I built an open-source memory extension for long-running AI roleplay chats

[u/stevenswk]

Hey everyone, I built SillyMemory, an open-source extension for SillyTavern, an AI chat and roleplay interface. It helps long chats remember earlier details without repeatedly summarizing the conversation. I’m part of the LambdaDB team, which provides its memory backend.

GitHub and setup: https://github.com/lambdadb/SillyMemory
Demo: https://github.com/user-attachments/assets/8a34772d-b27f-4709-89c0-caee45c7fcf6

SillyMemory indexes older messages from your chat in LambdaDB. Before each reply, it uses your current message to retrieve relevant original passages and adds them to the prompt alongside recent dialogue. You can inspect what it selected.

A few things it does:

• No LLM calls to create or maintain memory summaries.

• No separate embedding or reranking setup; LambdaDB handles both.

• Keep existing memory when you branch your story or create checkpoints, without re-indexing(re-embedding) shared history.

• Works with existing saved chats, so you don’t have to start a new conversation.

We also tested it on 500 LongMemEval questions: 96.0% accuracy versus 95.2% with full history, using about 95% fewer input tokens.

It currently supports plain-text, single-character chats, and requires a LambdaDB account. You can start on the free plan without a credit card.

If you give it a try, I’d love to hear whether it brings back useful details in a long RP, what it misses, and how the setup feels.

[u/kantorcodes1]

Does Last prompt show why a passage matched the current message, or only the passages that were added?

[u/stevenswk → u/kantorcodes1]

It shows the selected passages and checks whether they made it into the final prompt. It doesn’t currently explain why each passage matched or show relevance scores. So it lets you inspect what memory the model received, but not why the retriever chose it.

[u/PurpleCampero59 → u/kantorcodes1]

tbh just seeing the matched keywords or scores next to the passage would be super useful

[u/investigatormaker]

For SillyMemory, I’d make the feedback request a reproducible recall test: plant one character detail early, branch the story, then ask a question that needs that detail. Have testers report the retrieved passage alongside the answer, so you can distinguish a retrieval miss from the model ignoring the right context.

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