mirror of
https://github.com/JackChen-me/open-multi-agent.git
synced 2026-10-19 01:29:57 -05:00
Jack Chen
98dbc2025e
* docs(examples): prefix example run commands with packages/core/ Examples live under packages/core/, and both READMEs document running them from the repo root (npx tsx packages/core/examples/<path>.ts), but the in-file Run: headers and a few READMEs still used the bare examples/ path. The repo root has no top-level examples/, so copy-pasting those commands from root failed with file-not-found. Prefixes 67 run/cd commands across 56 files (53 example scripts + examples/README.md + two integration READMEs). Markdown links that use the bare examples/ path are correct relative links and are left unchanged. * docs: prefix two stale examples/ path references in docs/ context-management.md and tool-configuration.md referenced bare examples/ paths that do not resolve from the repo root. Make the first a relative link (matching the convention in model-routing.md) and prefix the second with packages/core/. Follow-up to the run-command fixes in this PR.
74 lines
2.8 KiB
TypeScript
74 lines
2.8 KiB
TypeScript
/**
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* Structured Output
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*
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* Demonstrates `outputSchema` on AgentConfig. The agent's response is
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* automatically parsed as JSON and validated against a Zod schema.
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* On validation failure, the framework retries once with error feedback.
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*
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* The validated result is available via `result.structured`.
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*
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* Run:
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* npx tsx packages/core/examples/patterns/structured-output.ts
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*
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* Prerequisites:
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* ANTHROPIC_API_KEY env var must be set.
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*/
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import { z } from 'zod'
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import { OpenMultiAgent } from '../../src/index.js'
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import type { AgentConfig } from '../../src/types.js'
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// ---------------------------------------------------------------------------
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// Define a Zod schema for the expected output
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// ---------------------------------------------------------------------------
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const ReviewAnalysis = z.object({
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summary: z.string().describe('One-sentence summary of the review'),
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sentiment: z.enum(['positive', 'negative', 'neutral']),
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confidence: z.number().min(0).max(1).describe('How confident the analysis is'),
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keyTopics: z.array(z.string()).describe('Main topics mentioned in the review'),
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})
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type ReviewAnalysis = z.infer<typeof ReviewAnalysis>
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// ---------------------------------------------------------------------------
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// Agent with outputSchema
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// ---------------------------------------------------------------------------
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const analyst: AgentConfig = {
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name: 'analyst',
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model: 'claude-sonnet-4-6',
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systemPrompt: 'You are a product review analyst. Analyze the given review and extract structured insights.',
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outputSchema: ReviewAnalysis,
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}
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// ---------------------------------------------------------------------------
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// Run
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// ---------------------------------------------------------------------------
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const orchestrator = new OpenMultiAgent({ defaultModel: 'claude-sonnet-4-6' })
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const reviews = [
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'This keyboard is amazing! The mechanical switches feel incredible and the RGB lighting is stunning. Build quality is top-notch. Only downside is the price.',
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'Terrible experience. The product arrived broken, customer support was unhelpful, and the return process took 3 weeks.',
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'It works fine. Nothing special, nothing bad. Does what it says on the box.',
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]
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console.log('Analyzing product reviews with structured output...\n')
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for (const review of reviews) {
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const result = await orchestrator.runAgent(analyst, `Analyze this review: "${review}"`)
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if (result.structured) {
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const data = result.structured as ReviewAnalysis
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console.log(`Sentiment: ${data.sentiment} (confidence: ${data.confidence})`)
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console.log(`Summary: ${data.summary}`)
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console.log(`Topics: ${data.keyTopics.join(', ')}`)
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} else {
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console.log(`Validation failed. Raw output: ${result.output.slice(0, 100)}`)
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}
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console.log(`Tokens: ${result.tokenUsage.input_tokens} in / ${result.tokenUsage.output_tokens} out`)
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console.log('---')
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}
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