Multi-agent research team

npm install @agentskit/runtime @agentskit/adapters @agentskit/skills @agentskit/tools

import { createRuntime } from '@agentskit/runtime'
import { anthropic, openai } from '@agentskit/adapters'
import { planner, researcher } from '@agentskit/skills'
import { webSearch, filesystem } from '@agentskit/tools'
import type { SkillDefinition } from '@agentskit/core'

const writer: SkillDefinition = {
  name: 'writer',
  description: 'Synthesizes research findings into a clear, structured report.',
  systemPrompt: `You are a precise technical writer.

Take the research notes you receive and produce a report with:
- TL;DR (3 bullets)
- Body organized by theme (not by source)
- Inline citations [1] [2] linking to source URLs
- "Open questions" section if the research surfaced uncertainty

Be terse. Cut adjectives. Keep paragraphs short.`,
  tools: ['filesystem_write'],
  temperature: 0.4,
}

const runtime = createRuntime({
  adapter: anthropic({ apiKey: KEY, model: 'claude-sonnet-4-6' }),  // planner uses this
  tools: [],   // planner uses delegates, no direct tools
  maxSteps: 10,
  maxDelegationDepth: 2,
})

const result = await runtime.run(
  'Research the current state of WebGPU support across browsers and write a report at ./out/webgpu.md',
  {
    skill: planner,
    delegates: {
      researcher: {
        skill: researcher,
        adapter: openai({ apiKey: KEY, model: 'gpt-4o-mini' }),  // cheaper for research
        tools: [webSearch()],
        maxSteps: 5,
      },
      writer: {
        skill: writer,
        tools: [...filesystem({ basePath: './out' })],
        maxSteps: 3,
      },
    },
  },
)

console.log(result.content)
console.log(`\n— ${result.steps} steps total, ${result.toolCalls.length} tool calls`)

mkdir -p out && npx tsx research.ts
cat out/webgpu.md