Production Features

import { Agent, openai, anthropic, google, withFallback } from "@agentium/core";

const resilientModel = withFallback(
  [openai(process.env.OPENAI_MODEL ?? "gpt-6.1-sol"), anthropic(process.env.ANTHROPIC_MODEL ?? "claude-sonnet-5-5"), google(process.env.GOOGLE_MODEL ?? "gemini-3.8-flash")],
  {
    circuitBreaker: { failureThreshold: 3, cooldownMs: 30_000 },
    onFallback: (from, to, error) => {
      console.log(`Fallback: ${from} → ${to} (${error})`);
    },
  },
);

const agent = new Agent({
  name: "resilient-agent",
  model: resilientModel,
  instructions: "You are a helpful assistant.",
});

const result = await agent.run("What is the capital of France?");

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

const router = new ModelRouter({
  tiers: [
    { model: openai(process.env.OPENAI_FAST_MODEL ?? "gpt-6-luna"), maxComplexity: 0.3 },
    { model: openai(process.env.OPENAI_MODEL ?? "gpt-6.1-sol"), maxComplexity: 0.7 },
    { model: openai("o3"), maxComplexity: 1.0 },
  ],
  outcomeTracking: true,
});

const agent = new Agent({ name: "smart-agent", model: router });
// Select current models for each routing tier; see Example models.

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

const agent = new Agent({
  name: "reflective-agent",
  model: openai(process.env.OPENAI_MODEL ?? "gpt-6.1-sol"),
  reflection: {
    enabled: true,
    maxReflections: 2,
    loopEscapeDetection: true,
    postMortemLearning: true,
  },
});

import { TenantScopedStorage, InMemoryStorage } from "@agentium/core";

const base = new InMemoryStorage();
const acme = new TenantScopedStorage(base, "acme");
const globex = new TenantScopedStorage(base, "globex");

await acme.set("sessions", "s1", { user: "alice" });
await globex.set("sessions", "s1", { user: "bob" });
// Same key, completely isolated data

import { TokenRateLimiter, ConcurrencyLimiter } from "@agentium/core";

const limiter = new TokenRateLimiter({
  maxTokensPerMinute: 100_000,
  perTenant: true,
});

const status = limiter.acquire(500, { tenantId: "acme" });
// { allowed: true, remaining: 99500, resetMs: 58000 }

import { type Agent, type ModelProvider } from "@agentium/core";
import { ConversationSuite } from "@agentium/eval";

export async function evaluateSupport(agent: Agent, simulator: ModelProvider) {
  const suite = new ConversationSuite({
    name: "Support tests",
    scenarios: [{
      name: "Password reset",
      persona: { name: "Jane", description: "Non-technical user", goal: "Reset password" },
      initialMessage: "I can't log in!",
      successCriteria: "The assistant explains the approved password reset process.",
    }],
  }, simulator);
  return suite.run(agent);
}

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

const agent = new Agent({
  name: "long-runner",
  model: openai(process.env.OPENAI_MODEL ?? "gpt-6.1-sol"),
  contextCompactor: { maxContextTokens: 120_000, reserveTokens: 8_000, strategy: "hybrid" },
  compressToolResults: true,
  toolResultLimit: { maxChars: 20_000, strategy: "truncate" },
});

import { estimateProgress, toolResultPreview } from "@agentium/core";

// Estimate progress based on roundtrip count
const percent = estimateProgress(2, 10, "tools"); // ~25%

// Generate concise previews for long tool results
const preview = toolResultPreview("A long tool result goes here.", 100);