MCP is Anthropic's open protocol that standardizes how AI agents connect to tools, APIs, and resources — making any capability pluggable and interoperable across agent frameworks.
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Category Archives: Artificial Intelligence
Dive into the world of Artificial Intelligence with practical, in-depth articles on the protocols, architectures, and hands-on projects that power modern AI systems. This category covers topics like building LLM chatbots with Python and LangChain from scratch — including conversation memory, RAG (Retrieval-Augmented Generation), and Streamlit UIs — as well as the Model Context Protocol (MCP) — Anthropic's standard for connecting AI agents to tools and APIs — and Agent-to-Agent (A2A) communication, Google's protocol for multi-agent collaboration. Whether you are building your first LLM chatbot or designing complex multi-agent pipelines, these articles will guide you through the key concepts, architectures, and hands-on implementations.
A2A is Google’s protocol for agent collaboration, enabling discovery, communication, and coordination across independent AI agents in scalable multi-agent systems.
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Add a Streamlit-based web UI to your LLM chatbot — session management, interactive chat window, and seamless integration with your existing LangChain backend.
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Add Retrieval-Augmented Generation (RAG) to your LLM chatbot — ingest PDFs and Wikipedia pages into a vector database and retrieve context dynamically.
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Learn how to build a context-aware LLM chatbot with Python and LangChain, managing conversation memory, generation parameters, and robust logging.
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Build a production-ready LLM chatbot with Python and LangChain. Step-by-step guide to scalable, configurable architecture for real-world applications.
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