lindy-reference-architecture

Lindy Reference Architecture

Overview

Production-ready reference architectures for Lindy AI integrations covering basic integration, event-driven, multi-agent orchestration, and high-availability patterns.

Prerequisites

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

Instructions

Step 1: Choose Architecture Pattern

Select based on requirements: Basic (simple request/response), Event-driven (async with queues), Multi-agent (orchestrated pipelines), or HA (failover and caching).

Step 2: Implement Basic Integration

Set up Express backend with Lindy SDK for simple synchronous agent interactions.

Step 3: Add Event-Driven Processing (if needed)

Use BullMQ workers to process agent tasks asynchronously from a queue, emitting results on completion.

Step 4: Set Up Multi-Agent Orchestration (if needed)

Build an orchestrator that plans work, delegates to specialized agents (research, analysis, writing), and synthesizes results.

Step 5: Configure High Availability (if needed)

Create an HA client with primary/fallback Lindy instances and Redis caching for automatic failover.

For detailed implementation code and architecture diagrams, load the reference guide: Read(${CLAUDE_SKILL_DIR}/references/implementation-guide.md)

Output

• Architecture pattern selection guide
• Basic integration implementation
• Event-driven worker setup
• Multi-agent orchestration
• HA client with failover

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

• Lindy Architecture Guide
• Best Practices
• Case Studies

Next Steps

Proceed to Flagship tier skills for enterprise features.

Examples

Basic usage: Apply lindy reference architecture to a standard project setup with default configuration options.

Advanced scenario: Customize lindy reference architecture for production environments with multiple constraints and team-specific requirements.