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lindy-reference-architecture

lindy-reference-architecture 是面向效率工具、数据与分析、软件开发的 Agent Skill,来自 jeremylongshore/claude-code-plugins-plus-skills。查看技能说明、操作步骤与使用约定,并通过原项目了解详细用法。

Reference architectures for Lindy AI integrations. Use when designing systems, planning architecture, or implementing production patterns. Trigger with phrases like "lindy architecture", "lindy design", "lindy system design", "lindy patterns".

SKILL.md

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namelindy-reference-architecture
descriptionReference architectures for Lindy AI integrations. Use when designing systems, planning architecture, or implementing production patterns. Trigger with phrases like "lindy architecture", "lindy design", "lindy system design", "lindy patterns".
allowed-toolsRead, Write, Edit
version1.0.0
licenseMIT
authorJeremy Longshore <[email protected]>

Lindy Reference Architecture

Overview

Production-ready reference architectures for Lindy AI integrations.

Prerequisites

  • Understanding of system design principles
  • Familiarity with cloud services
  • Production requirements defined

Architecture Patterns

Pattern 1: Basic Integration

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Client    │────▶│   Backend   │────▶│   Lindy AI  │
│   (React)   │◀────│   (Node.js) │◀────│   API       │
└─────────────┘     └─────────────┘     └─────────────┘
// Simple backend integration
import express from 'express';
import { Lindy } from '@lindy-ai/sdk';

const app = express();
const lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY });

app.post('/api/chat', async (req, res) => {
  const { message, agentId } = req.body;
  const result = await lindy.agents.run(agentId, { input: message });
  res.json({ response: result.output });
});

Pattern 2: Event-Driven Architecture

┌──────────────────────────────────────────────────────────┐
│                     Event Bus (Redis/SQS)                │
└────┬─────────────────┬─────────────────┬────────────────┘
     │                 │                 │
     ▼                 ▼                 ▼
┌─────────┐      ┌─────────┐      ┌─────────┐
│ Worker  │      │ Worker  │      │ Worker  │
│ (Agent) │      │ (Agent) │      │ (Agent) │
└────┬────┘      └────┬────┘      └────┬────┘
     │                │                │
     └────────────────┼────────────────┘
                      ▼
              ┌─────────────┐
              │  Lindy AI   │
              │    API      │
              └─────────────┘
// Event-driven worker
import { Queue } from 'bullmq';
import { Lindy } from '@lindy-ai/sdk';

const lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY });
const queue = new Queue('lindy-tasks');

// Producer
async function enqueueTask(agentId: string, input: string) {
  await queue.add('run-agent', { agentId, input });
}

// Consumer
const worker = new Worker('lindy-tasks', async (job) => {
  const { agentId, input } = job.data;
  const result = await lindy.agents.run(agentId, { input });

  // Emit result event
  await eventBus.publish('agent.completed', {
    jobId: job.id,
    result: result.output,
  });
});

Pattern 3: Multi-Agent Orchestration

                    ┌─────────────────┐
                    │   Orchestrator  │
                    │     Agent       │
                    └────────┬────────┘
                             │
           ┌─────────────────┼─────────────────┐
           │                 │                 │
           ▼                 ▼                 ▼
    ┌─────────────┐   ┌─────────────┐   ┌─────────────┐
    │  Research   │   │  Analysis   │   │  Writing    │
    │   Agent     │   │   Agent     │   │   Agent     │
    └─────────────┘   └─────────────┘   └─────────────┘
// Multi-agent orchestrator
class AgentOrchestrator {
  private lindy: Lindy;
  private agents: Record<string, string> = {
    research: 'agt_research',
    analysis: 'agt_analysis',
    writing: 'agt_writing',
    orchestrator: 'agt_orchestrator',
  };

  async execute(task: string): Promise<string> {
    // Step 1: Orchestrator plans the work
    const plan = await this.lindy.agents.run(this.agents.orchestrator, {
      input: `Plan steps for: ${task}`,
    });

    // Step 2: Execute each step
    const steps = JSON.parse(plan.output);
    const results: string[] = [];

    for (const step of steps) {
      const result = await this.lindy.agents.run(
        this.agents[step.agent],
        { input: step.task }
      );
      results.push(result.output);
    }

    // Step 3: Synthesize results
    const synthesis = await this.lindy.agents.run(this.agents.orchestrator, {
      input: `Synthesize: ${results.join('\n')}`,
    });

    return synthesis.output;
  }
}

Pattern 4: High-Availability Setup

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Load      │────▶│   App       │────▶│   Lindy     │
│   Balancer  │     │   Server 1  │     │   Primary   │
└─────────────┘     └─────────────┘     └──────┬──────┘
                                               │
                    ┌─────────────┐     ┌──────▼──────┐
                    │   App       │────▶│   Lindy     │
                    │   Server 2  │     │   Fallback  │
                    └─────────────┘     └─────────────┘
                                               │
┌─────────────┐     ┌─────────────┐            │
│   Cache     │◀────│   Shared    │◀───────────┘
│   (Redis)   │     │   State     │
└─────────────┘     └─────────────┘
// HA client with failover
class HALindyClient {
  private primary: Lindy;
  private fallback: Lindy;
  private cache: Redis;

  async run(agentId: string, input: string) {
    // Check cache first
    const cached = await this.cache.get(`${agentId}:${input}`);
    if (cached) return JSON.parse(cached);

    try {
      // Try primary
      const result = await this.primary.agents.run(agentId, { input });
      await this.cache.setex(`${agentId}:${input}`, 300, JSON.stringify(result));
      return result;
    } catch (error) {
      // Fallback
      console.warn('Primary failed, using fallback');
      return this.fallback.agents.run(agentId, { input });
    }
  }
}

Output

  • Architecture diagrams
  • Implementation patterns
  • HA/failover strategies
  • Multi-agent orchestration

Error Handling

Pattern Failure Mode Recovery
Basic API error Retry with backoff
Event-driven Worker crash Queue retry
Multi-agent Step failure Skip or fallback
HA Primary down Automatic failover

Resources

Next Steps

Proceed to Flagship tier skills for enterprise features.

来源:jeremylongshore/claude-code-plugins-plus-skills · MIT 许可 · 许可原文 · MD 为来源页保存的历史版本