# FaMA: LLM-Empowered Agentic Assistant for Consumer-to-Consumer Marketplace

arXiv: https://arxiv.org/abs/2509.03890 · HTML: https://mlsanity.com/paper/2509.03890/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 14 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 11 of 11)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [RecMind: Large Language Model Powered Agent For Recommendation](https://mlsanity.com/paper/2308.14296/) (2023)
- [Comparing Speech and Keyboard Text Entry for Short Messages in Two Languages on Touchscreen Phones](https://mlsanity.com/paper/1608.07323/) (2016)
- [IPL: Leveraging Multimodal Large Language Models for Intelligent Product Listing](https://mlsanity.com/paper/2410.16977/) (2024)
- [FishBargain: An LLM-Empowered Bargaining Agent for Online Fleamarket Platform Sellers](https://mlsanity.com/paper/2502.10406/) (2025)

## Built on by (most cited first, 4 of 4)

- [Agentic Metacognition: Designing a "Self-Aware" Low-Code Agent for Failure Prediction and Human Handoff](https://mlsanity.com/paper/2509.19783/) (2025)
- [Memory Management and Contextual Consistency for Long-Running Low-Code Agents](https://mlsanity.com/paper/2509.25250/) (2025)
- [Toward Causal-Visual Programming: Enhancing Agentic Reasoning in Low-Code Environments](https://mlsanity.com/paper/2509.25282/) (2025)
- [A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks](https://mlsanity.com/paper/2605.28556/) (2026)

## Abstract (arXiv)

The emergence of agentic AI, powered by Large Language Models (LLMs), marks a paradigm shift from reactive generative systems to proactive, goal-oriented autonomous agents capable of sophisticated planning, memory, and tool use. This evolution presents a novel opportunity to address long-standing challenges in complex digital environments. Core tasks on Consumer-to-Consumer (C2C) e-commerce platforms often require users to navigate complex Graphical User Interfaces (GUIs), making the experience time-consuming for both buyers and sellers. This paper introduces a novel approach to simplify these interactions through an LLM-powered agentic assistant. This agent functions as a new, conversational entry point to the marketplace, shifting the primary interaction model from a complex GUI to an intuitive AI agent. By interpreting natural language commands, the agent automates key high-friction workflows. For sellers, this includes simplified updating and renewal of listings, and the ability to send bulk messages. For buyers, the agent facilitates a more efficient product discovery process through conversational search. We present the architecture for Facebook Marketplace Assistant (FaMA), arguing that this agentic, conversational paradigm provides a lightweight and more accessible alternative to traditional app interfaces, allowing users to manage their marketplace activities with greater efficiency. Experiments show FaMA achieves a 98% task success rate on solving complex tasks on the marketplace and enables up to a 2x speedup on interaction time.