initial commit: frontend and backend; /, /chat, /chats/:agent_name routes
This commit is contained in:
Avraham Sakal
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import { jsonSchema, tool } from "ai";
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import { Agent } from "../types.js";
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import { singleSpace } from "../util.js";
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export const personalAssistantAgent: Agent = {
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id: "personal-assistant",
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name: "Personal Assistant",
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// modelName: "mistral/ministral-8b",
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modelName: "google/gemini-2.5-flash-preview",
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systemMessage:
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singleSpace(`You are a personal assistant Agent who is helpful, friendly, and
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responsible. You are my liason to other Agents who have specialized
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abilities and skills. I will inform you of the existence of these Agents by
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giving you each one's "Agent Card" in a structured format. You will delegate
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tasks to the Agent(s) that you deem most suitable, based on their skills and abilities. You
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will provide those Agents with the necessary information and context
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to complete the task effectively and efficiently; you will not include
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irrelevant details, and you will include relevant details and context.
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You may delegate to multiple Agents if the task or request can be split
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into subtasks each requiring distinct skills/abilities. If multiple Agents
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are equally-qualified to handle the task, you will ask me which one to delegate
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to, because you are responsible and do not want to choose anything but the most
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relevant Agent for a given task. Similarly, if you think there is no
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sufficiently-capable Agent to handle a given task, you will tell me so and exit
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the conversation so I can go create one.`),
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description:
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"A personal assistant who is helpful, friendly, and knowledgeable.",
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skills: [
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{
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id: "help",
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name: "Help",
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description: "Provides helpful information.",
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tags: ["help", "information"],
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examples: [
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"What is the weather like today?",
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"How do I cook a lasagna?",
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"Can you tell me about the history of the United States?",
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],
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},
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],
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tools: {
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delegate: tool({
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description:
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"Delegate the task to another Agent that you deem most suitable.",
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parameters: jsonSchema<{ agentId: string; prompt: string }>({
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type: "object",
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properties: {
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agentId: {
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type: "string",
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description: "The ID of the Agent to delegate the task to.",
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},
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prompt: {
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type: "string",
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description:
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"The prompt to use for the delegated task which includes all necessary information and context for the Agent to be able to complete the task.",
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},
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},
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}),
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// execute: async ({
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// agentId,
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// prompt,
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// }: {
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// agentId: string;
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// prompt: string;
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// }) => {
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// const agent = agents.find((agent) => agent.id === agentId);
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// if (!agent) {
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// throw new Error(`No such agent: ${agentId}`);
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// }
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// const response = conversation.send({
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// role: "user",
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// content: prompt,
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// });
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// await response.consumeStream();
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// for (const message of (await response.response).messages) {
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// conversation.messages.push(message);
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// }
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// },
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}),
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say: tool({
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description: "Say something.",
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parameters: jsonSchema<{ message: string }>({
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type: "object",
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properties: {
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message: {
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type: "string",
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description: "The message to say.",
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},
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},
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}),
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execute: async ({ message }: { message: string }) => {
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console.log(message);
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},
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}),
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exit: tool({
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description: "Exits the conversation.",
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parameters: jsonSchema<{ exitCode: number }>({
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type: "object",
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properties: {
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exitCode: {
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type: "number",
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description: "The exit code to use.",
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default: 0,
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},
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},
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}),
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}),
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get_random_number: tool({
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description: "Get a random number between `min` and `max`.",
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parameters: jsonSchema<{ min: number; max: number }>({
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type: "object",
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properties: {
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min: {
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type: "number",
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description: "The minimum value.",
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default: 0,
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},
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max: {
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type: "number",
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description: "The maximum value.",
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default: 1,
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},
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},
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}),
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execute: async ({ min, max }: { min: number; max: number }) => {
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return Math.random() * (max - min) + min;
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},
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}),
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},
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};
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@@ -0,0 +1,37 @@
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import { Agent } from "../types.js";
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export const chefAgent: Agent = {
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id: "chef",
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name: "Chef",
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modelName: "mistral/ministral-8b",
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systemMessage:
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"You are a master chef who is famous for his creative and original recipes, but also knows many standard, tried-and-true recipes.",
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description:
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"A master chef who is famous for his creative and original recipes, but also knows many standard, tried-and-true recipes.",
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skills: [
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{
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id: "invent_recipe",
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name: "Invent Recipe",
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description:
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"Creates an original recipe, as opposed to a conventional one.",
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tags: ["cooking", "recipe", "invention"],
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examples: [
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"Create a vegetarian lasagna recipe for 4 people.",
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"Invent a recipe for a chicken parmesan.",
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"Create a recipe for a vegan lasagna.",
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],
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},
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{
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id: "recall_recipe",
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name: "Recall Recipe",
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description:
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"Recalls a conventional recipe, as opposed to an original one.",
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tags: ["cooking", "recipe", "recall"],
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examples: [
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"Create a vegetarian lasagna recipe for 4 people.",
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"Invent a recipe for a chicken parmesan.",
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"Create a recipe for a vegan lasagna.",
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],
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},
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],
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};
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@@ -0,0 +1,40 @@
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import { Tool } from "ai";
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/** An Agent's Skill does not imply any specific tool; tool-use is strictly
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* internal to the Agent and considered an implementation detail. From the
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* perspective of other Agents, a Skill is simply an assertion that a
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* sufficiently-relevant task delegated to this agent will more likely be
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* accomplished successfully than with another, less-relevant Agent/Skill.
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*
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* Note that the granularity of task delegation is to an Agent, not to a
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* specific skill. We use this `Skill` interface as a compact, standardized way
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* of expressing task relevancy to other Agents; instead of relying solely on
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* the natural-language `description`.
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* */
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export interface Skill {
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/** Machine-friendly name. */
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id: string;
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/** Human-freindly name. */
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name: string;
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/** The description of the skill. */
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description: string;
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tags?: string[];
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examples?: string[];
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}
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export interface Agent {
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/** Machine-friendly name. */
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id: string;
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/** Human-freindly name. */
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name: string;
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modelName: string;
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/** The system prompt. Not exposed to other models. Therefore, it's safe to
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* include instructions like "DO" and "DO NOT", etc. */
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systemMessage: string;
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/** Exposed to other Agents, to help tham decide whether to delegate a given
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* task to this Agent. */
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description: string;
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/** See JSDoc on the `Skill` interface. */
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skills?: Skill[];
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tools?: Record<string, Tool>;
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}
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@@ -0,0 +1,3 @@
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export function singleSpace(str: string) {
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return str.replace(/\s+/g, " ");
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}
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@@ -0,0 +1,77 @@
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import { createOpenRouter } from "@openrouter/ai-sdk-provider";
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import { streamText } from "ai";
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import { Hono } from "hono";
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import { stream } from "hono/streaming";
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import { personalAssistantAgent } from "./agents/assistant.js";
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import { chefAgent } from "./agents/chef.js";
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import { Agent } from "./types.js";
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// This declaration is primarily for providing type hints in your code
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// and it doesn't directly define the *values* of the environment variables.
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interface Env {
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OPENROUTER_API_KEY: string;
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// Add other environment variables here
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}
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declare global {
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const env: Env;
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}
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const app = new Hono();
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const openrouter = createOpenRouter({
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apiKey: import.meta.env.VITE_OPENROUTER_API_KEY || env.OPENROUTER_API_KEY,
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});
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const systemMessage = {
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role: "system",
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content:
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"You are a wise old man named Dorf that answers questions succintly.",
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};
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app.post("/api/chat", async (c) => {
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const input = await c.req.json();
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console.log(input);
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const result = streamText({
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model: openrouter("mistral/ministral-8b"),
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messages: [systemMessage, ...input.messages],
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tools: {},
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});
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// Mark the response as a v1 data stream:
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c.header("X-Vercel-AI-Data-Stream", "v1");
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c.header("Content-Type", "text/plain; charset=utf-8");
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return stream(c, (stream) => stream.pipe(result.toDataStream()));
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});
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const agentsByName: Record<string, Agent> = {
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assistant: personalAssistantAgent,
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chef: chefAgent,
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};
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app.post("/api/chat/:agent_name", async (c) => {
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const input = await c.req.json();
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const agentName = c.req.param("agent_name");
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const agent = agentsByName[agentName];
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if (!agent) {
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return c.json({ error: `No such agent: ${agentName}` });
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}
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console.log(input);
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const result = streamText({
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model: openrouter(agent.modelName),
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messages: [
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{ role: "system", content: agent.systemMessage },
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...input.messages,
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],
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tools: agent.tools,
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});
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// Mark the response as a v1 data stream:
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c.header("X-Vercel-AI-Data-Stream", "v1");
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c.header("Content-Type", "text/plain; charset=utf-8");
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return stream(c, (stream) => stream.pipe(result.toDataStream()));
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});
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export default app;
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