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Agent Executors

info

Conceptual Guide

To make agents more powerful we need to make them iterative, ie. call the model multiple times until they arrive at the final answer. That's the job of the AgentExecutor.

Example​

An example that initialize a MRKL (Modular Reasoning, Knowledge and Language, pronounced "miracle") agent executor.

package main

import (
	"context"
	"fmt"
	"os"

	"github.com/tmc/langchaingo/agents"
	"github.com/tmc/langchaingo/chains"
	"github.com/tmc/langchaingo/llms/openai"
	"github.com/tmc/langchaingo/tools"
	"github.com/tmc/langchaingo/tools/serpapi"
)

func main() {
	if err := run(); err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}
}

func run() error {
	llm, err := openai.New()
	if err != nil {
		return err
	}
	search, err := serpapi.New()
	if err != nil {
		return err
	}
	agentTools := []tools.Tool{
		tools.Calculator{},
		search,
	}

	agent := agents.NewOneShotAgent(llm,
		agentTools,
		agents.WithMaxIterations(3))
	executor := agents.NewExecutor(agent)

	question := "Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?"
	answer, err := chains.Run(context.Background(), executor, question)
	fmt.Println(answer)
	return err
}