?.max_tokens || undefined,
-				temperature: options.temperature || this.ai.options.llm?.temperature || 0.7,
+				temperature: options.temperature || this.ai.options.llm?.temperature || undefined,
 				tools: tools.map(t => ({
 					type: 'function',
 					function: {
@@ -93,8 +95,21 @@ export class OpenAi extends LLMProvider {
 				}))
 			};
 
-			let resp: any, isFirstMessage = true;
+			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, terminal = false;
 			do {
+				requestParams.messages = history.map(({timestamp, ...m}) => m);
 				resp = await this.client.chat.completions.create(requestParams).catch(err => {
 					err.message += `\n\nMessages:\n${JSON.stringify(history, null, 2)}`;
 					throw err;
@@ -103,7 +118,7 @@ export class OpenAi extends LLMProvider {
 				if(options.stream) {
 					if(!isFirstMessage) options.stream({text: '\n\n'});
 					else isFirstMessage = false;
-					resp.choices = [{message: {role: 'assistant', content: '', tool_calls: []}}];
+					resp.choices = [{message: {role: 'assistant', content: '', tool_calls: [], timestamp: Date.now()}}];
 					for await (const chunk of resp) {
 						if(controller.signal.aborted) break;
 						if(chunk.choices[0].delta.content) {
@@ -145,25 +160,34 @@ export class OpenAi extends LLMProvider {
 					const results = await Promise.all(toolCalls.map(async (toolCall: any) => {
 						const tool = tools?.find(findByProp('name', toolCall.function.name));
 						if(options.stream) options.stream({tool: toolCall.function.name});
-						if(!tool) return {role: 'tool', tool_call_id: toolCall.id, content: '{"error": "Tool not found"}'};
+						if(!tool) return {role: 'tool', tool_call_id: toolCall.id, content: '{"error": "Tool not found"}', timestamp: Date.now()};
 						try {
 							const args = JSONAttemptParse(toolCall.function.arguments, {});
-							const result = await tool.fn(args, options.stream, this.ai);
+							// Wrap stream so a tool's `done` ends turn gracefully
-							return {role: 'tool', tool_call_id: toolCall.id, content: typeof result == 'object' ? JSONSanitize(result) : result};
+							const toolStream = options.stream && ((chunk: any) => {
+								if(chunk.done) { terminal = true; return; }
+								options.stream!(chunk);
+							});
+							const result = await tool.fn(args, toolStream, this.ai);
+							return {role: 'tool', tool_call_id: toolCall.id, content: typeof result == 'object' ? JSONSanitize(result) : result, timestamp: Date.now()};
 						} catch (err: any) {
-							return {role: 'tool', tool_call_id: toolCall.id, content: JSONSanitize({error: err?.message || err?.toString() || 'Unknown'})};
+							return {role: 'tool', tool_call_id: toolCall.id, content: JSONSanitize({error: err?.message || err?.toString() || 'Unknown'}), timestamp: Date.now()};
 						}
 					}));
 					history.push(...results);
 					requestParams.messages = history;
 				}
-			} while (!controller.signal.aborted && resp.choices?.[0]?.message?.tool_calls?.length);
+			} while (!terminal && !controller.signal.aborted && resp.choices?.[0]?.message?.tool_calls?.length);
-			history.push({role: 'assistant', content: resp.choices[0].message.content.trim() || ''});
-			history = this.toStandard(history);
 
+			if(!terminal) {
+				const textContent = resp.choices[0].message.content?.trim() || '';
+				history.push({role: 'assistant', content: textContent, timestamp: Date.now()});
+			}
+			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);
+			const finalContent = history.at(-1)?.content;
+			res(options.schema ? JSONAttemptParse(finalContent, finalContent) : finalContent);
 		}), {abort: () => controller.abort()});
 	}
 }

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>;

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 = {
+export const ExecJSTool: AiTool = {
-	name: 'get_datetime',
+	name: 'exec_javascript',
-	description: 'Get local date / time',
+	description: 'Execute commonjs javascript',
-	args: {},
+	args: {
-	fn: async () => new Date().toString()
+		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 = {
+export const ExecPythonTool: AiTool = {
-	name: 'get_datetime_utc',
+	name: 'exec_python',
-	description: 'Get current UTC date / time',
+	description: 'Execute commonjs javascript',
-	args: {},
+	args: {
-	fn: async () => new Date().toUTCString()
+		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 +145,483 @@ export const ExecTool: AiTool = {
 	}
 }
 
-export const FetchTool: AiTool = {
+export const FsDeleteTool = (whitelist: null | string[] = null): AiTool => {
-	name: 'fetch',
+	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()} ${os.release()}`
+		};
+	}
+}
+
+export const GetWikipediaTool: AiTool = {
+	name: 'get_wikipedia',
+	description: 'Search Wikipedia for matching articles',
+	args: {
+		query: {type: 'string', description: 'Search term or article title', required: true},
+		mode: {type: 'string', description: 'search - look for articles, summary - intro of first found article (default), full - complete first found article', enum: ['search', 'summary', 'full'], default: 'summary'},
+		ua: {type: 'string', description: 'User Agent'},
+	},
+	fn: async ({query, mode, ua}) => {
+		class WikipediaClient {
+			useragent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)';
+
+			constructor(useragent: string) {
+				this.useragent = useragent;
+			}
+
+			async get(url) {
+				const resp = await fetch(url, {headers: {'User-Agent': this.useragent}});
+				return resp.json();
+			}
+
+			api(params) {
+				const qs = new URLSearchParams({...params, format: 'json', utf8: '1'}).toString();
+				return this.get(`https://en.wikipedia.org/w/api.php?${qs}`);
+			}
+
+			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);
+				}
+
+				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();
+			}
+
+			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 || [];
+			}
+
+			async fetchExtract(title: string, introOnly = false) {
+				const params: any = {action: 'query', prop: 'extracts', titles: title, explaintext: 1, redirects: 1};
+				if(introOnly) params.exintro = 1;
+				const data = await this.api(params);
+				const page: any = Object.values(data.query?.pages || {})[0];
+				return this.clean(page?.extract || '');
+			}
+
+			pageUrl(title: string) {
+				return `https://en.wikipedia.org/wiki/${encodeURIComponent(title.replace(/ /g, '_'))}`;
+			}
+
+			stripHtml(text: string) {
+				return text.replace(/<[^>]+>/g, '');
+			}
+
+			async lookup(query: string, detail = 'summary') {
+				const results = await this.searchTitles(query, 6);
+				if(!results.length) return `❌ No Wikipedia articles found for "${query}"`;
+				const title = results[0].title;
+				const url = this.pageUrl(title);
+				const introOnly = detail !== 'full';
+				const content = await this.fetchExtract(title, introOnly);
+				return `## ${title}\n🔗 ${url}\n\n${content}`;
+			}
+
+			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.stripHtml(r.snippet || '').trim();
+					lines.push(`**${i + 1}. ${r.title}**\n${snippet}\n${this.pageUrl(r.title)}`);
+				}
+				return lines.join('\n\n');
+			}
+		}
+
+		const wiki = new WikipediaClient(ua);
+		if(mode === 'search') return wiki.search(query);
+		return wiki.lookup(query, mode || 'summary');
+	}
+};
+
+export const GeoCodeTool: AiTool = {
+	name: 'geo_code',
+	description: 'Converts coordinates to address OR vice versa',
+	args: {
+		query: {type: 'string', description: 'Search query - coordinates (lat,lon) or address string', required: true},
+	},
+	fn: async ({query}) => {
+		const coordinates = /(-?\d+(?:\.\d+)?).*?,.*?(-?\d+(?:\.\d+)?)/.exec(query);
+		if(coordinates) { // Geolocate
+			const url = `https://nominatim.openstreetmap.org/reverse?format=json&lat=${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 WebFetchTool: AiTool = {
+	name: 'web_fetch',
 	description: 'Make HTTP request to URL',
 	args: {
 		url: {type: 'string', description: 'URL to fetch', required: true},
@@ -107,30 +637,59 @@ export const FetchTool: AiTool = {
 	}) => new Http({url: args.url, headers: args.headers}).request({method: args.method || 'GET', body: args.body})
 }
 
-export const JSTool: AiTool = {
+export const WebFlareSolverTool = (host: string) => {
-	name: 'exec_javascript',
+	return {
-	description: 'Execute commonjs javascript',
+		name: 'web_flaresolverr',
-	args: {
+		description: 'Use a flaresolverr proxy to bypass cloudflare bot detection',
-		code: {type: 'string', description: 'CommonJS javascript', required: true}
+		args: {
-	},
+			url: {type: 'string', description: 'URL to fetch', required: true},
-	fn: async (args: {code: string}) => {
+			cmd: {type: 'string', description: 'Flaresolverr cmd', enum: ['request.get', 'request.post'], default: 'request.get'},
-		const c = consoleInterceptor(null);
+			maxTimeout: {type: 'number', description: 'Fetch time limit', default: 60_000},
-		const resp = await Fn<any>({console: c}, args.code, true).catch((err: any) => c.output.error.push(err));
+			postData: {type: 'object', description: 'Data to send during request.post requests'},
-		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)}`);
+					}
+				}
+
+				return pairs.join('&');
+			}
+
+			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 PythonTool: AiTool = {
+export const WebReadTool: AiTool = {
-	name: 'exec_javascript',
+	name: 'web_read',
-	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 ReadWebpageTool: AiTool = {
-	name: 'read_webpage',
 	description: 'Extract clean content from webpages, or convert media/documents to accessible formats',
 	args: {
 		url: {type: 'string', description: 'URL to read', required: true},
@@ -258,94 +817,3 @@ export const WebSearchTool: AiTool = {
 		return results;
 	}
 }
-
-class WikipediaClient {
-	private async get(url: string): Promise<any> {
-		const resp = await fetch(url, {headers: {'User-Agent': UA}});
-		return resp.json();
-	}
-
-	private api(params: Record<string, any>): Promise<any> {
-		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();
-	}
-
-	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;
-	}
-
-	private async searchTitles(query: string, limit = 6): Promise<any[]> {
-		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> {
-		const params: any = {action: 'query', prop: 'extracts', titles: title, explaintext: 1, redirects: 1};
-		if(intro) params.exintro = 1;
-		const data = await this.api(params);
-		const page = Object.values(data.query?.pages || {})[0] as any;
-		return this.clean(page?.extract || '');
-	}
-
-	private pageUrl(title: string): string {
-		return `https://en.wikipedia.org/wiki/${encodeURIComponent(title.replace(/ /g, '_'))}`;
-	}
-
-	private stripHtml(text: string): string {
-		return text.replace(/<[^>]+>/g, '');
-	}
-
-	async lookup(query: string, detail: 'intro' | 'full' = 'intro'): Promise<string> {
-		const results = await this.searchTitles(query, 6);
-		if(!results.length) return `❌ No Wikipedia articles found for "${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}`;
-	}
-
-	async search(query: string): Promise<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);
-			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);
-	}
-};

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);
+			return await new Promise<string | null>((res, rej) => {
-			await worker.terminate();
+				reject = rej;
-			res(data.text.trim() || null);
+				worker.recognize(path)
-		}).finally(() => worker?.terminate());
+					.then(({data}: any) => res(data.text.trim() || null))
+					.catch(rej);
+			});
+		})().finally(() => {
+			process.off('uncaughtException', handler);
+			worker?.terminate();
+		});
+
 		return Object.assign(p, {abort: () => worker?.terminate()});
 	}
 }

tsconfig.json

@@ -4,7 +4,10 @@
 		"target": "ESNext",
 		"useDefineForClassFields": true,
 		"module": "ESNext",
-		"lib": ["ESNext"],
+		"lib": [
+			"ESNext",
+			"dom"
+		],
 		"skipLibCheck": true,
 
 		/* Bundler mode */