Memory Systems

Conversational memory buffers and context tracking across interaction cycles.

Langchain.Memory.Class Langchain.Memory.Buffer

Conversational memory buffers and context tracking across interaction cycles.

Key Concepts

Buffer Memory: Store ordered user and AI turns to provide continuous context for multi-turn chats.
Context Injection: Automatically extract memory variables and inject them into prompt templates.
Window Management: Bound conversation length to stay within token limits.

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.Memory (runApp) where

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

runApp :: IO ()
runApp = do
  o <- newOllama "gemma3" defaultConfig
  mem <- newWindowBufferMemory 5 [systemMessage "You are a helpful assistant."]
  res <- runExceptT $ do
    chat_ o mem "Hi, my name is Alice."
    chat_ o mem "What is my name?"
  case res of
    Left err -> T.putStrLn $ errorMessage err
    Right _ -> pure ()

chat_ :: Ollama -> WindowBufferMemory -> Text -> ExceptT LangchainError IO ()
chat_ model_ mem prompt = do
  addUserMessage mem prompt
  history <- messages mem
  resp <- invoke model_ history Nothing
  let answer = extractMessageText resp
  addAiMessage mem answer
  liftIO $ do
    T.putStrLn $ "User: " <> prompt
    T.putStrLn $ "AI: " <> answer

{-# LANGUAGE OverloadedStrings #-}

module OpenAI.Memory (runApp) where

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

runApp :: IO ()
runApp = do
  o <- getOpenRouterModel defaultModelName
  mem <- newWindowBufferMemory 5 [systemMessage "You are a helpful assistant."]
  res <- runExceptT $ do
    chat_ o mem "Hi, my name is Alice."
    chat_ o mem "What is my name?"
  case res of
    Left err -> T.putStrLn $ errorMessage err
    Right _ -> pure ()

chat_ :: OpenAI -> WindowBufferMemory -> Text -> ExceptT LangchainError IO ()
chat_ model_ mem prompt = do
  addUserMessage mem prompt
  history <- messages mem
  resp <- invoke model_ history Nothing
  let answer = extractMessageText resp
  addAiMessage mem answer
  liftIO $ do
    T.putStrLn $ "User: " <> prompt
    T.putStrLn $ "AI: " <> answer

Core Types & Functions

Memory m => m -> IO (Map Text Value)

Memory m => m -> Map Text Text -> Map Text Text -> IO ()

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 memoryollama

OpenAI / OpenRouter

Ensure your OPENROUTER_API_KEY or OPENAI_API_KEY is exported:

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