Agents

Concept

Agent Memory Explained

How AI agents remember context across sessions — short-term window, long-term stores, vector memory, and when you do not need memory at all.

Concept

AI Agents Explained

Agents are LLMs that can take actions in a loop. Here's what that actually means, where they shine, and where they fall over.

Concept

Browser Agents Explained

AI that controls a web browser — clicking, typing, scraping, and filling forms. Use cases, risks, and how it differs from API-based automation.

Concept

Function Calling Explained

Function calling is how LLMs trigger your code. Here's how it actually works under the hood, and how to design tools that the model uses correctly.

Concept

Human-in-the-Loop Automation Patterns

When to require human approval in AI automations — approval gates, review queues, selective auto-send, and the metrics that tell you when to loosen the loop.

Concept

Idempotency and Retries for LLM Workflows

How to retry failed LLM and tool calls without double-charging customers, sending duplicate emails, or corrupting agent state.

Concept

MCP Explained

The Model Context Protocol is the USB-C of AI tooling. Here's what it is, why it matters, and how to think about it.

Concept

Top GitHub Repos Every AI Builder Should Know (2026)

Fifteen open-source projects that cover 90% of what AI engineers actually install — agents, RAG, observability, routing, and local inference.

Tool Guide

Dify Guide

The definitive guide to Dify for low-code AI apps — RAG chatbots, agent workflows, MCP import, self-host vs cloud, and when to pick it over Flowise or n8n.

Tool Guide

n8n Guide

The definitive guide to n8n for AI workflows — self-host vs cloud, agent nodes, LLM chaining, code escape hatches, and when to pick it over Zapier or Make.

Workflow

Build an MCP Server from Your API

Wrap your REST or GraphQL API as an MCP server so Claude Desktop, Cursor, and any MCP client can call it — write once, use everywhere.

Comparison

LangGraph vs CrewAI

An honest comparison of the two most popular open-source multi-agent frameworks — when graphs beat roles, and when CrewAI ships faster.

Case Study

Case Study: One MCP Server, Three AI Clients

How an internal platform team wrapped their admin API as an MCP server — Claude Desktop, Cursor, and a custom support bot shared the same tools.

Interview

How do you decide which tools to give an AI agent, and how many is too many?

Tool design is the main lever for agent reliability — bad tool sets cause wrong calls, loops, and security holes.

Interview

How would you design an AI agent that books meetings on behalf of a user?

Tests agent design fundamentals — tool design, safety, reliability under autonomy.

Directory

CrewAI

Multi-agent orchestration framework — define roles, goals, and tasks; agents collaborate on research, writing, and analysis jobs.

Directory

Dify

Open-source platform for building AI apps — visual workflows, RAG pipelines, agents, and an embeddable chat UI.

Directory

Flowise

Open-source visual builder for LLM chains and agents — drag-and-drop nodes for RAG, tools, and chatflows.

Directory

LangGraph

Stateful graph framework for building reliable multi-step AI agents with cycles, checkpoints, and human-in-the-loop.

Directory

Mastra

TypeScript framework for AI agents — workflows, tool calling, RAG, evals, and deploy targets tuned for full-stack JS teams.

Directory

Mem0

Open-source memory layer for AI agents — extract, store, and retrieve user facts across sessions with vector search.

Directory

Pydantic AI

Type-safe agent framework from the Pydantic team — tools, structured outputs, and dependency injection in Python and TypeScript.