Multimodal agents tutorial: How to use Gemini, Langchain, and LangGraph to build agents for object detection

Building AI agents that can detect objects is crucial for various use cases, including content moderation, multimedia search, and retrieval. LangChain and LangGraph are open-source frameworks that can be used to create multimodal agents that can identify objects. To build such agents, one needs to make three key decisions: whether to use no-code/low-code options or custom agents, which agentic framework to use, and where to deploy the agents. For simple agents, no-code/low-code options like Google's Vertex AI Agent Builder can be used, but for more complex use cases, custom agents are required. LangChain and LangGraph can be used as an agentic framework, along with Gemini 2.0 Flash as the LLM brain. An example code demonstrates how to identify an object in an image, audio file, and video using different agents working together. The generative AI workflow for object detection involves an orchestrator agent calling worker agents, which call respective tooling to analyze files and pass the findings back to the orchestrator agent. The final determination is made by the orchestrator agent after synthesizing the findings. The agents can be deployed on Cloud Run for simple apps or on Agent Engine for more enterprise-grade managed runtime. To get started, developers can use the ADK Quickstart or visit the Agent Development GitHub.