// Or pass apiKey inline (multi-tenant apps, tests, dynamic config)
await generateText({ provider: 'openai', apiKey: 'sk-...', prompt: '...' });

import { generateText } from '@framers/agentos';

// Auto-detect provider from env vars
const { text } = await generateText({
  prompt: 'Explain TCP handshakes in 3 bullets.',
});

// Pin a provider
const { text: claude } = await generateText({
  provider: 'anthropic',
  prompt: 'Compare TCP and UDP.',
});

import { buildFallbackChain, generateText } from '@framers/agentos';

const { text } = await generateText({
  provider: 'anthropic',
  prompt: 'Compare TCP and UDP.',
  fallbackProviders: buildFallbackChain('anthropic'),
});

import { streamText } from '@framers/agentos';

const stream = streamText({ provider: 'openai', prompt: 'Write a haiku.' });
for await (const chunk of stream.textStream) process.stdout.write(chunk);

import { generateObject } from '@framers/agentos';
import { z } from 'zod';

const { object } = await generateObject({
  provider: 'gemini',
  schema: z.object({
    sentiment: z.enum(['positive', 'negative', 'neutral']),
    topics: z.array(z.string()),
  }),
  prompt: 'Analyze: "Great camera but disappointing battery."',
});

import { agent } from '@framers/agentos';

const bot = agent({ provider: 'anthropic', instructions: 'You are a helpful assistant.' });
const reply = await bot.session('demo').send('What is 2+2?');
console.log(reply.text);

const tutor = agent({
  provider: 'anthropic',
  instructions: 'You are a patient CS tutor.',
  personality: {
    openness: 0.9,
    conscientiousness: 0.95,
    agreeableness: 0.85,
  },
  memory: {
    types: ['episodic', 'semantic'],
    working: { enabled: true, maxTokens: 1200 },
  },
});

const session = tutor.session('student-1');
await session.send('Explain recursion with an analogy.');
await session.send('Can you expand on that?'); // remembers context

import { agency } from '@framers/agentos';

const team = agency({
  strategy: 'graph',
  agents: {
    researcher: { provider: 'anthropic', instructions: 'Find relevant facts.' },
    writer:     { provider: 'openai', instructions: 'Write a clear summary.', dependsOn: ['researcher'] },
    reviewer:   { provider: 'gemini', instructions: 'Check accuracy.', dependsOn: ['writer'] },
  },
});

const result = await team.generate('Compare TCP vs UDP for game networking.');

import { generateImage, generateVideo, generateMusic, performOCR, embedText } from '@framers/agentos';

const image = await generateImage({ provider: 'openai', prompt: 'Neon cityscape at sunset' });
const video = await generateVideo({ prompt: 'Drone over misty forest' });
const music = await generateMusic({ prompt: 'Lo-fi hip hop beat' });
const ocr   = await performOCR({ image: './receipt.png', strategy: 'progressive' });
const embed = await embedText({ provider: 'openai', input: ['hello', 'world'] });

import { workflow, AgentGraph, mission } from '@framers/agentos/orchestration';

// 1. workflow() — deterministic DAG
const pipe = workflow('content').step('research', { tool: 'web_search' }).then('draft', { gmi: { instructions: '...' } }).compile();

// 2. AgentGraph — cycles, subgraphs
const graph = new AgentGraph('review').addNode('draft', gmiNode({...})).addNode('review', judgeNode({...})).addEdge('draft','review').compile();

// 3. mission() — goal-driven, planner decides steps
const m = mission('research').goal('Research {topic}').planner({ strategy: 'adaptive' }).compile();

// 1. Separate fields (recommended)
generateText({ provider: 'anthropic', model: 'claude-sonnet-4-20250514', prompt: '...' });

// 2. Colon format (canonical combined string)
generateText({ model: 'anthropic:claude-sonnet-4-20250514', prompt: '...' });

// 3. Slash format (also supported for known providers)
generateText({ model: 'anthropic/claude-sonnet-4-20250514', prompt: '...' });

// Auto-detect (omit both provider and model)
generateText({ prompt: '...' }); // uses first available provider