====== Tool Use for LLM Agents ======

===== Introduction =====
Tool use is a critical capability for Large Language Model (LLM) agents, enabling them to interact with external systems, access up-to-date information, and perform actions beyond their inherent knowledge. This functionality allows LLMs to handle complex tasks that require real-time data retrieval or specific operations.

===== Frameworks and Libraries =====

==== LangChain ====
  * **Website**: [[https://www.langchain.com/]]
  * **GitHub**: [[https://github.com/langchain-ai/langchain]]
  * **Features**:
    * Extensive toolkit for agent-tool interactions
    * Pre-built tools for tasks such as web search, mathematical computations, and code execution
    * Capabilities for creating custom tools

==== AutoGen ====
  * **GitHub**: [[https://github.com/microsoft/autogen]]
  * **Features**:
    * Flexible tool integration within multi-agent systems
    * Support for code execution and external API calls

==== LlamaIndex ====
  * **Website**: [[https://www.llamaindex.ai/]]
  * **GitHub**: [[https://github.com/jerryjliu/llama_index]]
  * **Features**:
    * Data connectors and structuring for tool creation
    * Advanced retrieval interfaces for data-centric tools

==== Haystack ====
  * **Website**: [[https://haystack.deepset.ai/]]
  * **GitHub**: [[https://github.com/deepset-ai/haystack]]
  * **Features**:
    * SearchEngine tool for information retrieval
    * TopPSampler for text generation tasks

==== BMTools ====
  * **GitHub**: [[https://github.com/OpenBMB/BMTools]]
  * **Features**:
    * Platform for building and sharing tools
    * Extends language models with various capabilities

===== Types of Tools =====

  * **Web search tools**
  * **Mathematical computation tools** (e.g., WolframAlpha)
  * **Code execution tools**
  * **Database query tools** (e.g., SQL, CSV, JSON)
  * **API interaction tools**
  * **Vector store tools** for efficient data retrieval
  * **Image analysis tools**

===== Tool Integration Approaches =====

==== Function Calling ====
  * Allows agents to select and use appropriate tools based on task requirements
  * Supports dynamic tool selection and parameter passing

==== Retrieval-Augmented Generation (RAG) ====
  * Enhances tool use by providing relevant context from external data sources
  * Improves the accuracy and relevance of tool outputs

==== Tool-Augmented Language Models ====
  * Models like Toolformer and TALM are fine-tuned for tool interactions
  * Exhibit enhanced ability to use external APIs and tools effectively

===== Challenges in Tool Use =====

  * **Quality and Availability of Tool Documentation**: Diverse, redundant, or incomplete documentation can hinder effective tool utilization.
  * **Decision-Making in Tool Use**: LLMs may struggle to determine when and which tools to use, affecting performance.

===== Recent Advancements =====

  * **ToolLLM**: An open platform that enables LLMs to master thousands of real-world APIs, improving their ability to execute complex instructions and generalize to unseen APIs. [[https://arxiv.org/abs/2307.16789]]
  * **GPT4Tools**: A framework that allows open-source LLMs to use multimodal tools through self-instruction, enhancing their problem-solving capabilities. [[https://arxiv.org/abs/2305.18752]]

===== Conclusion =====
Effective tool use significantly expands the capabilities of LLM agents, allowing them to perform complex tasks and interact seamlessly with external systems. The frameworks and approaches mentioned above provide various methods to implement tool use in LLM-based applications, empowering developers to create more powerful and versatile AI agents.