SummaryMemory

A memory implementation that automatically compresses old messages into a running summary when the buffer grows too large. Ideal for long conversations that must stay within an LLM's context window.

Usage

import { SummaryMemory } from "flowneer/plugins/memory";
import { callLlm } from "./utils/callLlm";

const memory = new SummaryMemory({
  maxMessages: 10,
  summarize: async (messages) => {
    const text = messages.map((m) => `${m.role}: ${m.content}`).join("\n");
    return callLlm(`Summarize this conversation concisely:\n${text}`);
  },
});

Constructor Options

Option	Type	Required	Default	Description
summarize	(msgs) => string | Promise<string>	✅	—	Summarisation function (usually an LLM call)
maxMessages	number		10	Number of recent messages to keep verbatim

Methods

Method	Signature	Description
add	async (msg) => Promise<void>	Append a message; may trigger summarisation
get	() => MemoryMessage[]	Current messages, prepended with summary if present
clear	() => void	Remove all messages and the running summary
toContext	() => string	Formatted context: summary block + recent messages

Note: add() is async because it may call your summarize function. Await it in your steps.

Compression Behaviour

When messages.length > maxMessages:

1. The oldest half of messages is passed to summarize.
2. If a previous summary exists it is prepended as context for the new summary.
3. The compressed messages are replaced by a single [Summary] ... system message.

This keeps the conversation context manageable for long sessions.

Example with withMemory

import { FlowBuilder } from "flowneer";
import { withMemory, SummaryMemory } from "flowneer/plugins/memory";

const AppFlow = FlowBuilder.extend([withMemory]);

const memory = new SummaryMemory({
  maxMessages: 8,
  summarize: (msgs) =>
    callLlm(
      "Summarize:\n" + msgs.map((m) => `${m.role}: ${m.content}`).join("\n"),
    ),
});

const flow = new AppFlow<ChatState>()
  .withMemory(memory)
  .startWith(async (s) => {
    await s.__memory!.add({ role: "user", content: s.userInput });
    const ctx = await s.__memory!.toContext();
    s.response = await callLlm(`${ctx}\nassistant:`);
    await s.__memory!.add({ role: "assistant", content: s.response });
  });