Problem

Flowise currently has no self-hosted, decay-weighted memory backend. The available external memory options (Redis, MongoDB, DynamoDB) store raw chat history without semantic relevance ranking. Users running Flowise in air-gapped or GDPR-restricted environments have no way to provide agents with cross-session semantic recall without sending data to an external cloud API.

Proposed Solution

Add a Dakera Memory node to the Memory category — a self-hosted vector memory backend with decay-weighted semantic search.

Dakera (dakera.ai) is an open-source, self-hosted memory server that exposes:

• POST /v1/memories — store a memory
• POST /v1/memories/search — semantic recall with decay weighting (recent + frequently-accessed memories rank higher)
• DELETE /v1/memories — clear memories for a session

Implementation pattern

Follows the established Mem0/Zep node pattern exactly:

class DakeraMemoryExtended extends BaseChatMemory implements MemoryMethods {
  // getChatMessages — semantic recall via Dakera + DB message fetch (DESC, reversed)
  // addChatMessages — stores user and AI messages independently
  // clearChatMessages — deletes Dakera memories + DB chat messages
}

No new npm dependency — uses native fetch to call the Dakera REST API directly, keeping the PR self-contained.

Files

Self-hosting

docker run -d -p 3000:3000 -e DAKERA_API_KEY=your-key dakera/dakera:latest

No signup, no external API. Works with any Flowise deployment.

Why this matters

Dakera fills a gap the existing memory nodes don't cover:

I'm happy to submit a PR following Flowise contribution guidelines.