A new guide demonstrates how to combine Microsoft AutoGen and Google’s free Gemini API via LiteLLM to produce a multi-agent conversational AI engine in Google Colab. This approach builds on distinct agent roles—for research, business analysis and software development—and supports live interaction among modules powered by large language models. The framework can handle multi-step workflows with minimal human input by assigning tasks, collecting findings and executing code automatically.
Setting up the environment requires installation of the autogen, litellm and google-generative-ai Python packages. After library installation, the script imports core modules and configures a logging system to track requests and performance metrics. API credentials and Colab settings sit in the top section of the notebook so that developers can adjust model parameters, prompt templates and resource limits as needed.
At the heart of the system is the GeminiAutoGenFramework class, which defines methods for model initialization, agent creation and conversation management. Within the constructor, the class loads the Gemini model, applies AutoGen wrappers and sets timeouts. It then spins up three agents—one for research, another for business analysis and a third for software engineering—each driven by specialized prompt templates. A group chat interface passes messages among them in an orchestrated loop.
The research agent scrapes background data and synthesizes insights, feeding results to the analysis agent. The business agent examines scenarios, builds table-based summaries and flags potential risks. The development agent drafts code snippets, runs simple test cases and returns logs to the group. This modular structure lets each AI subunit focus on its area as the collective network conducts collaborative problem solving.
A demonstration function initializes the class, outputs system metrics such as memory consumption and API call counts, then runs three end-to-end workflows: a literature review, a market analysis report and a prototype application build. Each workflow runs inside Google Colab, providing a sandboxed environment that can scale up with GPU or TPU configurations. Logs remain visible in real time, giving observers a clear view of agent decisions and intermediate results.
The resulting framework can carry out in-depth research, generate business deliverables and assemble software prototypes with limited human oversight. It illustrates how blending Microsoft AutoGen’s structured orchestration with a free LLM service and a light coordination layer delivers practical multi-agent capabilities. This example aims to help data scientists, consultants and developers adapt similar schemes for domains like academic research, financial modeling, product design and more.
Asif Razzaq is the CEO of Marktechpost Media Inc. A veteran entrepreneur and engineer, he directs the Marktechpost AI Media Platform, which offers technically sound coverage of machine learning and deep learning news in clear, accessible language. The platform attracts over 2 million monthly readers, showing strong engagement from a broad audience interested in AI trends and insights.