praval.memory.memory_manager

MemoryManager - Unified interface for all Praval agent memory systems

This coordinates: - Short-term working memory - Long-term vector memory - Episodic conversation memory - Semantic knowledge memory

Classes

MemoryManager(agent_id[, backend, ...])

Unified memory management system for Praval agents
class praval.memory.memory_manager.MemoryManager(agent_id, backend='auto', qdrant_url='http://localhost:6333', storage_path=None, collection_name='praval_memories', short_term_max_entries=1000, short_term_retention_hours=24, knowledge_base_path=None, embedding_provider=None, embedding_model=None, embedding_dimensions=None, embedding_provider_options=None, embedding_runtime=None)[source]

Bases: object

Unified memory management system for Praval agents

Provides a single interface to: - Store and retrieve memories across all systems - Coordinate between short-term and long-term storage - Manage different types of memory (episodic, semantic, etc.) - Optimize memory access patterns

Parameters:

  • agent_id (str)

  • backend (str)

  • qdrant_url (str)

  • storage_path (str | None)

  • collection_name (str)

  • short_term_max_entries (int)

  • short_term_retention_hours (int)

  • knowledge_base_path (str | None)

  • embedding_provider (str | None)

  • embedding_model (str | None)

  • embedding_dimensions (int | None)

  • embedding_provider_options (Dict[str, Any] | None)

  • embedding_runtime (Any | None)

__init__(agent_id, backend='auto', qdrant_url='http://localhost:6333', storage_path=None, collection_name='praval_memories', short_term_max_entries=1000, short_term_retention_hours=24, knowledge_base_path=None, embedding_provider=None, embedding_model=None, embedding_dimensions=None, embedding_provider_options=None, embedding_runtime=None)[source]

Initialize the unified memory manager

Parameters:

  • agent_id (str) – ID of the agent using this memory

  • backend (str) – Memory backend (“auto”, “chromadb”, “qdrant”, “memory”)

  • qdrant_url (str) – URL for Qdrant vector database

  • storage_path (Optional[str]) – Path for persistent storage

  • collection_name (str) – Collection name for vector storage

  • short_term_max_entries (int) – Max entries in short-term memory

  • short_term_retention_hours (int) – Short-term memory retention time

  • knowledge_base_path (Optional[str]) – Path to knowledge base files to auto-index

  • embedding_provider (Optional[str]) – Provider used for vector embeddings

  • embedding_model (Optional[str]) – Embedding model identifier

  • embedding_dimensions (Optional[int]) – Expected embedding vector size

  • embedding_provider_options (Optional[Dict[str, Any]]) – Provider-specific embedding options

  • embedding_runtime (Optional[Any]) – Preconfigured embedding runtime

store_memory(agent_id, content, memory_type=None, **kwargs)
retrieve_memory(memory_id)
search_memories(query)[source]

Search memories across all systems

Parameters:

query (MemoryQuery) – The search query

Return type:

MemorySearchResult

Returns:

Combined search results

get_conversation_context(agent_id, turns=10)[source]

Get recent conversation context for an agent

Parameters:

  • agent_id (str) – The agent ID

  • turns (int) – Number of conversation turns

Return type:

List[MemoryEntry]

Returns:

List of conversation memories

store_conversation_turn(agent_id, user_message, agent_response, **kwargs)
store_knowledge(agent_id, knowledge, domain='general', confidence=1.0, knowledge_type='fact')[source]

Store knowledge or facts

Parameters:

  • agent_id (str) – The agent ID

  • knowledge (str) – The knowledge content

  • domain (str) – Domain of knowledge

  • confidence (float) – Confidence in the knowledge

  • knowledge_type (str) – Type of knowledge (fact, concept, rule)

Return type:

str

Returns:

Memory ID

get_domain_knowledge(agent_id, domain, limit=20)[source]

Get knowledge in a specific domain

Parameters:

  • agent_id (str) – The agent ID

  • domain (str) – The domain

  • limit (int) – Maximum results

Return type:

List[MemoryEntry]

Returns:

List of knowledge entries

clear_agent_memories(agent_id, memory_types=None)[source]

Clear memories for a specific agent

Parameters:

  • agent_id (str) – The agent ID

  • memory_types (Optional[List[MemoryType]]) – Types of memory to clear (all if None)

get_memory_stats()[source]

Get comprehensive memory statistics

Return type:

Dict[str, Any]

health_check()[source]

Check health of all memory systems

Return type:

Dict[str, bool]

get_active_backend()[source]

Get information about the currently active memory backend.

Returns:

  • name: Backend name (“chromadb”, “qdrant”, “memory”)

  • type: “persistent” or “in_memory”

  • available: Whether the backend is operational

  • details: Backend-specific information

Return type:

Dict[str, Any]

recall_by_id(memory_id)[source]

Recall a specific memory by ID (for spore references)

Return type:

List[MemoryEntry]

Parameters:

memory_id (str)

get_knowledge_references(content, importance_threshold=0.7)[source]

Get knowledge references for lightweight spores

Return type:

List[str]

Parameters:

  • content (str)

  • importance_threshold (float)

shutdown()[source]

Shutdown all memory systems