DataClaw: An Autonomous Data Agent with Instant Messaging Integration

2026-04-27

AI Summary

This work addresses the high barrier non-technical users face when performing routine data tasks—such as form filling, analysis, and visualization—that typically require switching across multiple tools. The authors propose an autonomous data agent embedded within an instant messaging (IM) platform, enabling users to initiate end-to-end data processing workflows through natural language commands alone. The system innovatively integrates a transparent ReAct reasoning mechanism, cross-session memory, and a plug-in-based skill architecture to achieve accessible and efficient automation of data tasks. Experimental results demonstrate that users can directly obtain insights, visualizations, and reports within real-world IM environments, substantially reducing the complexity of data manipulation.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Intelligent User Interfaces
• Natural Language Processing: Conversational AI/Dialog Systems

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

In daily life, there are many scenarios that people need to tackle data-related tasks, such as filling out forms, analyzing Excel files, and visualize data report. However, the tools available for these tasks often fragment, requiring users to switch between multiple applications and manually orchestrate steps like data processing, querying, and visualization. Moreover, these tools often assume a certain level of technical proficiency, creating barriers for non-technical users. To facilitate tacking daily data task, we present DataClaw, an autonomous data agent that integrates directly into familiar instant messaging (IM) platforms. By simply typing a natural language request in a chat interface, users enable DataClaw to autonomously plan and execute a complete analytical pipeline, delivering insights, charts, and reports directly back into the conversation. Under the hood, DataClaw is powered by a transparent ReAct reasoning engine, a multi-tiered memory system for cross session context preservation, and a pluggable skill architecture for on-the-fly extensibility. In this demonstration, attendees will interact with DataClaw via standard IM platforms to solve real-world data scenarios, experiencing how it serves as a highly capable personal data assistant.

Problem

• data tasks
• tool fragmentation
• non-technical users
• instant messaging integration

Innovation

• autonomous data agent
• instant messaging integration
• natural language interface
• ReAct reasoning
• pluggable skill architecture