Tool Calling Agent

The Tool Calling Agent provides a flexible framework for executing multiple tools in sequence or parallel. It manages tool orchestration, memory management, error handling, and maintains conversation context while providing detailed execution tracking.

Tool Calling Agent workflow and architecture

Configuration Parameters

Required Input Parameters

tools: Array of available tools
input: User query or instruction
system_prompt: Base system instructions

Optional Configuration

llm: Language model configuration
- model_name: Model identifier
- temperature: Response creativity (0-1)
- max_tokens: Maximum response length
agent_config: Agent behavior settings
- agent_description: Role and capabilities
- handle_parse_errors: Error handling strategy
- verbose: Detailed logging
- max_iterations: Maximum tool call iterations
chat_memory: Memory configuration
- type: Memory storage type
- max_tokens: Token limit for history
- persistence: Storage duration

Output Format

{
  "agent": {
    "status": "success" | "error",
    "execution_path": [
      {
        "tool": string,
        "timestamp": string,
        "success": boolean,
        "iteration": number
      }
    ],
    "memory_usage": {
      "current_tokens": number,
      "max_tokens": number
    },
    "performance_metrics": {
      "total_time": number,
      "tool_execution_time": number,
      "thinking_time": number
    }
  },
  "response": {
    "message": string,
    "tool_results": [
      {
        "tool_name": string,
        "input": object,
        "output": any,
        "error": string | null
      }
    ],
    "reasoning_steps": [
      {
        "thought": string,
        "action": string,
        "result": string
      }
    ],
    "confidence": number
  }
}

Features

Multi-tool orchestration
Sequential and parallel execution
Memory management
Error handling and recovery
Execution tracking
Performance monitoring
Detailed logging
Context preservation

Note: Monitor memory usage when handling long conversations. Implement appropriate cleanup strategies for completed tool executions.

Tip: Use descriptive tool names and clear parameter definitions. Enable verbose mode during development for better debugging.

Example Usage

const toolAgent = new ToolCallingAgent({
  tools: [
    {
      name: "search",
      description: "Search for information",
      parameters: {
        query: "string",
        max_results: "number"
      }
    },
    {
      name: "summarize",
      description: "Summarize text content",
      parameters: {
        text: "string",
        max_length: "number"
      }
    }
  ],
  llm: {
    model_name: "gpt-4",
    temperature: 0.3
  },
  system_prompt: "You are a helpful assistant that can use various tools...",
  agent_config: {
    agent_description: "Tool orchestration specialist",
    handle_parse_errors: true,
    verbose: true,
    max_iterations: 5
  },
  chat_memory: {
    type: "buffer",
    max_tokens: 2000,
    persistence: "session"
  }
});

const response = await toolAgent.execute({
  input: "Search for recent AI developments and provide a summary"
});