rollmem 0.0.1

Standalone, dependency-free rolling conversation memory (summary + buffer), inspired by LangChain's ConversationSummaryBufferMemory.

rollmem

Standalone, dependency-free rolling conversation memory for LLM apps — a running summary plus a recent-message buffer, inspired by LangChain's ConversationSummaryBufferMemory, but with no LangChain (or any) dependency.

Handy for conversation memory, context compression, summarization, and gist-style long-chat handling — a tiny LangChain alternative when you only need the summary-buffer pattern.

Why

ConversationSummaryBufferMemory is a great pattern: keep recent turns verbatim, fold older turns into a running summary so context stays bounded. But pulling in all of LangChain just for that is heavy. rollmem extracts the idea into a tiny, provider-agnostic package. You inject how to summarize and how to count tokens — rollmem stays neutral.

Install

pip install rollmem

Usage

from rollmem import RollingMemory

def summarize(existing_summary, messages):
    # plug in any LLM here; return the new summary string
    folded = " ".join(m.content for m in messages)
    return (existing_summary + " " + folded).strip()

mem = RollingMemory(
    max_tokens=2000,
    summarize_fn=summarize,   # optional; without it, evicted turns are dropped
    # token_counter=...       # optional; defaults to a word-count estimate.
    #                         # In production inject a model-accurate counter, e.g.
    #                         # token_counter=lambda text: len(enc.encode(text))
)

mem.add_user_message("Hi, I'm planning a trip to Korea.")
mem.add_assistant_message("Great! When are you going?")

print(mem.get_context())    # -> str: summary (if any) + recent buffer, joined
print(mem.get_messages())   # -> list[Message]: summary prepended as a system turn

max_tokens is the budget for the verbatim recent-message buffer — not the running summary, and not a model's generation max_tokens (output limit). When the buffer exceeds it, the oldest turns are folded into the summary.

token_counter takes a single message's text (str) and returns an int. The default is a crude word count — fine for demos, but pass a model-accurate counter (such as tiktoken) for real token budgets.

Persistence

to_dict() / from_dict() serialize the memory state (running summary plus buffer) to and from a plain dict — you choose the storage format:

import json

raw = json.dumps(mem.to_dict())   # save anywhere: file, DB column, cache...

mem = RollingMemory.from_dict(
    json.loads(raw),
    max_tokens=2000,
    summarize_fn=summarize,        # callbacks are NOT serialized — re-inject them
    # token_counter=...
)

max_tokens and the callbacks are runtime configuration, not saved state, so you pass them again on restore. The buffer is restored verbatim; the token budget is re-applied on the next added message.

How it works

• New turns go into buffer.
• When buffer exceeds max_tokens, the oldest turns are folded into summary via summarize_fn (or dropped if none is provided).
• get_messages() -> list[Message] returns the buffer with the summary prepended as a system turn. get_context() -> str is the string form of the same thing (prompt-ready), so the two never diverge. Neither adds a language-specific label — relabel the summary in your own prompt assembly if you need to.

Limitations

• Lossy by design. Older turns are folded into the summary repeatedly, so each pass can blur or drop detail (a "telephone game" effect). Keep max_tokens large enough that anything you can't afford to lose stays in the verbatim buffer.
• The summary is not bounded for you. max_tokens limits only the verbatim buffer, not the running summary. rollmem hands your summarize_fn the current summary plus the evicted turns and stores whatever it returns — so keeping the summary compact is your summarize_fn's job. If it merely concatenates, the summary (and thus get_context()) grows without limit. Prompt it to compress, or cap the summary length inside the callback.
• Only as accurate as your counter. The default token counter is a rough word count; inject a model-accurate one (e.g. tiktoken) for real budgets.
• In-memory by default. State lives in memory, but to_dict() / from_dict() let you persist and restore it (see Persistence). Callbacks are not serialized and must be re-injected on restore.

License

MIT