Dakera Memory

Persistent, decay-weighted memory for Dify agents, backed by a self-hosted
Dakera server.

Dakera is a self-hosted memory server that adds persistent, decay-weighted vector recall
across sessions: memories are importance-scored and decay over time, so stale context stops
competing with fresh, relevant facts. This plugin lets a Dify Agent, Chatflow, or Workflow
store facts and recall them semantically in later runs.

Tools

Tool	What it does	Endpoint
Store Memory	Persist a concise, self-contained fact for future recall. Optional importance (0–1), session scope, and tags.
Recall Memory	Retrieve the most semantically relevant memories for a natural-language query, ranked by score.
Search Memory	Filtered browse/list over memories — optional text query plus tag, importance, and count filters.
Get Memory	Fetch a single memory by its ID (content, importance, tags, metadata).
Update Memory	Change an existing memory's content (re-embedded), importance, or tags by ID.
Forget Memory	Delete memories by ID, session, tags, or importance threshold. Requires a selector; deletion is permanent.

Memories are namespaced by — use the same across tools to keep
each agent's (or user's) memories isolated. Recall is best for precise semantic retrieval;
Search is best for browsing/filtering/auditing what an agent remembers.

Setup

1. Run a Dakera server

Dakera is self-hosted. The quickest path is the docker-compose in
, which starts the
server (image ) plus its object store. By default the API listens
on port 3000.

2. Configure the plugin

Provide two credentials when authorizing the tool:

Dakera Server URL — the base URL of your server, e.g. .
Dakera API Key (optional) — a key if your server was started with
. Leave empty for unauthenticated local development.

The plugin validates the connection with a probe when you save credentials.

Usage example

In an Agent app, add the tools you need (all six, or just Store + Recall). A typical loop:

Early in a task, call Recall Memory with a query like
(same you use
everywhere) to pull in relevant prior context.
When the agent learns something durable, call Store Memory with a concise fact such as
, .

Next session, recalling with the same surfaces that fact even though the
conversation is new.

Chaining tools with memory IDs

Recall and Search return each memory's ID in their output, so an agent can act on a specific
memory afterwards:

Recall/Search → Get — pull the full record of a specific hit.
Recall/Search → Update — correct or re-weight a memory (e.g. bump , replace
) by its ID.
Recall/Search → Forget — delete a memory that is now wrong or obsolete by its ID.

Use Forget with a , , or selector to prune a whole set at
once — it refuses to run without at least one selector, so it can't wipe an agent's namespace by
accident.

Development

The plugin source lives alongside the packaged in this directory. To run the tests:

The directory is excluded from the packaged plugin via .

Requirements & connection

A reachable, self-hosted Dakera server (this plugin does not bundle or host one).
Network egress from Dify to the server URL you configure.
No third-party accounts — all data stays on the server you run.

Contact

Issues and questions: https://github.com/dakera-ai/dakera-py/issues