Build multimodal agents using Gemini, Langchain, and LangGraph

First decision: No-code/low-code, or custom agents?

The first decision enterprises have to decide is:  no-code/low-code options or build custom agents? If you are building a simple agent like a customer service chat bot, you can use Google’s Vertex AI Agent Builder to build a simple agent in a few minutes or start from pre-built agents that are available in Google Agentspace Agent Gallery. 

But if your use case requires  orchestration of multiple agents and integration with custom tooling, you would have to build custom agents which leads to the next question. 

Second decision: What agentic framework to use?

It’s hard to keep up with so many agentic frameworks out there releasing new features every week. Top contenders include CrewAI, Autogen, LangGraph and Google’s ADK. Some of them, like ADK and CrewAI, have higher levels of abstraction while others like LangGraph allow higher degree of control. 

That’s why in this blog, we center the discussion on building a custom agent using the open-sourced LangChain, LangGraph as an agentic framework, and Gemini 2.0 Flash as the LLM brain. 

Code deep dive

This example code identifies an object in an image, in an audio file, and in a video. In this case we will use a dog as the object to be identified. We have different agents (image analysis agent, audio analysis agent, and a video analysis agent) performing different tasks but all working together towards a common goal, object identification.