Imagine a customer calls your support line about a broken part on a new coffee machine. They spend five frustrating minutes trying to describe the specific plastic piece that snapped. “It’s the little black lever,” they say, “the one next to the silver thingy.” Your voice bot, as smart as it is, can only process words. It can’t see what the customer sees, leading to a dead end and an escalation to a human agent.
Now, imagine a different scenario. The voice bot says, “I’m having trouble visualizing that. Can you send me a picture of the part?” This is the power of multimodal AI agents. They represent the next evolution of conversational AI, moving beyond the limits of a single sense. These agents don’t just hear; they can see, read, and understand context from multiple sources at once.
For developers, building AI agents with multimodal models is the new frontier. It’s about creating richer, more helpful, and more human-like interactions. This guide will explore the top tools you can use in 2025 to build these next-generation voice agents.
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What Are Multimodal AI Agents?
In the simplest terms, a multimodal AI agent is a system that can understand and process information from more than one type of data (a “modality”) at the same time. While a traditional voice bot is unimodal (it only understands voice/audio), a multimodal agent can combine voice with other inputs like images, text, and even video.
This ability to process multiple data streams at once gives the agent a much deeper and more accurate understanding of the user’s situation.
Also Read: Which TTS And STT Combos Work Best For Call Centers?
Unimodal vs. Multimodal: A Quick Comparison
| Feature | Unimodal Voice Bot | Multimodal AI Agent |
| Input | Audio/Voice Only | Audio, Text, Images, Video, etc. |
| Understanding | Limited to what the user can describe. | Can understand complex situations by seeing and reading. |
| Problem Solving | Good for straightforward, verbal tasks. | Excellent for complex, real-world problems. |
| User Experience | Can be limiting and sometimes frustrating. | More intuitive, faster, and more helpful. |
This shift is what makes building AI agents with multimodal models so transformative. It’s about giving your AI eyes as well as ears.
Top 7 Tools For Building Multimodal AI Voice Agents
As this technology has matured, a powerful ecosystem of tools has emerged to help developers create their own multimodal solutions. Here are seven of the best tools to get you started on your journey.
OpenAI GPT-4o API
GPT-4o (the “o” stands for “omni”) is OpenAI’s flagship model and a true multimodal powerhouse. It was designed from the ground up to natively accept and process a combination of text, audio, and image inputs and generate responses in text and audio. Its real-time processing capabilities make it a top choice for live, interactive agents.
Key Features
- Native Multimodality: It doesn’t just translate images to text; it reasons across different data types simultaneously.
- Real-Time Audio Processing: Can handle conversational turn-taking with incredibly low latency, essential for a voice interface.
- Advanced Vision Capabilities: Can understand charts, read text in images, and analyze scenes with remarkable accuracy.
Best for: Developers who want a state-of-the-art, all-in-one model for building highly responsive multimodal AI agents.
Also Read: Top 8 Voice APIs For Realtime Conversational AI
Google Gemini API
Google’s Gemini family of models was also built from the ground up to be multimodal. The Gemini API allows developers to send prompts that include a mix of text, images, and video, making it incredibly versatile for a wide range of applications.
Key Features
- Excellent Cross-Modal Reasoning: Can find specific information within long documents or videos.
- Scalable and Versatile: Comes in different sizes (from Ultra to Nano), allowing you to choose the right balance of power and efficiency for your application.
- Deep Integration with Google Cloud: Easily connects with the entire Google Cloud ecosystem.
Best for: Developers looking for a powerful and flexible multimodal model, especially those already working within the Google Cloud Platform.
LangChain
LangChain is not an AI model but an essential open-source framework for building applications with language models. For building AI agents with multimodal models, LangChain acts as the “glue.” It provides the tools to chain different models and data sources together into a single, cohesive application.
Key Features
- Agent Framework: Provides powerful tools for building agents that can use other tools (like a calculator, a search engine, or another API).
- Multi-Modal Support: Easily integrate vision models, text models, and other data sources into a single workflow.
- Strong Community and Integrations: A massive ecosystem of integrations with hundreds of different models and services.
Best for: Developers who need to orchestrate multiple models and tools to create a complex, autonomous agent.
LlamaIndex
Similar to LangChain, LlamaIndex is an open-source framework, but it specializes in connecting your LLMs to your data. When building a multimodal agent, you often need it to reason over a knowledge base that contains images, PDFs, and text. LlamaIndex excels at this.
Key Features
- Advanced RAG (Retrieval-Augmented Generation): The go-to tool for building applications that need to retrieve and reason over large, complex datasets.
- Multi-Modal Data Connectors: Provides tools for ingesting and indexing different data types, including images and text, so your agent can search across them.
Best for: Building data-centric multimodal AI agents that need to act as an expert on a specific set of documents or images.
Also Read: What Is Low-Latency Voice Streaming For AI Agents?
Hugging Face Transformers & Agents
Hugging Face is the heart of the open-source AI community. Their transformers library provides access to thousands of pre-trained models, including many powerful multimodal models. Their Agents library allows you to easily combine these models to perform tasks.
Key Features
- Vast Model Hub: Access to a huge variety of open-source multimodal models.
- Complete Control: Host the models yourself for maximum privacy and customization.
- Community-Driven: Leverage the latest research and models as soon as they become available.
Best for: Developers who want to use open-source models to have complete control over their stack and avoid vendor lock-in.
Roboflow
For your multimodal agent to “see,” it sometimes needs more than a general-purpose vision model. It might need to recognize your specific products, parts, or documents. Roboflow is a platform that makes it easy to build and deploy custom computer vision models.
Key Features
- End-to-End Computer Vision Platform: Tools for annotating data, training models, and deploying them via an API.
- Easy Integration: You can train a custom model in Roboflow and then have your LangChain agent call it as a tool.
Best for: Developers who need their agent to have specialized vision capabilities, like identifying a specific product model or a defect on a part.
Microsoft Azure AI Vision
Microsoft offers a comprehensive suite of AI services, and its Azure AI Vision service is a key component for building AI agents with multimodal models. It provides a set of powerful, pre-built APIs for image and video analysis.
Key Features
- Rich Pre-built Capabilities: Offers ready-to-use APIs for optical character recognition (OCR), object detection, and image analysis.
- Enterprise-Grade: Backed by Microsoft’s robust, secure, and compliant cloud infrastructure.
Best for: Enterprises building on the Azure platform that need to quickly add powerful, pre-trained vision capabilities to their applications.
Conclusion
The tools to build incredible multimodal AI agents are here. Platforms like OpenAI, Google, and a vibrant open-source ecosystem have given developers the “brains” and “senses” needed to create applications that can see, hear, and understand in a profoundly new way. By mastering these tools, you can start building AI agents with multimodal models that solve real-world problems more effectively than ever before.
However, all these powerful senses are useless if they can’t communicate in real time. The entire experience hinges on the ability to transmit voice, images, and data instantly. Any lag or delay destroys the conversational flow. This is where the underlying infrastructure becomes the most critical piece of the puzzle.
A specialized platform like FreJun Teler provides the high-performance “plumbing” designed for the intense demands of real-time communication. We ensure your agent’s voice stream is crystal clear and instant, providing the reliable foundation needed to make your multimodal conversations a reality.
Unlock Teler’s power with a demo.
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Frequently Asked Questions (FAQs)
Omnichannel refers to providing a consistent user experience across different communication channels (e.g., phone, text, web chat). Multimodal refers to understanding different data types (voice, image, text) within a single interaction or channel.
One of the biggest challenges is orchestrating all the different models and data streams in real-time. Ensuring low latency is critical, as any delay between the different modalities (e.g., the voice and the image analysis) can lead to a confusing user experience.
No, not anymore. APIs from Google and OpenAI, and frameworks like LangChain, handle much of the underlying complexity. A developer with strong API integration skills can now build a powerful multimodal agent without needing a Ph.D. in AI.
The top proprietary models still tend to have a performance edge in general reasoning. However, open-source models are catching up quickly and can be fine-tuned on your specific data to achieve superior performance for a particular task.
On a phone call, the primary channel is voice. The agent might direct the user to a web page or use MMS to send and receive images. The core voice interaction is managed by a voice infrastructure platform, which then streams the audio to the AI models and coordinates with the other data streams.