Plan-and-Execute Agent

Macro-level planning decoupled from execution with dynamic replanning.

Langchain.Agent.PlanAndExecute

Macro-level planning decoupled from execution with dynamic replanning.

Key Concepts

Explicit Plan Generation: Initial planning step produces an ordered list of tasks required to achieve a complex objective.
Specialized Execution: Executors handle sub-tasks independently without losing global focus.
Replanning Phase: Review completed tasks and current state to update remaining steps dynamically.

Working Code Example

Compare local execution via Ollama and cloud API execution via OpenAI / OpenRouter. Use the toggle tabs or the global provider switcher in the header to switch:

{-# LANGUAGE OverloadedStrings #-}

module Ollama.PlanAndExecute (runApp) where

import Control.Monad.Except (runExceptT)
import qualified Data.Text.IO as T
import Langchain.Prelude

runApp :: IO ()
runApp = do
  o <- newOllama "qwen3.5:2b" defaultConfig
  let tools = [shellTool]
      executor = createReActAgent (bindTools tools o) tools
      agent = newPlanAndExecuteAgent o executor Nothing
      goal =
        "Use shell commands to check the operating system name (uname -s) and architecture (uname -m), then summarize the host platform."
  res <- runExceptT $ runPlanAndExecute agent goal
  case res of
    Left err -> T.putStrLn $ errorMessage err
    Right ans -> T.putStrLn ans

{-# LANGUAGE OverloadedStrings #-}

module OpenAI.PlanAndExecute (runApp) where

import Control.Monad.Except (runExceptT)
import qualified Data.Text.IO as T
import Langchain.Prelude
import OpenAI.Common (defaultModelName, getOpenRouterModel)

runApp :: IO ()
runApp = do
  o <- getOpenRouterModel defaultModelName
  let tools = [shellTool]
      executor = createReActAgent o tools
      agent = newPlanAndExecuteAgent o executor Nothing
      goal =
        "Use shell commands to check the operating system name (uname -s) and architecture (uname -m), then summarize the host platform."
  res <- runExceptT $ runPlanAndExecute agent goal
  case res of
    Left err -> T.putStrLn $ errorMessage err
    Right ans -> T.putStrLn ans

Core Types & Functions

data Plan = Plan { planSteps :: [Text] }

ChatModel m => m -> [Tool] -> Text -> IO (Either LangchainError Text)

Running This Example

Local Ollama

Ensure your Ollama daemon is running locally with the target model:

ollama run gemma3 # or your desired model
stack run planandexecuteollama

OpenAI / OpenRouter

Ensure your OPENROUTER_API_KEY or OPENAI_API_KEY is exported:

export OPENROUTER_API_KEY="your-api-key"
stack run planandexecuteopenai