Concepts¶
Memorizz composes agent behavior from memory types, storage providers, and application modes.
Memory Types¶
MemoryType is defined in src/memorizz/enums/memory_type.py.
| Enum | Purpose | Main Implementation |
|---|---|---|
PERSONAS |
Versioned agent identity and evolution history | src/memorizz/long_term/semantic/persona/ |
KNOWLEDGE_BASE |
Semantic facts and reusable knowledge | src/memorizz/long_term/semantic/ |
ENTITY_MEMORY |
Structured entity profiles and attributes | src/memorizz/long_term/semantic/entity_memory/ |
TOOLBOX |
Callable tools and tool metadata | src/memorizz/long_term/procedural/toolbox/ |
WORKFLOW_MEMORY |
Process and task execution traces | src/memorizz/long_term/procedural/workflow/ |
SKILLBOX |
Distilled learned skills and lifecycle state | src/memorizz/long_term/procedural/skillbox/ |
CONVERSATION_MEMORY |
User/assistant interaction history | src/memorizz/long_term/episodic/ |
SUMMARIES |
Compressed conversation summaries | src/memorizz/long_term/episodic/summary_component.py |
SHORT_TERM_MEMORY |
Provider-backed working-session records | src/memorizz/memagent/core.py |
SEMANTIC_CACHE |
Similar-query response caching | src/memorizz/short_term_memory/semantic_cache.py |
TOOL_LOG |
Offloaded tool output referenced from prompt context | src/memorizz/memagent/utils/tool_log.py |
SHARED_MEMORY |
Multi-agent coordination state | src/memorizz/coordination/shared_memory/ |
MEMAGENT |
Persisted agent configuration | src/memorizz/memagent/models.py |
Providers vs Memory Types¶
- Memory types define what data is stored.
- Providers define where data is stored (filesystem, Oracle, MongoDB, custom).
- Application modes choose a default combination of memory types.
Application Modes¶
Mode defaults come from src/memorizz/enums/application_mode.py.
assistant: conversation, long-term, personas, entity memory, short-term, summariesworkflow: workflow memory, toolbox, long-term, short-term, summariesdeep_research: toolbox, shared memory, long-term, short-term, summaries
You can still override with explicit memory_types if your use case needs a custom stack.
Typical Runtime Lifecycle¶
- Agent receives a query.
- Relevant memory is retrieved from active memory types via the configured provider.
- LLM produces a response (and may call registered tools).
- Interaction is written back to memory stores.
- Optional semantic cache and summary logic optimize future turns.