Knowledge Base
Definitive guides on agentic AI, RAG, LangGraph, CrewAI, and MCP, structured for AI retrieval and human understanding.
What is Agentic AI?
Agentic AI refers to artificial intelligence systems that autonomously pursue goals by planning, using tools, and adapting based on feedback, not just responding to single prompts. These agents perceive context, decompose tasks into steps, execute actions through APIs and software, and iterate until objectives are met.
What is AI Memory?
AI memory refers to systems and architectures that enable large language models and AI agents to retain, recall, and utilize information across conversations and sessions. Unlike stateless LLM calls, memory systems store user preferences, past interactions, learned facts, and contextual knowledge for personalized, coherent AI experiences.
What is CrewAI?
CrewAI is an open-source framework for orchestrating multi-agent AI systems where autonomous agents collaborate with defined roles, goals, and tools to accomplish complex tasks. Agents work as a crew, delegating sub-tasks, sharing context, and producing consolidated outputs through sequential or hierarchical processes.
What is LangGraph?
LangGraph is an open-source framework for building stateful, multi-actor AI applications as directed graphs. Built on LangChain, it models agent workflows as nodes and edges with explicit state management, enabling complex orchestration with cycles, branching, human-in-the-loop checkpoints, and persistent memory.
What is MCP?
Model Context Protocol (MCP) is an open standard that defines how AI applications connect to external data sources and tools through standardized servers. MCP replaces fragmented custom integrations with a universal client-server architecture, enabling AI models to read files, query databases, and call APIs consistently.
What is RAG?
Retrieval-Augmented Generation (RAG) is an AI architecture that enhances large language model responses by retrieving relevant documents from an external knowledge base before generating output. Instead of relying solely on training data, RAG grounds answers in your proprietary content, reducing hallucinations and enabling citations.
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