OpenAgents: An Open Platform for Language Agents in the Wild

Summary

This paper presents OpenAgents, an open-source platform for real-life language agent applications. The authors design three agents: Data Agent, Plugin Agent and Web Agent, and provide interfaces for different types of users (general users, developers, and researchers). The platform's open-source nature may inspire broader community involvement and innovation, advancing the development and application of language agent technologies. Overall, "OpenAgents" represents a high-quality and practically significant work.

Rating

7

Confidence

4

Ethics flag

1

Reasons to accept

1. This presents an open-source implementation of ChatGPT Plus； 2. This paper provides a well-defined architecture to guide the design of the language agent platform. 3. The proposed platform ‘OpenAgents’ is designed to be scalable and flexible, supporting various research needs and adapting to various computational environments.

Reasons to reject

1. This paper doesn’t emphasize the difference between ‘OpenAgents’ and ‘ChatGPT Plus’. 2. Most functionality of ‘OpenAgents’ is based on code or APIs, which makes ‘OpenAgents’ like an assistant rather than an intelligent agent. In more complex scenarios, for example, in OS operation and Household tasks, agents need to consider more.

Questions to authors

1. Compared to LangChain, what specific advantages do ‘OpenAgents’ offer? Additionally, can ‘OpenAgents’ be seamlessly integrated with LangChain? 2. Prompting is a critical part of OpenAgents. Does OpenAgents support a diverse set of prompting strategies, or is it easy for users to add their own?

Ethics concerns details

none

Summary

- This paper introduces OpenAgents, a versatile and open platform designed for the deployment and management of language agents in real-world settings. - OpenAgents consists of three types of agents: the Data Agent, the Plugins Agent, and the Web Agent.

Rating

5

Confidence

4

Ethics flag

1

Reasons to accept

- The OpenAgents platform is engineered to serve a diverse audience, encompassing general users, developers, and researchers. - It provides a wide array of features, including a web user interface, streaming capabilities, and more. - The paper offers an in-depth exploration of agent-based systems, yielding numerous insights that substantially enrich the field. - It details the implementation meticulously, ensuring clarity in the processes involved.

Reasons to reject

- Gathering feedback from actual users is crucial for evaluating the platform's effectiveness and overall user experience. However, the paper does not discuss this aspect. - The performance of the auto-selection feature for plugins has not been evaluated. - In terms of agents' design, there are no significant novel contributions. - It seems that the platform lacks a chat API service capable of handling large volumes of user queries.

Questions to authors

- Do you open-source all 200+ APIs for Plugins Agent?

Summary

The authors propose an open platform OpenAgents for deploying and interacting with language agents in everyday contexts. It addresses the gap in current frameworks by focusing on accessibility for non-expert users and emphasizing application design. The platform comprises three key agents: a Data Agent for handling data analysis tasks using Python/SQL and other tools, a Plugins Agent equipped with over 200 daily utility APIs, and a Web Agent designed for autonomous web navigation.

Rating

6

Confidence

4

Ethics flag

1

Reasons to accept

1. Good illustrations of the functional utilities of the proposed framework. 2. The studied topic is important and appeals to a broad audience. 3. The provision of an open-source codebase, developed with considerable dedication, is both welcomed and appreciated.

Reasons to reject

1. [Writing Style & Technical Depth] The paper presents its content at a too high-level fashion, leaving ambiguities regarding its novelty from a methodological perspective. It fails to clarify the rationale behind the selection and integration of numerous tools, the features to be provided, and what makes the construction of such a platform technically challenging and non-trivial. For instance, the discussion on "tackling the challenge of strategic data storage" in Sec A.1.2 is too brief, offering only two sentences without adequate details. This lack of information does not sufficiently explain the issues related to `data storage` or how the approach is `strategic`. 2. [No Experiments & Analysis] The paper does not adequately justify the proposed platform due to the absence of quantitative verification. The lack of empirical and numerical support makes it difficult for readers to grasp the effectiveness and operational mechanisms of the platform. 3. [Preciseness & Timeliness] Assertions and arguments to current works require careful review and updates, given that LLM-Agent is an extremely active and rapidly evolving field of study. For example, in Table 1, some markers and specific numbers are outdated (gathered by September 2023, whereas the submission deadline for COLM is March 29, 2024), potentially misleading new readers and being unfair to the works being compared. 4. It is recommended to explore additional literature on data agents and multi-agent systems for possible improvements or integration. For instance, a data agent with enhanced data processing capabilities could offer significant benefits [1], and the proposed *three agents* could potentially be enhanced to cooperatively work better through more customized multi-agent workflows [2]. [1] (arXiv’23, SIGMOD’24) Data-Juicer: A One-Stop Data Processing System for Large Language Models [2] (arXiv’24) AgentScope: A Flexible yet Robust Multi-Agent Platform