Top 7 Platforms to Quickly Build Multimodal AI Agents in 2025!

TL;DR

• Multimodal AI agents process and respond to inputs like text, images, and audio—making them more human-like and versatile than traditional AI.
  
• LangChain, LangGraph, AutoGen, and CrewAI are top frameworks for developers looking to build powerful, open-source, agentic systems in 2025.
  
• Phidata and Relevance AI offer no-code/low-code solutions for teams that want faster deployment with workflow automation and LLM integration.
  
• Bizway stands out for product and business teams with its intuitive interface and enterprise-ready AI agent builder.
  
• If you’re looking to build custom, scalable AI agents for finance, retail, crypto, or healthcare, a dedicated AI Agent Development Company can help you go beyond plug-and-play solutions.

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Introduction

The era of multimodal AI agents is here. As businesses race to integrate intelligent agents capable of processing text, image, code, audio, and structured data, one thing is clear: building these agents from scratch isn’t scalable. That’s why AI builders, startups, and enterprises are turning to purpose-built platforms that offer ready-to-use frameworks, tool integrations, and agent orchestration layers.

And for businesses seeking tailored solutions beyond plug-and-play tools, partnering with a trusted AI Agent Development Company can ensure enterprise-grade scalability, customization, and performance.

In this blog, we break down the top 7 platforms to quickly build multimodal AI agents in 2025—ranked for their capabilities, use cases, and extensibility.

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What Makes a Platform Ideal for Multimodal Agent Development?

Before jumping in, let’s set a baseline. A good platform for building multimodal AI agents should support:

• Multiple input modalities: text, image, audio, video, structured documents
  
• Tool usage & function calling: via APIs or plugins
  
• Agent orchestration: ability to coordinate memory, planning, decision-making
  
• Extensibility: works with various LLMs (OpenAI, Claude, Gemini, LLaMA, etc.)
  
• Rapid prototyping: low-code or componentized architecture

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Top 7 Platforms to Quickly Build Multimodal AI Agents

From open-source toolkits to enterprise-grade orchestration frameworks, these platforms empower developers to build, manage, and deploy multimodal AI agents at scale. Whether you’re an AI startup, product team, or researcher, here are the top platforms reshaping agentic development in 2025.

1. LangChain – The Foundational Agent Framework

Best for: Developers who want full control over LLM agents
Modality support: Text (native), with plugins for vision/audio via OpenAI GPT-4o

LangChain has quickly become the go-to framework for building LLM-powered agents. While it started with chain-based prompts, it has evolved to support agentic workflows, tool use, and memory modules.

LangChain now supports multimodal capabilities by integrating with models like GPT-4o, Gemini 1.5, and Claude 3—allowing developers to process and reason over text, images, and audio.

Why it works:

• Open architecture with Python/JavaScript SDKs
  
• Tight integration with LangSmith for agent evaluation
  
• Can be embedded in any backend, giving complete ownership
  

Use Case:
Create a medical AI agent that interprets image scans, fetches structured data via tools, and provides a diagnosis explanation—all in one workflow.

2. Microsoft AutoGen – Multi-Agent Conversations Done Right

Best for: Enterprises and researchers building multi-agent systems
Modality support: Text native; vision/audio via model integrations (GPT-4o, Azure OpenAI)

Microsoft’s AutoGen is a powerful open-source framework focused on multi-agent collaboration. It enables agents to talk to each other, delegate tasks, and coordinate results—ideal for agentic systems that go beyond single responses.

Its multimodal capabilities come from integration with Azure OpenAI (GPT-4o), which supports vision, audio, and code.

Why it works:

• Multi-agent orchestration out of the box
  
• Conversable agents with defined roles and memory
  
• Enterprise-friendly with Azure security layers
  

Use Case:
Build a team of agents: one reads legal PDFs, another summarizes content, and a third converts it into an explainer video script.

3. LangGraph – Agent Workflows as Graphs

Best for: Agent developers needing deterministic, state-based flows
Modality support: Text + multimodal support via custom nodes (GPT-4o, Gemini)

LangGraph, developed by the LangChain team, introduces a new paradigm: agents as stateful graphs. Think of it as combining LangChain’s LLM tools with the reliability of workflow engines like Airflow or Prefect.

LangGraph lets you build agents with defined paths, retries, and conditional logic—perfect for production use.

Why it works:

• Graph structure gives more control over agent behavior
  
• Memory + state transitions built-in
  
• Works with any LLM backend (OpenAI, Claude, Anthropic)
  

Use Case:
An HR agent that processes resumes (PDF/image), extracts entities, scores them, and sends personalized interview invites.

4. Phidata – Lightweight Agent Stack with Strong Dev Experience

Best for: Developers and startups building fast prototypes
Modality support: Text-first; GPT-4o integration adds multimodal capabilities

Phidata is a newer but powerful framework focused on developer productivity. It offers a simple SDK to build LLM apps, agents, and tools with minimal overhead. While not as complex as AutoGen or LangGraph, its strength lies in quick development cycles.

Phidata supports tool usage, OpenAI plugins, and GPT-4o—making it capable of handling images and audio with lightweight configuration.

Why it works:

• Minimal setup time
  
• Works well for solo builders and startup teams
  
• Fast local testing and iteration
  

Use Case:
Build a customer support agent that takes screenshots, understands user queries, and fetches relevant KB articles.

5. Relevance AI – Hosted Multimodal Agents with Workflow UI

Best for: Teams who prefer a visual UI and hosted infra
Modality support: Text, image, tables, embeddings

Relevance AI positions itself as “Agents-as-a-Service.” It offers a full-stack agent platform: workflow editor, embeddings, memory, tool integration, and multimodal interfaces.

You can build agents that reason over PDFs, extract structured data from images, or generate multi-turn responses—all from a hosted platform.

Why it works:

• Drag-and-drop agent workflows
  
• Native multimodal input parsing
  
• Dashboard, analytics, and memory management built-in
  

Use Case:
Create an AI analyst that reads reports (PDF), highlights anomalies (charts/images), and sends Slack alerts with next steps.

6. CrewAI – Multi-Agent Collaboration with Role-Based Agents

Best for: Developers needing modular, role-based agents
Modality support: Text-focused; can wrap multimodal tools via GPT-4o or APIs

CrewAI is built around the concept of agent roles—each agent has a defined job and works within a coordinated system. Think of it as a lightweight AutoGen alternative, great for AI teams with specialized agents.

You can extend CrewAI to process images or audio using multimodal models or external APIs, making it flexible for custom agent systems.

Why it works:

• Supports asynchronous agent workflows
  
• Integrates well with custom tools/APIs
  
• Low boilerplate setup
  

Use Case:
An internal task force of agents: one reads email content, one checks attached invoices (images), another updates finance software.

7. Bizway – No-Code Agent Builder with Multimodal Capabilities

Best for: Non-developers and fast enterprise prototyping
Modality support: Text, file uploads (image, PDF), APIs

Bizway is a no-code agent builder designed for business users. It allows creation of AI agents that answer questions, extract data, summarize documents, and more—without writing code.

It supports file input, API integration, and even lets users define custom workflows, making it a go-to for teams that want results fast without hiring developers.

Why it works:

• No-code UI + file processing
  
• Multimodal document understanding
  
• Live deployment in minutes
  

Use Case:
Build a real estate AI assistant that answers queries using uploaded property brochures, price sheets, and market reports.

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Looking for the Most Efficient Multimodal AI Integration Solutions?

Efficiency is no longer a nice-to-have—it’s the difference between experimentation and deployment.

Platforms like Phidata and Relevance AI are geared toward teams looking to skip the infrastructure headaches and launch operational agents fast. They bundle in-built integrations, GUI-based flows, and cloud-ready environments, making them highly suitable for startups and business teams. On the other hand, LangGraph and AutoGen allow deeper control, agent memory, parallelism, and state handling for those who demand flexibility at scale.

If your focus is ROI, not just experimentation, then choosing the most efficient multimodal AI platform means balancing ease of use, extensibility, and the level of agent autonomy required.

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Best Companies Offering AI Workflows with Multimodal Capabilities

While platforms make AI development more accessible, not every organization has the in-house expertise to build, fine-tune, and scale AI agents for real-world use cases. That’s where experienced vendors come in.

Several companies now specialize in AI workflows with multimodal capabilities, offering services such as prompt engineering, API integration, custom pipeline design, and performance tuning across text, image, voice, and structured inputs.

One standout in this space is Creole Studios—a trusted AI Agent Development Company helping businesses across finance, healthcare, retail, and crypto build agent-based solutions that are scalable, secure, and production-ready.

If you’re looking to go beyond prebuilt platforms and build truly custom, enterprise-grade multimodal agents, these companies can handle everything from discovery to deployment.

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Final Thoughts

Multimodal AI agents aren’t a future concept—they’re a present-day competitive advantage. Whether you’re building a cross-functional team of agents or need a single intelligent assistant that can understand charts, text, and audio, these platforms drastically reduce your development effort.
Instead of reinventing the wheel, choose the platform that fits your team’s skill level, modality needs, and deployment requirements. And if you’re looking for custom-built, enterprise-grade solutions beyond what these platforms offer, partnering with experienced AI Agent Development Services can help you turn your vision into a scalable product.

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Frequently Asked Questions (FAQs)

1. What is a multimodal AI agent?
A multimodal AI agent is an intelligent system capable of processing and interacting using multiple input types—such as text, images, voice, and even video. These agents can understand complex contexts and deliver more human-like responses across various tasks.

2. Which is the best platform to build AI agents with minimal coding?
Platforms like Phidata and Relevance AI are ideal for non-developers or business teams. They offer low-code/no-code environments, allowing users to create multimodal AI workflows using simple interfaces and visual builders.

3. Is LangChain suitable for production-grade AI agents?
Yes. LangChain is one of the most mature open-source frameworks for building robust, composable, and scalable AI agents. It offers integrations with tools, memory, and reasoning chains—making it ideal for advanced production use cases.

4. What’s the Best Platform for Multimodal AI Integration in AI Workflows?
It depends on your needs—LangChain and AutoGen are great for developers, while Bizway and Phidata suit low-code teams. Choose based on modality support, integration ease, and scalability.

5. Can I use these platforms to build industry-specific AI agents?
Absolutely. Many of these platforms (e.g., Bizway and LangGraph) are flexible enough to build AI agents tailored for industries like finance, healthcare, ecommerce, and customer support. For more specialized use cases, consider working with an AI Agent Development Company.

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