TaskWeaver: A Code-First Agent Framework

• URL: http://arxiv.org/abs/2311.17541v3
• Date: Thu, 20 Jun 2024 02:53:20 GMT
• Title: TaskWeaver: A Code-First Agent Framework
• Authors: Bo Qiao, Liqun Li, Xu Zhang, Shilin He, Yu Kang, Chaoyun Zhang, Fangkai Yang, Hang Dong, Jue Zhang, Lu Wang, Minghua Ma, Pu Zhao, Si Qin, Xiaoting Qin, Chao Du, Yong Xu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang,
• Abstract summary: TaskWeaver is a code-first framework for building LLM-powered autonomous agents. It converts user requests into executable code and treats user-defined plugins as callable functions. It provides support for rich data structures, flexible plugin usage, and dynamic plugin selection.
• Score: 50.99683051759488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have shown impressive abilities in natural language understanding and generation, leading to their widespread use in applications such as chatbots and virtual assistants. However, existing LLM frameworks face limitations in handling domain-specific data analytics tasks with rich data structures. Moreover, they struggle with flexibility to meet diverse user requirements. To address these issues, TaskWeaver is proposed as a code-first framework for building LLM-powered autonomous agents. It converts user requests into executable code and treats user-defined plugins as callable functions. TaskWeaver provides support for rich data structures, flexible plugin usage, and dynamic plugin selection, and leverages LLM coding capabilities for complex logic. It also incorporates domain-specific knowledge through examples and ensures the secure execution of generated code. TaskWeaver offers a powerful and flexible framework for creating intelligent conversational agents that can handle complex tasks and adapt to domain-specific scenarios. The code is open sourced at https://github.com/microsoft/TaskWeaver/.

Related papers

• Codellm-Devkit: A Framework for Contextualizing Code LLMs with Program Analysis Insights [9.414198519543564]
  We present codellm-devkit (hereafter, CLDK'), an open-source library that significantly simplifies the process of performing program analysis. CLDK offers developers an intuitive and user-friendly interface, making it incredibly easy to provide rich program analysis context to code LLMs.
  arXiv  Detail & Related papers  (2024-10-16T20:05:59Z)
• The Compressor-Retriever Architecture for Language Model OS [20.56093501980724]
  This paper explores the concept of using a language model as the core component of an operating system (OS) A key challenge in realizing such an LM OS is managing the life-long context and ensuring statefulness across sessions. We introduce compressor-retriever, a model-agnostic architecture designed for life-long context management.
  arXiv  Detail & Related papers  (2024-09-02T23:28:15Z)
• HELPER-X: A Unified Instructable Embodied Agent to Tackle Four Interactive Vision-Language Domains with Memory-Augmented Language Models [13.963676467274109]
  We extend the capabilities of HELPER by expanding its memory with a wider array of examples and prompts. This simple expansion of HELPER into a shared memory enables the agent to work across domains executing plans from dialogue, natural language instruction, active question asking, and common room reorganization. We evaluate the agent on four diverse interactive visual-language embodied agent: AChRED, TEA, DialFRED, and the Tidy Task.
  arXiv  Detail & Related papers  (2024-04-29T19:12:42Z)
• On the Multi-turn Instruction Following for Conversational Web Agents [83.51251174629084]
  We introduce a new task of Conversational Web Navigation, which necessitates sophisticated interactions that span multiple turns with both the users and the environment. We propose a novel framework, named self-reflective memory-augmented planning (Self-MAP), which employs memory utilization and self-reflection techniques.
  arXiv  Detail & Related papers  (2024-02-23T02:18:12Z)
• Executable Code Actions Elicit Better LLM Agents [76.95566120678787]
  This work proposes to use Python code to consolidate Large Language Model (LLM) agents' actions into a unified action space (CodeAct) integrated with a Python interpreter, CodeAct can execute code actions and dynamically revise prior actions or emit new actions upon new observations through multi-turn interactions. The encouraging performance of CodeAct motivates us to build an open-source LLM agent that interacts with environments by executing interpretable code and collaborates with users using natural language.
  arXiv  Detail & Related papers  (2024-02-01T21:38:58Z)
• If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents [81.60906807941188]
  Large language models (LLMs) are trained on a combination of natural language and formal language (code) Code translates high-level goals into executable steps, featuring standard syntax, logical consistency, abstraction, and modularity.
  arXiv  Detail & Related papers  (2024-01-01T16:51:20Z)
• CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets [75.64181719386497]
  We present CRAFT, a tool creation and retrieval framework for large language models (LLMs) It creates toolsets specifically curated for the tasks and equips LLMs with a component that retrieves tools from these sets to enhance their capability to solve complex tasks. Our method is designed to be flexible and offers a plug-and-play approach to adapt off-the-shelf LLMs to unseen domains and modalities, without any finetuning.
  arXiv  Detail & Related papers  (2023-09-29T17:40:26Z)
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation [61.455159391215915]
  AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools.
  arXiv  Detail & Related papers  (2023-08-16T05:57:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.