Dakera Memory Adapter • AI Agent SDK
DakeraMemoryAdapter: Memory adapter that wraps the Dakera SDK to implement “MemoryProtocol“.
DakeraMemoryAdapter
Defined in the factories module.
AI Agent
Memory adapter that wraps the Dakera SDK to implement `MemoryProtocol`.
Dakera (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
Dict[str, Any]No description available.
Methods
Store content in short-term (`working`) memory.
Search short-term (`working`) memory.
Store content in long-term (`episodic`) memory.
Search long-term (`episodic`) memory.
Return all memories for the agent (no embedding required).
delete_memory()Delete a specific memory by id. Returns True on success.
delete_memories()Delete multiple memories by id. Returns the number deleted.
reset_short_term()Clear all short-term (`working`) memory for the agent.
Clear all long-term (`episodic`) memory for the agent.
Source
View on GitHubpraisonaiagents/memory/adapters/factories.py at line 646