Skill UI

This blueprint provides a scalable, production-grade reference architecture for advanced LLM applications using LangChain. It guides the implementation of layered design, provider abstraction (allowing seamless switching between LLMs like OpenAI and Anthropic), dynamic chain registry, robust RAG pipelines, and complex multi-agent orchestration. Ideal for building enterprise-level, maintainable, and highly flexible AI systems.

This comprehensive guide details how to safely upgrade LangChain versions (e.g., from 0.1.x to 0.3.x+). It covers critical breaking changes across both TypeScript and Python implementations, including updating import paths, converting legacy LLMChain to the modern LCEL pipe syntax, updating agent creation, and migrating memory management. Essential for maintaining and modernizing existing LLM applications.

Implement comprehensive end-to-end tracing for Large Language Model (LLM) calls, chains, and complex agents using Langfuse. This workflow demonstrates techniques like OpenAI drop-in wrappers, manual span creation for RAG pipelines, tracking streamed responses, and integrating with various LLM providers (OpenAI, Anthropic). Use this to monitor latency, track token usage, and debug complex AI features in production.

Framework for building stateful, multi-actor AI applications with LangGraph, covering graph construction, reducers, persistence, human-in-the-loop patterns, and ReAct agents suitable for Python production systems.

Fetch execution traces from LangSmith Studio to inspect LangChain/LangGraph agent runs, analyze errors, tool calls, memory usage, and performance, and export sessions for deeper diagnostics using the langsmith-fetch CLI.

This guide provides the framework for configuring Continuous Integration and Continuous Deployment (CI/CD) pipelines specifically for Lindy AI agent integrations. It covers setting up automated tests for webhook receivers, validating payload processing, and checking authentication signatures using GitHub Actions and Node.js, ensuring robust and reliable deployment of agent services.

A comprehensive guide to diagnose and resolve common errors in Lindy AI agent workflows. Covers troubleshooting issues related to trigger failures (webhooks, email, schedule), action execution errors (Slack, Gmail, HTTP requests, code), and advanced agent step logic problems, ensuring reliable automation.

A comprehensive guide and tool for building and configuring complex, multi-step AI agent workflows. Users can define sophisticated automation pipelines by chaining together customizable triggers (e.g., email, webhook), actions (e.g., Slack post, database update), conditional logic, and external knowledge bases. Ideal for automating business processes like customer support triage, data processing, and complex notifications, ensuring deterministic and scalable AI operations.

Master the creation of complex, event-driven, and scheduled automation workflows. This guide covers setting up various triggers (email, chat, webhook, calendar), implementing conditional filters, managing multi-step processes, and orchestrating specialized AI agents in a multi-agent 'Society' for advanced delegation and collaboration.

This guide provides a comprehensive strategy for managing and reducing expenses within the Lindy AI platform. Learn how to audit agent credit consumption, right-size models by using less expensive versions for simple tasks, consolidate redundant agents, and optimize trigger frequency. Implementing these steps ensures cost efficiency and sustainable operation of your AI workflows.

This skill provides comprehensive best practices for managing data within AI agent workflows. It covers data classification (PII, PHI), prompt-level controls, secure memory usage, and isolation techniques. It ensures that agents adhere to global regulations like GDPR, CCPA, and HIPAA when handling sensitive or regulated data.

A comprehensive diagnostic toolkit designed for advanced troubleshooting of Lindy AI agents. This bundle systematically collects environment information (OS, Python, Node.js), tests API connectivity for webhooks and major integrations (Slack, Notion, Gmail), reviews task history, and guides the user in generating a full, structured support ticket for technical support.