Uploaded August 2025 | Updated September 2026, 1 week ago
A quick explainer on multi-agent patterns and why they are useful.
Multi-agent AI refers to systems where multiple specialized agents, often powered by large language models (LLMs), collaborate to solve complex tasks. This approach is gaining traction across engineering, healthcare, and enterprise AI because it offers modularity, scalability, and precision.
Try out these samples:
OpenAI Swarm with Python: github.com/AzureCosmosDB/multi-agent-swarm
LangGraph (Python) and Semantic Kernel (C#): github.com/AzureCosmosDB/banking-multi-agent-workshop
Spring AI (Java): github.com/AzureCosmosDB/multi-agent-spring-ai
#azurecosmosdb #AI
A quick explainer on multi-agent patterns and why they are useful.
Multi-agent AI refers to systems where multiple specialized agents, often powered by large language models (LLMs), collaborate to solve complex tasks. This approach is gaining traction across engineering, healthcare, and enterprise AI because it offers modularity, scalability, and precision.
Try out these samples:
OpenAI Swarm with Python: github.com/AzureCosmosDB/multi-agent-swarm
LangGraph (Python) and Semantic Kernel (C#): github.com/AzureCosmosDB/banking-multi-agent-workshop
Spring AI (Java): github.com/AzureCosmosDB/multi-agent-spring-ai
#azurecosmosdb #AI




![Multi-Agent API with LangGraph and Azure Cosmos DB
The rise of multi-agent AI applications is transforming how we build intelligent systems - but how do you architect them for real-world scalability and performance? In this session, we’ll take a deep dive into a production-grade multi-agent application built with LangGraph for agent orchestration, FastAPI for an API layer, and Azure Cosmos DB as the backbone for state management, vector storage, and transactional data.
Through a detailed code walkthrough, you’ll see how to design and implement an agent-driven workflow that seamlessly integrates retrieval-augmented generation (RAG), memory persistence, and dynamic state transitions. We’ll cover:
- Agent collaboration with LangGraph for structured reasoning
- Real-time chat history storage using Azure Cosmos DB - the same database that powers the chat history in ChatGPT, the fastest-growing AI agent application in history
- Vector search for knowledge retrieval with Cosmos DBs native embeddings support
- FastAPI’s async capabilities to keep interactions responsive and scalable
By the end of this session, you’ll have a clear blueprint for building and deploying your own scalable, cloud-native multi-agent applications that harness the power of modern AI and cloud infrastructure. Whether youre an AI engineer, cloud architect, or Python developer, this talk will equip you with practical insights and battle-tested patterns to build the next generation of AI-powered applications
#MicrosoftReactor #learnconnectbuild #AgentHack
📌 Learn more about the series here: https://aka.ms/AgentHack-Py/y
[eventID:25314] Multi-Agent API with LangGraph and Azure Cosmos DB](https://i.ytimg.com/vi/RTYI-j0QVIE/mqdefault.jpg)





