Multimodal Memory-Augmented Multi-Agent Conversational AI with Multilingual Support

Abstract

Conversational AI technologies have made significant progress in enabling intelligent human–computer interaction; however, many existing systems still struggle with limitations such as weak contextual memory, reliance on a single interaction modality, and limited multilingual communication capabilities. These challenges reduce the ability of conversational systems to sustain meaningful long-term interactions and effectively serve users from diverse linguistic backgrounds. To address these issues, this paper presents a Multimodal Memory-Augmented Multi-Agent Conversational AI with Multilingual Support, designed to enhance conversational intelligence and adaptability. The proposed system integrates multiple input modalities, including text, speech, and images, allowing users to interact with the system in more natural and flexible ways. A contextual memory mechanism is incorporated to retain both short-term and long-term conversational history, enabling the system to generate more coherent, context-aware, and personalized responses during extended interactions. Furthermore, the architecture employs a collaborative multi-agent framework in which specialized agents perform tasks such as language translation, summarization, sentiment analysis, and recommendation generation. By leveraging multimodal processing techniques and multilingual language models, the system supports communication across languages such as English, Hindi, and Telugu. Experimental design and system architecture demonstrate the feasibility of the proposed framework for real-world conversational AI applications.

Introduction

Conversational AI enables natural human–machine interaction through chatbots, virtual assistants, and intelligent platforms. Advances in large language models (LLMs) and deep learning have enhanced conversational fluency and task performance, but key limitations remain:

• Restricted contextual memory: Most systems cannot retain long-term conversation history or user preferences.

• Single-modality input: Predominantly text-based, limiting speech or image interaction.

• Limited multilingual support: Reliance on external translation can reduce accuracy and context.

To address these challenges, the text proposes a Multimodal Memory-Augmented Multi-Agent Conversational AI with Multilingual Support. Key features include:

1. Multimodal interaction – Supports text, speech, and images for richer communication.

2. Memory augmentation – Maintains short-term and long-term context for personalized, coherent responses.

3. Multi-agent framework – Specialized agents handle translation, sentiment detection, summarization, and recommendations, improving scalability and task efficiency.

4. Integrated multilingual support – Directly handles multiple languages (e.g., English, Hindi, Telugu) to reduce translation errors.

This approach aims to enhance intelligence, adaptability, and usability, particularly in domains like education, healthcare, customer service, business analytics, and personal assistants.

Literature Review Highlights

1. JARVIS-1 (Wang et al.) – Multimodal memory-augmented agent framework enabling long-horizon task reasoning and adaptation in open-ended environments (e.g., Minecraft). Highlights the importance of memory for coherent AI interactions.

2. MA-LMM (He et al.) – Memory-augmented model for long-term video understanding, showing efficiency in retaining crucial visual and temporal information for multimodal tasks.

3. Nunchi-aware Multi-Agent Chatbot (Kim & Ko) – Multi-agent system modeling social awareness and context, improving natural, empathetic, and situation-aware responses.

4. ConvoGen (Gody et al.) – Multi-agent framework generating high-quality synthetic conversational data, enhancing lexical diversity and realism for training AI.

5. MACRS (Fang et al.) – Task-oriented multi-agent recommender system with feedback-aware adaptation, improving recommendation quality, coherence, and personalization.

6. Analysis of Multi-Agent LLMs (Becker) – Multi-agent LLMs excel in complex reasoning and collaboration but face challenges like agent drift, alignment collapse, and discussion monopolization, emphasizing careful agent role design.

Conclusion

The Conversational Artificial Intelligence has significantly evolved from simple rule-based chatbots to advanced large language model-based systems. However, existing systems still face limitations such as weak long-term memory, single-mode interaction, lack of structured multi-agent collaboration, and limited direct multilingual support. To address these challenges, this project proposed a Multimodal Memory-Augmented Multi-Agent Conversational AI with Multilingual Support. The system integrates text, speech, and image processing capabilities to enable natural and flexible human–computer interaction. By incorporating both short-term and long-term memory modules, the system enhances contextual understanding and personalization. Furthermore, the multi-agent architecture improves reasoning efficiency by assigning specialized tasks to different agents such as translation, summarization, sentiment analysis, and recommendation. Direct multilingual capability ensures inclusive communication across diverse users. Overall, the proposed system aims to build a smarter, adaptive, scalable, and user-centric conversational AI platform capable of handling complex real-world interactions.

References

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Copyright

Copyright © 2026 Durgunala Ranjith, Abhinaya Jyothi, A. Harsha Vardhan Reddy, J. Chandrahas. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.