Tool Use - joehubert/ai-agent-design-patterns GitHub Wiki

Introduction

Classification

Architectural Pattern

Intent

Enable LLMs to invoke external tools and APIs to extend their capabilities beyond text generation, allowing them to access and manipulate information outside their training data or perform specialized functions they weren't explicitly designed to handle.

Also Known As

Function Calling, API Integration, Tool Augmentation, Capability Extension

Motivation

LLMs are powerful text generators but have inherent limitations:

  • They operate within a fixed knowledge cutoff date
  • They cannot access real-time information
  • They lack the ability to perform specialized calculations or data manipulations
  • They cannot interact with external systems or services directly

When a user asks an LLM to perform tasks requiring current data (like weather forecasts), specialized functions (like complex mathematical calculations), or interactions with external systems (like database queries), the model alone is insufficient.

The Tool Use pattern addresses these limitations by creating a structured framework for LLMs to identify when external tools are needed, format the necessary parameters to invoke these tools correctly, interpret the results, and integrate that information into their response pipeline.

Applicability

Use the Tool Use pattern when:

  • Users need access to information beyond the LLM's knowledge cutoff date
  • Tasks require real-time data (weather, stock prices, news)
  • Complex calculations or specialized processing is needed (statistical analysis, image generation)
  • Interactions with external systems are required (databases, calendars, email)
  • Actions need to be performed on behalf of the user (booking appointments, making purchases)
  • Information needs verification from authoritative sources
  • Operations require specialized domain knowledge or functionality

Structure

To do...

Components

  • LLM Agent: The core language model that processes user requests, identifies tool needs, formats tool calls, and integrates tool responses.
  • Tool Registry: A catalog of available tools, their capabilities, input parameters, and expected outputs that the LLM can reference.
  • Tool Parser: A component that translates the LLM's natural language or structured requests into properly formatted API calls or function invocations.
  • Tools/APIs: External services, functions, or capabilities that provide specialized functionality (web search, database queries, calculators, etc.).
  • Result Interpreter: A component that processes the output from tools and reformats it for the LLM to understand and incorporate.
  • Response Composer: A component that combines the original user context, tool results, and LLM-generated text into a coherent response.

Interactions

  1. The LLM Agent receives a user request and determines if external tools are needed.
  2. If tools are needed, the LLM consults the Tool Registry to identify the appropriate tool.
  3. The LLM formulates the necessary parameters for the tool call.
  4. The Tool Parser validates and formats the call for the external API or function.
  5. The external tool executes and returns results.
  6. The Result Interpreter processes the tool output into a format the LLM can utilize.
  7. The LLM Agent incorporates the tool results into its reasoning.
  8. The Response Composer creates a final response combining the original context, tool results, and LLM-generated content.

Consequences

Benefits:

  • Extends the LLM's capabilities beyond text generation
  • Provides access to current information and real-time data
  • Enables complex, specialized functionality without requiring it to be built into the LLM
  • Improves response accuracy by grounding answers in external, authoritative sources
  • Creates a modular architecture where new tools can be added without modifying the core LLM

Limitations:

  • Increases system complexity and potential points of failure
  • Requires careful API design and error handling
  • May introduce latency due to external service calls
  • Tool misuse can lead to privacy or security concerns
  • Tool selection logic may be imperfect, leading to unnecessary or incorrect tool usage

Performance implications:

  • Additional processing time for tool selection and result interpretation
  • Network latency when calling external APIs
  • Potential rate limiting or quota issues with third-party services
  • Increased computational resources to manage parallel tool invocations

Implementation

  1. Define Tool Interfaces: Create clear specifications for each tool, including required parameters, expected outputs, and usage constraints.

  2. Tool Selection Logic: Implement robust mechanisms for the LLM to determine when a tool is needed and which specific tool to use. This can include:

    • Explicit tool descriptions in the system prompt
    • Few-shot examples of appropriate tool use
    • Guardrails to prevent unnecessary tool invocation

  3. Parameter Extraction: Develop methods for the LLM to extract and validate necessary parameters from user inputs:

    • JSON schema validation
    • Type checking
    • Default values for optional parameters

  4. Error Handling: Implement comprehensive error handling for:

    • Tool unavailability
    • Invalid parameters
    • Timeout conditions
    • Rate limiting
    • Authentication failures

  5. Result Processing: Create mechanisms to process tool outputs into formats suitable for LLM consumption, including:

    • Summarization for large outputs
    • Filtering for relevant information
    • Formatting for readability

  6. User Transparency: Provide clear indications to users when tools are being used and what information is being accessed.

Code Examples

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Variations

  • Autonomous Tool Selection: The LLM automatically determines which tools to use without explicit user direction.
  • User-Approved Tool Usage: The LLM requests permission before invoking any external tools.
  • Multi-Tool Orchestration: Coordinating multiple tool calls in sequence or parallel to accomplish complex tasks.
  • Tool Result Caching: Storing and reusing previous tool results to improve performance and reduce API calls.
  • Adaptive Tool Usage: Learning which tools are most effective for different query types based on user feedback.
  • Custom Tool Creation: Allowing users to define or customize their own tools for the LLM to utilize.

Real-World Examples

  • OpenAI's GPT models with function calling capabilities
  • Anthropic's Claude with tool use for web search, code execution, and data analysis
  • LangChain's agent framework for tool integration
  • Perplexity AI's real-time web search integration
  • GitHub Copilot with code repository integration
  • HuggingFace's PEFT for fine-tuning models with tool-use capabilities

Related Patterns