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Building AI Agents

Learn how to build powerful AI agents with persistent memory, context understanding, and intelligent recall. Comprehensive guides for developers.

AI Agent Guide

An Introduction to AI Agents

AI agents are advanced AI systems that use a Large Language Model (LLM) as their core "brain" to autonomously interpret instructions, make decisions, and act to accomplish goals.

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AI Agent Guide

The Complete Guide to Building LangChain Agents

Building applications with LLMs presents unique challenges, particularly in orchestrating complex tasks and managing memory. LangChain and LangGraph are popular frameworks that have gained attention for their ability simplify these processes, allowing for more efficient and effective AI agent development.

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AI Agent Guide

A Developer's Guide to the MCP

This guide provides a clear breakdown of MCP's key concepts, compares it with existing API standards like OpenAPI, and includes practical examples to help you effectively incorporate MCP into your own development workflow.

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AI Agent Guide

LangGraph Tutorial: Building Agents with LangChain's Agent Framework

Learn how LangGraph, an AI agent framework built by LangChain, allows developers to create complex and flexible agent workflows using stateful graphs and built-in memory management.

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AI Agent Guide

LLM Evaluation Framework: Evaluating LLM-based Applications

Learn how to effectively evaluate LLM-based applications, including selecting appropriate metrics and creating evaluation datasets, to optimize performance and meet business requirements.

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AI Agent Guide

Reducing LLM Hallucinations: A Developer's Guide

Large Language Models (LLMs) are powerful but prone to hallucination - generating confident but incorrect output. This guide explains why hallucinations occur and provides strategies to mitigate them, aiming to help developers build more truthful and reliable LLM applications.

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AI Agent Guide

Prompt Engineering for Reasoning Models

Recent large language models (LLMs), such as OpenAI's O1/O3 series, Anthropic's Claude 3.7, and DeepSeek R1, have shifted from basic text generation to internal multi-step reasoning. Unlike earlier models like GPT-4o, which primarily generate responses via next-word prediction, these reasoning-focused models internally simulate logical analysis before responding12. Prompt engineering strategies must evolve accordingly.

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