Ai Agents

Large Language Models (LLMs) excel at processing text, but they lack real-time web access. By integrating browsers, developers can empower LLMs to fetch live data, automate tasks, and interact dynamically with websites. This zero-to-hero tutorial covers core methods—browser APIs, web scraping, automation, and agent pipelines—with practical Python/JS examples using tools like LangChain, Playwright, Selenium, and more. Why Browsers + LLMs? Key Use Cases Browsers bridge LLMs’ knowledge gaps by enabling: ...

Introduction The rise of AI agents has created a fundamental challenge: how do you connect dozens of LLMs to hundreds of external tools without writing custom integrations for every combination? This is the “N×M problem”—managing connections between N models and M tools becomes exponentially complex. The Model Context Protocol (MCP) solves this by providing a standardized interface between AI systems and external capabilities. Docker’s integration with MCP takes this further by containerizing MCP servers, adding centralized management via the MCP Gateway, and enabling dynamic tool discovery. ...

Introduction Most LLM systems are fundamentally reactive: you ask a question, they generate an answer, and that’s it. If the first answer is wrong, there’s no self-correction. If the task requires multiple steps, there’s no iteration. If results don’t meet expectations, there’s no refinement. The React Loop changes this paradigm entirely. It transforms a static, one-shot LLM system into a dynamic, iterative agent that can: Sense its environment and gather context Reason about what actions to take Act by executing tools and generating responses Observe the results of its actions Evaluate whether it succeeded or needs to try again Learn from outcomes to improve future iterations The core insight: ...

Introduction The difference between a chatbot and an agent isn’t just autonomy—it’s memory. A chatbot responds to each message in isolation. An agent remembers context, learns from outcomes, and evolves behavior over time. Agent memory is the system that enables this persistence: storing relevant information, retrieving it when needed, updating beliefs as reality changes, and forgetting what’s no longer relevant. Without memory, agents can’t maintain long-term goals, learn from mistakes, or provide consistent experiences. ...

Introduction Traditional RAG systems treat knowledge as a collection of text chunks—embedded, indexed, and retrieved based on semantic similarity. This works well for simple factual lookup, but fails when questions require understanding relationships, dependencies, or multi-hop reasoning. Graph RAG fundamentally reimagines how knowledge is represented: instead of flat documents, information is structured as a graph of entities and relationships. This enables LLMs to traverse connections, follow dependencies, and reason about how concepts relate to each other. ...