Build intelligent Multi-Agent applications with AutoGen 0.7

Learn how AI agents empower large language models with tools and private organizational data to provide context-aware answers through autonomous decision making, goal orientation, adaptability, and interactivity.

Explore how AI agents combine components and large language models to interpret information, access external knowledge, use tools and APIs, and automate business processes.

Explore large language models and their interactive use with chatgpt. Learn to connect ai agents to llm services via openai api, azure openai, and olma for llama.

Test the OpenAI API key in an AutoGen project by using a secure .env file, loading variables, and running code in VS Code.

learn how Autogen extensions access OpenAI services, configure user, system, and assistant messages, select a model like GPT four mini, and invoke the OpenAI client to respond.

Explore using open source llm models like llama 3.2 with AutoGen to build intelligent agents on your own hardware, installing and running locally, and integrating via AutoGen and Allama clients.

Autogen messages enable seamless communication between agents. They include agent-to-agent and internal events types, support text and multimodal content, and show how to define and send messages to coordinate actions.

The lecture shows defining a text message from the Autogen messages module, setting the user as source, and sending it to an assistant agent powered by a GPT-4 mini LLM.

Discover the AutoGen 0.6 agent chat framework, importing agents like base chat, assistant, code executor, society of mind, and user proxy, and using methods like own messages and on reset.

Examine two ways to use a function as a tool: function calls and the function tool class from Autogen core tools, including reflect on tool use.

Explore combining text and image data in multi-modal AI tasks within the AutoGen framework, using multimodal messages and the assistant agent to describe forest scenes and locate a cat.

Explore real-time token streaming from language models using the streaming feature. Use the assistant agent with model client stream set to true to print tokens as they are generated.

Develop a Streamlit-based user interface to input Python code via an input box and receive an Autogen agent's explanation, debugging tips, and optimization suggestions, using a single agent.

This code walkthrough demonstrates a streamlit-powered frontend that analyzes user-provided python code, explains it, and offers optimization suggestions using an agent-based code expert workflow.

Learn how a team of specialized agents, writer, summariser, and reviewer, collaborates to tackle complex tasks by assigning goals and routing actions via a planner.

Explore available group chat teams in AutoGen, including base, round robin, sequential writer and reviewer, selector group chat, swarm, and magnetic one group chat orchestrated by the magnetic one orchestrator.

Run a round robin group chat with two agents sharing context and taking turns; draft creates the blog post and the enhancement optimizes for readability and seo, ending with final.

Learn how to involve humans in the loop for a multi-agent team by using a user proxy agent or post-run feedback, and persist state across sessions.

Learn how termination conditions govern multi-agent teams, using max message termination condition and text message termination condition to stop at the right moment and prevent output issues.

Showcase running a selector group chat with a custom selector function to coordinate weather and currency agents, using tool calls and planning agent for task termination.

Explore how to set up a FastAPI backed multi-agent chatbot by building a lightweight uvicorn server, exposing a chat endpoint, and routing queries through a backend function.

Explore backend architecture in teams.py that configures an agent team, defines product and order databases, and deploys tools for product inquiry, order placement, order status, and complaint registration.

Discover how the planning agent identifies requests, delegates tasks to product inquiry, order placement, order status, and complaint agents, and how termination conditions and the response agent format final replies.

Test a multi-agent chat portal by running streamlit and uvicorn, place a guest order for iPhone 15 Pro, retrieve price and stock, and validate agent-to-tool coordination for production-ready projects.

Explore magnetic one, a multi-agent system with web server, file server, coder, and terminal agents that read content and summarize quantum computing papers via a multimodal web server.