Team Examples

import { Agent, Team, TeamMode, openai, defineTool } from "@agentium/core";
import { z } from "zod";

const writer = new Agent({
  name: "Writer",
  model: openai("gpt-4o"),
  instructions: `You are a professional long-form writer.
    Given research notes and a topic, produce clear, engaging prose.
    Target 300–500 words. Use subheadings and concrete data points.`,
  register: false,
});

const editor = new Agent({
  name: "Editor",
  model: openai("gpt-4o"),
  instructions: `You are a meticulous editor.
    Fix grammar, tighten structure, and improve clarity.
    Preserve the author's voice. Return the edited piece in full.`,
  register: false,
});

const factChecker = new Agent({
  name: "FactChecker",
  model: openai("gpt-4o"),
  instructions: `You verify factual claims against known data.
    Flag anything unsubstantiated with [NEEDS CITATION].
    Suggest corrections with sources where possible.`,
  register: false,
});

const researchTeam = new Team({
  name: "ResearchTeam",
  mode: TeamMode.Coordinate,
  model: openai("gpt-4o"),
  members: [writer, editor, factChecker],
  instructions: `Workflow: first ask the Writer for a draft,
    then the FactChecker to verify all claims,
    then the Editor for a final polish incorporating any corrections.`,
});

const result = await researchTeam.run(
  "Write a 300-word article on the economic impact of quantum computing in 2026."
);
console.log(result.text);

import { Agent, Team, TeamMode, openai } from "@agentium/core";

const billingAgent = new Agent({
  name: "BillingAgent",
  model: openai("gpt-4o"),
  instructions: `You handle billing inquiries: invoices, payment methods,
    refunds, subscription changes, and pricing questions.
    Always confirm the customer's account ID before making changes.`,
  register: false,
});

const technicalAgent = new Agent({
  name: "TechnicalAgent",
  model: openai("gpt-4o"),
  instructions: `You troubleshoot technical issues: API errors, integration
    problems, SDK bugs, and performance debugging.
    Ask for error codes and stack traces when relevant.`,
  register: false,
});

const generalAgent = new Agent({
  name: "GeneralAgent",
  model: openai("gpt-4o"),
  instructions: `You answer general questions about the product, onboarding
    flow, feature availability, and company policies.
    Redirect to billing or technical if the query is clearly in their domain.`,
  register: false,
});

const supportRouter = new Team({
  name: "CustomerSupport",
  mode: TeamMode.Route,
  model: openai("gpt-4o-mini"),
  members: [billingAgent, technicalAgent, generalAgent],
  instructions: `Route each message to exactly one agent.
    Billing: invoices, charges, refunds, subscriptions.
    Technical: errors, integrations, API, performance.
    General: everything else.`,
});

const billing = await supportRouter.run(
  "I was double-charged $49.99 on my last invoice (account: ACCT-8821)."
);
console.log("Billing response:", billing.text);

const technical = await supportRouter.run(
  "My webhook endpoint returns 502 errors every time I push to /events."
);
console.log("Technical response:", technical.text);

import { Agent, Team, TeamMode, openai } from "@agentium/core";

const optimist = new Agent({
  name: "Optimist",
  model: openai("gpt-4o"),
  instructions: `Analyze topics from an optimistic perspective.
    Highlight opportunities, growth potential, and competitive advantages.
    Back claims with market data or precedent where possible.`,
  register: false,
});

const skeptic = new Agent({
  name: "Skeptic",
  model: openai("gpt-4o"),
  instructions: `Analyze topics from a critical perspective.
    Surface risks, hidden costs, failure modes, and competitive threats.
    Quantify downside scenarios when feasible.`,
  register: false,
});

const pragmatist = new Agent({
  name: "Pragmatist",
  model: openai("gpt-4o"),
  instructions: `Analyze topics from a practical perspective.
    Focus on feasibility, timelines, resource needs, and concrete next steps.
    Suggest a phased rollout plan where appropriate.`,
  register: false,
});

const analysisTeam = new Team({
  name: "MultiPerspectiveAnalysis",
  mode: TeamMode.Broadcast,
  model: openai("gpt-4o"),
  members: [optimist, skeptic, pragmatist],
  instructions: `Synthesize all three perspectives into a balanced executive
    summary. Structure it as: Key Opportunities, Key Risks, Recommended
    Action Plan. End with a clear go/no-go recommendation.`,
});

const result = await analysisTeam.run(
  "Should our 50-person startup pivot from B2C to B2B SaaS given declining consumer retention?"
);
console.log(result.text);

import { Agent, Team, TeamMode, openai } from "@agentium/core";

const ideator = new Agent({
  name: "Ideator",
  model: openai("gpt-4o"),
  instructions: `You generate creative product ideas.
    Build on feedback from previous rounds to refine proposals.
    Each round, present your top 2 ideas with one-sentence rationale.`,
  register: false,
});

const critic = new Agent({
  name: "Critic",
  model: openai("gpt-4o"),
  instructions: `You critically evaluate ideas.
    Identify the single biggest weakness of each proposal.
    Suggest one specific, actionable improvement per idea.`,
  register: false,
});

const strategist = new Agent({
  name: "Strategist",
  model: openai("gpt-4o"),
  instructions: `You assess market fit and business viability.
    Score each idea on market size (1-5), feasibility (1-5), and
    differentiation (1-5). Suggest positioning and GTM strategy.`,
  register: false,
});

const brainstormTeam = new Team({
  name: "BrainstormTeam",
  mode: TeamMode.Collaborate,
  model: openai("gpt-4o"),
  members: [ideator, critic, strategist],
  maxRounds: 4,
  instructions: `Facilitate a brainstorm to converge on one winning idea.
    Stop early if all members agree on a single top idea.`,
});

const result = await brainstormTeam.run(
  "We need a new feature for our developer productivity tool that reduces context-switching. Our users are full-stack engineers at mid-size companies."
);
console.log(result.text);

import { Agent, Team, TeamMode, openai, defineTool } from "@agentium/core";
import { z } from "zod";

const searchKnowledgeBase = defineTool({
  name: "search_kb",
  description: "Search the internal knowledge base for relevant articles",
  parameters: z.object({
    query: z.string().describe("Search query"),
    limit: z.number().optional().default(5),
  }),
  execute: async ({ query, limit }) => {
    const res = await fetch(
      `https://kb.internal/api/search?q=${encodeURIComponent(query)}&limit=${limit}`
    );
    return JSON.stringify(await res.json());
  },
});

const createTicket = defineTool({
  name: "create_ticket",
  description: "Create a support ticket in the ticketing system",
  parameters: z.object({
    title: z.string(),
    description: z.string(),
    priority: z.enum(["low", "medium", "high", "critical"]),
    assignee: z.string().optional(),
  }),
  execute: async (args) => {
    const ticket = await fetch("https://tickets.internal/api/tickets", {
      method: "POST",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify(args),
    }).then((r) => r.json());
    return `Ticket created: ${ticket.id} (${ticket.url})`;
  },
});

const updateStatus = defineTool({
  name: "update_ticket_status",
  description: "Update the status of an existing support ticket",
  parameters: z.object({
    ticketId: z.string(),
    status: z.enum(["open", "in_progress", "waiting", "resolved", "closed"]),
    note: z.string().optional(),
  }),
  execute: async ({ ticketId, status, note }) => {
    await fetch(`https://tickets.internal/api/tickets/${ticketId}/status`, {
      method: "PATCH",
      headers: { "Content-Type": "application/json" },
      body: JSON.stringify({ status, note }),
    });
    return `Ticket ${ticketId} updated to ${status}`;
  },
});

const sharedTools = [searchKnowledgeBase, createTicket, updateStatus];

const triageAgent = new Agent({
  name: "TriageAgent",
  model: openai("gpt-4o"),
  instructions: `Classify incoming issues by severity and affected system.
    Search the knowledge base to check for known issues before escalating.`,
  tools: sharedTools,
  register: false,
});

const resolutionAgent = new Agent({
  name: "ResolutionAgent",
  model: openai("gpt-4o"),
  instructions: `Search the knowledge base for solutions to the triaged issue.
    If a fix exists, provide step-by-step instructions.
    If no fix is found, create a ticket for the engineering team with priority based on triage severity.`,
  tools: sharedTools,
  register: false,
});

const supportTeam = new Team({
  name: "SupportOps",
  mode: TeamMode.Coordinate,
  model: openai("gpt-4o-mini"),
  members: [triageAgent, resolutionAgent],
  instructions: "Triage the issue first, then attempt resolution using the knowledge base.",
});

const result = await supportTeam.run(
  "User reports SSO login fails with error AUTH-403 after password reset. Affecting 12 enterprise accounts since 9am PST."
);
console.log(result.text);

import { Agent, Team, TeamMode, openai } from "@agentium/core";

// --- Engineering sub-team ---
const frontendDev = new Agent({
  name: "FrontendDev",
  model: openai("gpt-4o"),
  instructions: `You plan frontend implementation: React components, state
    management (Zustand/Redux), API integration, and responsive design.
    Estimate complexity in story points.`,
  register: false,
});

const backendDev = new Agent({
  name: "BackendDev",
  model: openai("gpt-4o"),
  instructions: `You plan backend implementation: REST/GraphQL APIs, database
    schema migrations, service architecture, and caching strategy.
    Estimate complexity in story points.`,
  register: false,
});

const qaEngineer = new Agent({
  name: "QAEngineer",
  model: openai("gpt-4o"),
  instructions: `You define the test strategy: unit tests, integration tests,
    E2E scenarios, and edge cases. Identify regression risks.`,
  register: false,
});

const engineeringTeam = new Team({
  name: "Engineering",
  mode: TeamMode.Coordinate,
  model: openai("gpt-4o-mini"),
  members: [frontendDev, backendDev, qaEngineer],
  instructions: "Produce a technical implementation plan with effort estimates.",
  register: false,
});

// --- Design sub-team ---
const uxDesigner = new Agent({
  name: "UXDesigner",
  model: openai("gpt-4o"),
  instructions: `Create user flows, wireframe descriptions, and interaction
    patterns. Prioritize accessibility (WCAG 2.1 AA).`,
  register: false,
});

const visualDesigner = new Agent({
  name: "VisualDesigner",
  model: openai("gpt-4o"),
  instructions: `Define visual style: color palette, typography scale,
    spacing system, and component variants. Follow design tokens.`,
  register: false,
});

const designTeam = new Team({
  name: "Design",
  mode: TeamMode.Collaborate,
  model: openai("gpt-4o-mini"),
  members: [uxDesigner, visualDesigner],
  maxRounds: 2,
  register: false,
});

// --- Product team coordinates everything ---
const productManager = new Agent({
  name: "ProductManager",
  model: openai("gpt-4o"),
  instructions: `Write PRDs, define user stories with acceptance criteria,
    and prioritize the backlog. You coordinate engineering and design teams.`,
  register: false,
});

const productOrg = new Team({
  name: "ProductOrganization",
  mode: TeamMode.Coordinate,
  model: openai("gpt-4o"),
  members: [productManager, engineeringTeam, designTeam],
  instructions: `The PM writes the spec first. Then delegate to Engineering
    for a technical plan and Design for the UX/visual plan in parallel.
    Synthesize everything into a launch-ready project brief.`,
});

const result = await productOrg.run(
  "Plan the v2 launch of our analytics dashboard: new chart types, real-time filters, CSV/PDF export, and a share-via-link feature."
);
console.log(result.text);

import {
  Agent,
  Team,
  TeamMode,
  openai,
  SqliteStorage,
} from "@agentium/core";

const storage = new SqliteStorage({ path: "./team-memory.db" });

const analyst = new Agent({
  name: "Analyst",
  model: openai("gpt-4o"),
  instructions: `You analyze business data and produce quantitative insights.
    Reference earlier findings when available to maintain continuity.
    Always cite the metric, time range, and percentage change.`,
  memory: {
    storage,
    session: { maxMessages: 50 },
    summaries: { enabled: true, model: openai("gpt-4o-mini") },
  },
  register: false,
});

const reporter = new Agent({
  name: "Reporter",
  model: openai("gpt-4o"),
  instructions: `You write executive reports from analysis.
    Reference previous reports in this session for trend continuity.
    Structure: Executive Summary → Key Metrics → Recommendations.`,
  memory: {
    storage,
    session: { maxMessages: 50 },
    summaries: { enabled: true, model: openai("gpt-4o-mini") },
  },
  register: false,
});

const analyticsTeam = new Team({
  name: "AnalyticsTeam",
  mode: TeamMode.Coordinate,
  model: openai("gpt-4o-mini"),
  members: [analyst, reporter],
  sessionState: { quarter: "Q1-2026", region: "North America" },
  instructions: "The Analyst produces insights first, then the Reporter writes the executive summary.",
});

const sessionId = "analytics-session-q1-2026";

const q1Report = await analyticsTeam.run(
  "Summarize Q1 revenue trends. We saw $12.4M in ARR with 8% MoM growth.",
  { sessionId }
);
console.log("Q1 Report:\n", q1Report.text);

// Second call — agents recall the Q1 analysis from the shared session
const comparison = await analyticsTeam.run(
  "Compare these Q1 trends with Q4-2025 ($11.1M ARR, 5% MoM growth). What's accelerating?",
  { sessionId }
);
console.log("\nComparison:\n", comparison.text);

// Third call — the full conversation history is available
const forecast = await analyticsTeam.run(
  "Based on the Q4→Q1 acceleration, project Q2 ARR assuming the same growth trajectory.",
  { sessionId }
);
console.log("\nForecast:\n", forecast.text);

import { Agent, Team, TeamMode, openai, CostTracker } from "@agentium/core";

const costTracker = new CostTracker({
  budget: {
    maxCostPerRun: 0.50,
    onBudgetExceeded: "warn",
  },
});

const researcher = new Agent({
  name: "Researcher",
  model: openai("gpt-4o"),
  instructions: `Research topics thoroughly using long-form reasoning.
    Cite at least 3 data points per claim. Be comprehensive.`,
  costTracker,
  register: false,
});

const synthesizer = new Agent({
  name: "Synthesizer",
  model: openai("gpt-4o-mini"),
  instructions: `Distill research into a concise, actionable summary.
    Target 150 words. Use bullet points for key findings.`,
  costTracker,
  register: false,
});

const qualityReviewer = new Agent({
  name: "QualityReviewer",
  model: openai("gpt-4o-mini"),
  instructions: `Review the summary for accuracy against the original research.
    Flag any claims that were oversimplified or lost nuance.`,
  costTracker,
  register: false,
});

const costAwareTeam = new Team({
  name: "CostAwareResearch",
  mode: TeamMode.Coordinate,
  model: openai("gpt-4o-mini"),
  members: [researcher, synthesizer, qualityReviewer],
  instructions: `Research first, then synthesize, then quality-review.
    Use gpt-4o only for deep research; gpt-4o-mini for synthesis and review.`,
});

const result = await costAwareTeam.run(
  "Research the current state of solid-state battery technology for EVs. Focus on energy density breakthroughs and projected commercial timelines."
);
console.log(result.text);

const summary = costTracker.getSummary();
console.log(`\n--- Cost Summary ---`);
console.log(`Total cost:   $${summary.totalCost.toFixed(4)}`);
console.log(`Total tokens: ${summary.totalTokens.totalTokens.toLocaleString()}`);
console.log(`  ├─ Input:   ${summary.totalTokens.inputTokens.toLocaleString()}`);
console.log(`  └─ Output:  ${summary.totalTokens.outputTokens.toLocaleString()}`);
console.log(`Entries:      ${summary.entries}`);
console.log(`\nPer-agent breakdown:`);

for (const [agent, stats] of Object.entries(summary.byAgent)) {
  console.log(
    `  ${agent}: $${stats.cost.toFixed(4)} | ${stats.tokens.totalTokens.toLocaleString()} tokens | ${stats.runs} call(s)`
  );
}