agents.rag.memory_aware.agent¶

Memory-Aware RAG Agents.

from typing import Any Memory-aware RAG with persistent context and iterative learning. Uses structured output models for memory management.

Classes¶

`MemoryAwareRAGAgent`	Complete Memory-Aware RAG agent with persistent learning.
`MemoryImportance`	Importance levels for memory items.
`MemoryItem`	Individual memory item with metadata.
`MemoryRetrievalAgent`	Agent that retrieves relevant memories for context enhancement.
`MemoryType`	Types of memory in the system.

Functions¶

`create_memory_aware_rag_agent`(documents[, llm_config, ...])	Create a Memory-Aware RAG agent.
`get_memory_aware_rag_io_schema`()	Get I/O schema for Memory-Aware RAG agents.

Module Contents¶

class agents.rag.memory_aware.agent.MemoryAwareRAGAgent(config)¶

Bases: haive.agents.multi.base.SequentialAgent

Complete Memory-Aware RAG agent with persistent learning.

Init .

Parameters:: config (SequentialAgentConfig) – [TODO: Add description]

classmethod from_documents(documents, llm_config=None, max_memories=100, **kwargs)¶

Create Memory-Aware RAG agent from documents.

Parameters:

documents (list[langchain_core.documents.Document])
llm_config (haive.core.models.llm.base.LLMConfig | None)
max_memories (int)

class agents.rag.memory_aware.agent.MemoryImportance¶

Bases: str, enum.Enum

Importance levels for memory items.

Initialize self. See help(type(self)) for accurate signature.

class agents.rag.memory_aware.agent.MemoryItem(/, **data)¶

Bases: pydantic.BaseModel

Individual memory item with metadata.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:: data (Any)

class agents.rag.memory_aware.agent.MemoryRetrievalAgent(llm_config=None, max_memories=10, **kwargs)¶

Bases: haive.agents.base.agent.Agent

Agent that retrieves relevant memories for context enhancement.

Initialize memory retrieval agent.

Parameters:

llm_config (haive.core.models.llm.base.LLMConfig | None)
max_memories (int)

build_graph()¶

Build memory retrieval graph.

Return type:: haive.core.graph.state_graph.base_graph2.BaseGraph

class agents.rag.memory_aware.agent.MemoryType¶

Bases: str, enum.Enum

Types of memory in the system.

Initialize self. See help(type(self)) for accurate signature.

agents.rag.memory_aware.agent.create_memory_aware_rag_agent(documents, llm_config=None, memory_mode='adaptive', **kwargs)¶

Create a Memory-Aware RAG agent.

Parameters:

documents (list[langchain_core.documents.Document])
llm_config (haive.core.models.llm.base.LLMConfig | None)
memory_mode (str)

Return type:

MemoryAwareRAGAgent

agents.rag.memory_aware.agent.get_memory_aware_rag_io_schema()¶

Get I/O schema for Memory-Aware RAG agents.

Return type:: dict[str, list[str]]