# MindFlow: Revolutionizing E-commerce Customer Support with Multimodal LLM Agents

arXiv: https://arxiv.org/abs/2507.05330 · HTML: https://mlsanity.com/paper/2507.05330/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 9 of 9)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains](https://mlsanity.com/paper/2406.12045/) (2024)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [eCeLLM: Generalizing Large Language Models for E-commerce from Large-scale, High-quality Instruction Data](https://mlsanity.com/paper/2402.08831/) (2024)
- [A survey on fairness of large language models in e-commerce: progress, application, and challenge](https://mlsanity.com/paper/2405.13025/) (2024)
- [LiLiuM: eBay's Large Language Models for e-commerce](https://mlsanity.com/paper/2406.12023/) (2024)

## Built on by (most cited first, 6 of 6)

- [ShopSimulator: Evaluating and Exploring RL-Driven LLM Agent for Shopping Assistants](https://mlsanity.com/paper/2601.18225/) (2026)
- [SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue](https://mlsanity.com/paper/2602.03548/) (2026)
- [Survey of Specialized Large Language Model](https://mlsanity.com/paper/2508.19667/) (2025)
- [ProductResearch: Training E-Commerce Deep Research Agents via Multi-Agent Synthetic Trajectory Distillation](https://mlsanity.com/paper/2602.23716/) (2026)
- [Reinforcing Real-world Service Agents: Balancing Utility and Cost in Task-oriented Dialogue](https://mlsanity.com/paper/2602.22697/) (2026)
- [MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems](https://mlsanity.com/paper/2606.30602/) (2026)

## Abstract (arXiv)

Recent advances in large language models (LLMs) have enabled new applications in e-commerce customer service. However, their capabilities remain constrained in complex, multimodal scenarios. We present MindFlow, the first open-source multimodal LLM agent tailored for e-commerce. Built on the CoALA framework, it integrates memory, decision-making, and action modules, and adopts a modular "MLLM-as-Tool" strategy for effect visual-textual reasoning. Evaluated via online A/B testing and simulation-based ablation, MindFlow demonstrates substantial gains in handling complex queries, improving user satisfaction, and reducing operational costs, with a 93.53% relative improvement observed in real-world deployments.