> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Dakera Memory Adapter • AI Agent SDK

> DakeraMemoryAdapter: Memory adapter that wraps the Dakera SDK to implement ``MemoryProtocol``.

# DakeraMemoryAdapter

> Defined in the [**factories**](https://praison.ai/docs/sdk/reference/praisonaiagents/modules/factories.md) module.

AI Agent

Memory adapter that wraps the Dakera SDK to implement `MemoryProtocol`.

Dakera ([https://dakera.ai](https://dakera.ai)) is a self-hosted memory server that provides
persistent, decay-weighted vector recall across sessions: memories are
importance-scored and decay over time, so stale context stops competing
with fresh, relevant facts. All memories are scoped by `agent_id`.

Unlike a flat vector store, Dakera has a first-class `memory_type` field,
so this adapter maps PraisonAI's two tiers onto distinct Dakera types
(short-term -> `"working"`, long-term -> `"episodic"` by default),
keeping recency-heavy scratch context separate from durable knowledge.

Also implements the optional `DeletableMemoryProtocol` (`delete_memory` /
`delete_memories`) and `ResettableMemoryProtocol` (`reset_short_term` /
`reset_long_term`).

## Constructor

**dakera\_config** `Dict[str, Any]` *required*

No description available.

## Methods

> [card] [store\_short\_term()](https://praison.ai/docs/sdk/reference/praisonaiagents/functions/DakeraMemoryAdapter-store_short_term.md)
>
> Store content in short-term (`working`) memory.

> [card] [search\_short\_term()](https://praison.ai/docs/sdk/reference/praisonaiagents/functions/DakeraMemoryAdapter-search_short_term.md)
>
> Search short-term (`working`) memory.

> [card] [store\_long\_term()](https://praison.ai/docs/sdk/reference/praisonaiagents/functions/DakeraMemoryAdapter-store_long_term.md)
>
> Store content in long-term (`episodic`) memory.

> [card] [search\_long\_term()](https://praison.ai/docs/sdk/reference/praisonaiagents/functions/DakeraMemoryAdapter-search_long_term.md)
>
> Search long-term (`episodic`) memory.

> [card] [get\_all\_memories()](https://praison.ai/docs/sdk/reference/praisonaiagents/functions/DakeraMemoryAdapter-get_all_memories.md)
>
> Return all memories for the agent (no embedding required).

> [card] [delete\_memory()](https://praison.ai/docs/sdk/reference/praisonaiagents/functions/DakeraMemoryAdapter-delete_memory.md)
>
> Delete a specific memory by id. Returns True on success.

> [card] [delete\_memories()](https://praison.ai/docs/sdk/reference/praisonaiagents/functions/DakeraMemoryAdapter-delete_memories.md)
>
> Delete multiple memories by id. Returns the number deleted.

> [card] [reset\_short\_term()](https://praison.ai/docs/sdk/reference/praisonaiagents/functions/DakeraMemoryAdapter-reset_short_term.md)
>
> Clear all short-term (`working`) memory for the agent.

> [card] [reset\_long\_term()](https://praison.ai/docs/sdk/reference/praisonaiagents/functions/DakeraMemoryAdapter-reset_long_term.md)
>
> Clear all long-term (`episodic`) memory for the agent.

## Source

> [card] [View on GitHub](https://github.com/MervinPraison/PraisonAI/blob/main/src/praisonai-agents/praisonaiagents/memory/adapters/factories.py#L646)
>
> `praisonaiagents/memory/adapters/factories.py` at line 646

***

## Related Documentation

> [card] [Memory Concept](https://praison.ai/docs/docs/concepts/memory)

> [card] [Memory Overview](https://praison.ai/docs/docs/memory/overview)

> [card] [Memory Configuration](https://praison.ai/docs/docs/configuration/memory-config)

> [card] [Session Resume](https://praison.ai/docs/docs/memory/session-resume)


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