Building AI Agents & Agentic AI System via Microsoft Autogen

Install Anaconda and VS Code to create and manage Python environments, then set the path and use VS Code to run .ipynb and .py files.

Learn to create and manage virtual environments with conda, activate them, and install packages via a requirements.txt in your project workspace.

Learn to create virtual environments and manage Python projects with the UV package manager, a fast rust-based tool, using uv init, uv venv, and uv add.

Discover how to set up a python project in VS Code, create a conda environment with python 3.12, and run code and Jupyter notebooks with ipykernel.

Explore Python basics: syntax and semantics, comments, case sensitivity, and indentation. Practice variable assignment and type inference with practical code examples.

Explore variables in Python by declaring, assigning, and naming them, and understand variable types. Master type checking, dynamic typing, and conversion with practical examples like input and a calculator.

Learn Python data types from basics to advanced, covering integers, floating point numbers, strings, and booleans, plus lists, tuples, sets, and dictionaries, with type conversion and common errors.

Explore Python operators, including arithmetic, comparison, and logical operators, with practical examples and a calculator project that demonstrates addition, subtraction, division, modulus, and exponentiation.

Explore Python control flow through conditional statements (if, elif, else), including nested conditionals, with practical examples like leap year checks and simple calculators, plus common errors and best practices.

Explore Python loops in depth, mastering for and while loops, range syntax, and loop control statements like break, continue, and pass, plus nested loops and practical examples.

Learn Python lists as ordered, mutable data structures; create, access, modify, slice, and iterate, and master list methods and list comprehension, including nested forms.

Explore real-world uses of Python lists with to-do lists, grades, and inventory, using append, remove, in checks, and built-in functions like max, min, sum to compute averages.

Explore how Python sets store unique items, enable membership tests, and perform union, intersection, and difference operations, with practical examples for removing duplicates and counting unique words.

Master dictionaries in Python by creating, accessing, and modifying key-value pairs; explore nested dictionaries, comprehension, and common methods like keys, values, and items, with practical coding examples.

Explore tuples in Python: creating, accessing, and manipulating immutable ordered collections, including packing and unpacking, nested tuples, and common methods like count and index, with practical examples.

Explore how to define and call Python functions, use parameters including default and variable-length arguments, distinguish positional and keyword arguments, and implement return statements and docstrings to organize code.

Explore practical Python function examples, including temperature conversion, password strength checks, shopping cart total cost, palindrome detection, recursive factorial, file word frequency counting, and email validation with regular expressions.

Explore Python lambda functions, an anonymous one-expression tool for quick operations and higher-order use. Learn syntax, examples, and how map and filter leverage lambda for list processing.

Master the Python map function to apply a function to every item in an iterable, returning an iterator and transforming lists with examples using square, lambda, and dict operations.

Explore how the Python filter function creates iterators by applying a condition to an iterable, including using lambda expressions for even numbers, greater-than-five values, and dictionary records.

Learn to import modules and packages in Python, use built-in math and numpy, install packages, create custom modules, and organize code with init files and subpackages.

Explore the Python standard library overview, covering modules such as array, math, random, os, json, csv, datetime, time, and re for file operations, data serialization, and pattern matching.

Explore Python file operations by reading and writing text and binary files using with open. Learn text line reading, appending, writing vs overwriting, and cursor seek to reset reading position.

Create directories, list files and folders, join paths, and check for file or directory existence using the OS module, while distinguishing absolute and relative paths.

learn exception handling in Python using try, except, else, and finally blocks to gracefully handle errors, from name and zero division errors to file operations that close resources reliably.

Explore classes and objects in Python, define attributes and methods, and initialize with a constructor using self, shown through a bank account example with deposit, withdraw, and balance.

Learn how Python object-oriented programming implements inheritance, using single and multiple inheritance to share attributes and methods between parent and child classes, with practical examples.

Explore polymorphism in Python through method overriding and interfaces, use abstract base classes, and see shape and vehicle examples including rectangle and circle area calculations and start engine.

Explore encapsulation and abstraction in Python object-oriented programming by bundling data and methods into a single unit, restricting access with private and protected variables and using getters and setters.

Learn how abstraction hides complex implementation details in object-oriented programming, using abstract base classes and abstract methods to expose only necessary features, illustrated with a vehicle example and Python code.

Explore how magic methods, or dunder methods, define object behavior for built-in operations in Python. Override __init__, __str__, and __repr__ to customize initialization and string representations of objects.

Explore operator overloading in Python by overriding magic methods like __add__, __sub__, __mul__, and __truediv__ for vectors, including comparisons and representations with __repr__.

Explore custom exception handling in python by creating a generic error class and a date of birth exception, then validate age between 20 and 30 with a try-catch block.

Explore Python iterators to enable efficient looping and memory management, using lazy loading with list and string examples and handling stop iteration with next.

Master Python generators by using yield to lazily produce values and save memory, compare with iterators, and apply to processing large files efficiently.

Explore advanced python decorators, including function copy and closures, to modify function behavior without changing code, create reusable wrappers, and apply decorators with arguments using @ syntax.

Learn how to implement Python logging to track events and errors in AI projects, configuring basic logging, using levels (debug to critical), formatting, and writing logs to app.log.

Learn to log with multiple loggers in Python's logging framework by creating module-specific loggers, setting their levels, and emitting debug, warning, and error messages.

Demonstrate Python logging by configuring level, format, and file and stream handlers, writing to app_one.log; define arithmetic app logger with add, subtract, multiply, divide and debug and error messages.

Explore Pydantic, a fast, type-hint driven data validation library for Python, and see how it enforces JSON schema, data types, and reliable API responses in FastAPI and Lang Graph workflows.

Explore Pydantic practical implementation to build robust data validation in Python, using base models, optional fields, lists, and nested structures, with schema generation and field constraints.

Explore what agentic AI is, how autonomous AI agents decide and act, and how multi-agent workflows use tools to automate tasks across real-world scenarios.

Learn how microsoft autogen serves as a framework for building AI agents and applications, enabling single and multi-agent systems to collaborate on end-to-end solutions. Explore its architecture, components, and open-source ecosystem within microsoft ai, azure, and semantic kernels.

Explore autogen v0.4, a complete rewrite with asynchronous foundation and layered design, clarifying the AG2 split, 0.2 to 0.4 migration, and observability for production with Azure Semantic Kernel.

Set up Microsoft Autogen in VSCode by creating a project folder, configuring a conda environment within the project, and installing autogen via a requirements.txt file for practical, code-along development.

Configure the OpenAI API key for Autogen by creating a dot env and secret key, enabling LM power for end-to-end agents and a virtual environment.

Explore large language model providers to power your first autogen agent, including Gemini and OpenAI. Get keys, configure a model client, and use free or local options.

Build your first Autogen agent in a Jupyter notebook, connect to GPT-4 via a model client, and run end-to-end practice with an Autogen environment.

Explore the Autogen architecture, apps, the Autogen framework, and developer tools, with core, agent chat, and extensions powering scalable ai agents.

Compare Autogen with Langchain and other frameworks, focusing on architecture, multi-agent capabilities, and observability; Autogen’s asynchronous design enables collaborative AI, whereas Langchain favors sequential, single-agent workflows.

Compare Autogen with other AI frameworks, highlighting its core and agent chat APIs, template-based customization, maturity, and use cases from team tasks to complex enterprise workflows.

Compare Microsoft Autogen and Microsoft Semantic Kernel, highlighting Autogen's multi-agent collaboration and standalone agent network against Semantic Kernel’s integration, function composition, and skill memory for AI in existing apps.

Compare Autogen and Lama index to show Autogen's strength in dynamic multi-agent interactions and task automation, versus Lama index's knowledge retrieval and document integration for enterprise data.

Master async functionality in Python to build real-time agent interactions with Autogen. Learn synchronous vs asynchronous, subroutine versus coroutine, and concurrency and parallelism, with practical code demonstrations.

Explore Python async functionality with practical examples, contrasting synchronous code with coroutines, await, and asyncio sleep, and discuss Autogen synchronization in ai agents.

Explore the agent chat api built on autogen core, a modern async, event-driven framework to define and chat with assistant agents, customize prompts, and connect tools and data.

Build your first agent with the Autogen Agent Chat API in a Jupyter notebook, wiring a GPT-4 model client and running asynchronous conversations to answer real tasks.

Configure the agent's brain by selecting OpenAI Gemini or other locally hosted models with Autogen, and learn to connect via the OpenAI chat completion client using a Gemini API key.

Configure your agent to run local llms using autogen and llama, test via Google Colab, and learn to connect to a local model server for private, cost-conscious deployments.

Configure LLMs on an agent with OpenRouter's free API key, access a range of models, and set the base URL for autogen without high-end hardware or costly cloud services.

Explore agent customization in Autogen by defining an agent's role with system messages and tailored prompts, then apply prompt engineering to create specialized, engaging AI assistants.

Explore how tools in Autogen version 0.4 let an agent act by calling external services and APIs, using a weather tool as a practical example.

Explore messages in Autogen version 0.4 and how agents communicate, using text and image messages for multimodal tasks, with a hands-on coding demo and future tool messages.

Level up by sending multi-modal messages that combine text and images to an agent, using a function to prepare requests, convert images via PIL, and test multimodal outputs.

Learn how to run and observe a single agent in Microsoft Autogen, using agent run and on message stream to inspect tool calls, inner messages, and streaming output.

learn to enforce structured output in ai agents using autogen, pydantic base models, and json formats to power reliable api calls with clearly defined planet info fields.

Explore multi-agent collaboration in Autogen, connecting specialized agents to tackle complex tasks through shared messages and tasks. Understand how specialization, asynchronous execution, and resiliency boost performance over a single agent.

Explore practical multi-agent collaboration in Autogen by building a three-agent team: plot writer, character writer, and ending writer, using round-robin interaction to craft richer, more engaging short stories.

Observe how a multi-agent team built with round robin group chat collaborates in real time using Autogen, streaming live messages and task results to reveal stop reasons and collaboration dynamics.

Explore team operation in Autogen by observing agents, running them in a round-robin group chat, and applying reset, stop, resume, and abort to manage stateful workflows.

Explore how termination conditions control Autogen teams, including max message termination and text mention termination, and how to combine them for production-ready workflows.

Learn how external termination controls stopping a team from outside the run in Microsoft Autogen, using a set method to stop after the current turn completes.

Discover how aborting a team immediately stops execution and raises a canceled error, using a cancellation token to cancel pending async calls in Autogen core.

Discover single agent teams in Microsoft Autogen, enabling one agent to function as a controllable, observable, scalable team with termination conditions; learn setup and testing via a short poem task.

Save and load the state of agents and teams to disk or a database to preserve past interactions, then load that memory into new agents for continuity in stateless apps.

Learn to manage the state of autonomous teams in Microsoft Autogen by saving and loading team and agent state, resetting contexts, and persisting state to enable reliable reloading.

Learn to save and load the team state of agents by serializing a dictionary to a json file, enabling persistence and loading after crashes with Microsoft Autogen.

Explore human in the loop concepts in Autogen, interact with a team of agents using a user proxy and run-time feedback, and learn how feedback shapes agent outputs.

Explore how to provide feedback for the next run in the Microsoft Autogen series, using either maximum turns or termination conditions to influence subsequent team runs and maintain state.

Provide feedback between runs using max turns to pause for user input. Use a round-robin team of three agents (narrator, hero, guide) preserving state for continuous engagement.

Learn how to provide feedback to next runs using termination conditions in Microsoft Autogen, including text termination, max turns, and human-in-the-loop feedback.

Learn to implement handoff termination in Autogen, pausing runs to capture user feedback, using handoff messages, termination conditions, and text mention termination for interactive AI agents.

Explore how tools empower agentic ai in autogen to perform tasks, automate workflows, and extend an agent's capabilities with custom function tools, built-in tools, and third-party integrations.

Explore the types of tools in Autogen—custom function tools, inbuilt tools, and third-party tools—showing how agents call APIs, run code, and perform web searches with function wrappers.

Create ai agents and teams with Autogen Studio using drag-and-drop, test in the playground, and save artifacts to a gallery in a no-code interface.

Autogen studio, a ui for building ai agents without coding, built on Microsoft Autogen. Install via pip, export api key, and run the ui to design and test agents.

Explore the Autogen Studio UI to build teams with agents, models, tools, and termination conditions, configure default models, test API keys, and deploy multi-agent workflows.

Explains Autogen studio components—agents, models, tools, and terminations—how teams use round-robin or selector workflows to manage AI assistants.

Learn to configure Autogen Studio teams for round robin and selector group chats, using prebuilt agents, tools, and models, edit and validate JSON, and test deployments in the playground.

Explore how Autogen Studio's web and deep search team orchestrates multi-modal agents to search, fetch, and summarize web content using Google search and Playwright with verifier and summary agents.

Showcases integrating a local LLM with Autogen Studio via LM Studio, configuring a Mistral 7B model, and testing locally. It also covers addressing setup errors to ensure reliable local inference.

Explore Autogen Studio FAQs and JSON team setup, covering GPT-4 mini, OpenAI, Azure, Anthropic, and local models; define an assistant agent and round-robin team, and test configurations.