Microsoft’s AutoGen library lets dev teams coordinate multiple AI agents with just a few lines of code. By pairing RoundRobinGroupChat with the TeamTool pattern, you bring together dedicated assistants—Researcher, FactChecker, Critic, Summarizer and Editor—into a single DeepDive tool. AutoGen handles message turns, stopping rules and live output, so you set each assistant’s role and system prompt instead of writing callback functions or chained prompts. Whether you need deep research, accuracy checks, stylistic feedback, concise summaries or language polishing, and even integration with external services, AutoGen’s unified API covers everything from simple two-agent setups to full five-agent teams.
This pattern excels for content teams and developers who need consistent reports without building custom orchestration. With one API call, you spin up entire analysis pipelines that handle discovery, validation, critique and polishing, all in one go.
You begin by installing three Python packages in your Colab notebook: the AutoGen AgentChat module with Gemini support, the OpenAI extension for API alignment and nest_asyncio to patch the event loop. Then you apply nest_asyncio.patch() so Jupyter can run nested async loops. After that, use getpass.getpass() to enter your Gemini key and store it in os.environ for safe access. All packages install via pip in seconds, and version conflicts rarely arise within a single notebook.
Next, set up an OpenAI-compatible chat client directed at Google’s Gemini model gemini-1.5-flash-8b. Pass through your stored API token, specify api_type="google" and you have a model_client ready for all downstream agents. You can also point the client at other endpoints by updating the model name and api_type, allowing use of OpenAI’s GPT services or custom LLMs.
You then define five assistant agents. Each gets a system message that matches its specialty and the same Gemini-based model_client to carry out its task: Researcher gathers raw data, FactChecker verifies info, Critic offers feedback, Summarizer condenses text and Editor polishes final language. Prompts adopt a structured format such as “You are a Researcher tasked with gathering relevant data.” Consistent phrasing helps each assistant behave predictably as they pass work between them.
After that, import RoundRobinGroupChat along with two stopping conditions. Build a rule that ends the session once 20 messages have passed or when the Editor agent outputs “APPROVED.” Pass that rule to RoundRobinGroupChat together with your five agents, setting up an ordered cycle of research, fact checking, critique, summarization and editing that halts when any stop rule fires.
Wrap this chat team in a TeamTool called DeepDive and give it a clear description. Once you do, DeepDive becomes a single callable component that other assistants can invoke.
Then configure a Host agent that also uses the Gemini-powered model_client. Grant it access to the DeepDive tool and provide a system prompt explaining how to trigger DeepDive. The Host agent acts as a single entry point for user prompts, hiding the complexity of a multi-agent sequence.
Write an asynchronous run_deepdive function that tells the Host to run DeepDive on any chosen topic, prints the result and calls model_client.close() when finished. Finally, grab the notebook’s existing asyncio loop and run that coroutine to let your workflow complete from start to finish in one go.
By combining Google’s Gemini via an OpenAI-compatible client and packaging your multi-agent setup as a single TeamTool, developers get a modular template for reusable workflows. AutoGen takes care of async loop management with nest_asyncio, live streaming and stop logic, freeing teams to adjust agent roles and overall flow in minutes. This approach simplifies building systems where multiple AI services work together and paves the way for adding document retrieval, dynamic agent selectors based on context or branching execution flows.
Teams of LLM agents are under study for tasks requiring joint reasoning and divided expertise. At the same time, businesses adding voice-based AI helpers need evaluation suites that measure both response time and successful completion. Many benchmarks still emphasize speed rather than accuracy.
Chain-of-thought reasoning boosts model performance but often demands heavy compute and can repeat unnecessary steps. Advances in long-context inputs let both pure LMs and vision-language systems handle larger dialogues and multi-page documents more effectively, reducing overhead and improving comprehension. These designs also help maintain coherence across sections.
Modern AI must cover tasks like form entry, account management, data queries and dashboard access and balance compute costs and adaptability, with transformers leading for their speed and flexibility.
Models built for reasoning, such as OpenAI’s o1 and o3, DeepSeek-R1, Grok 3.5 and Gemini 2.5 Pro, are delivering impressive outcomes on extended chains-of-thought benchmarks.
Anthropic released Claude Opus 4 and Claude Sonnet 4, each offering refinements in training strategies and parameter efficiency.
Mixed-input math reasoning now lets systems solve problems with text and visuals—equations, charts and diagrams—in a single workflow.
Work is underway to shrink model footprints so AI performs efficiently on phones, tablets and laptops and protect user data privacy.