autourgos-memory

Base memory interfaces for Autourgos agents — the foundation package. It defines the abstract interfaces (BaseMemory, BaseRetriever, MemoryMessage, Document) that every concrete memory implementation uses.

from autourgos_memory import RuntimeShortTermMemory  # requires autourgos-buffer-memory installed
from autourgos_agent import Agent
from autourgos_openaichat import OpenAIChatModel

my_llm = OpenAIChatModel(model="gpt-4o-mini")
memory = RuntimeShortTermMemory(max_messages=20)
agent  = Agent(llm=my_llm, memory=memory)

Features

• Abstract interfaces: BaseMemory (short-term conversational), BaseRetriever (relevance-scored recall), MemoryMessage, Document
• Soft re-exports: concrete backends resolve from autourgos_memory directly if their own package is installed — RuntimeShortTermMemory, ConversationBufferMemory, LocalShortTermMemory, SQLiteMemory, KeywordRetriever, KeywordMemory, SummaryBufferedMemory, TokenBufferedMemory, VectorMemory, VectorRetriever, EpisodicMemory
• Zero required dependencies — install only the concrete backends you actually need

Table of Contents

• Install
• Memory Types at a Glance
• Quick Start
• Base Interfaces
• License

Install

# Base interfaces only
pip install autourgos-memory

# Or install concrete implementations individually
pip install autourgos-buffer-memory      # in-memory ring buffer
pip install autourgos-local-memory       # JSON file + SQLite
pip install autourgos-semantic-memory    # TF-IDF keyword retrieval
pip install autourgos-summary-memory     # LLM-compressed rolling summary
pip install autourgos-token-memory       # token-bounded buffer
pip install autourgos-vector-memory      # local, provider-agnostic embedding recall
pip install autourgos-episodic-memory    # structured task/outcome log

Memory Types at a Glance

Quick Start

RuntimeShortTermMemory is soft re-exported from autourgos_memory — it only resolves if autourgos-buffer-memory is also installed:

pip install autourgos-memory autourgos-buffer-memory autourgos-openaichat

from autourgos_memory import RuntimeShortTermMemory
from autourgos_agent import Agent
from autourgos_openaichat import OpenAIChatModel

my_llm = OpenAIChatModel(model="gpt-4o-mini")  # needs OPENAI_API_KEY set
memory = RuntimeShortTermMemory(max_messages=20)
agent  = Agent(llm=my_llm, memory=memory)
result = agent.invoke("What did I ask you last time?")

Base Interfaces

MemoryMessage

from autourgos_memory import MemoryMessage
from datetime import datetime, timezone

msg = MemoryMessage(role="user", content="Hello", timestamp=datetime.now(timezone.utc))
print(msg.to_dict())
# {"role": "user", "content": "Hello", "timestamp": "2024-..."}

Allowed roles: user, agent, system, tool.

BaseMemory

Implement this to create your own memory backend:

from autourgos_memory import BaseMemory, MemoryMessage

class MyCustomMemory(BaseMemory):
    def add_user_message(self, content: str) -> MemoryMessage: ...
    def add_agent_message(self, content: str) -> MemoryMessage: ...
    def add_tool_message(self, tool_name: str, result: str) -> MemoryMessage: ...
    def format_for_llm(self, query: str = None) -> str: ...
    def clear(self) -> None: ...

BaseRetriever

Implement this to plug in your own vector database:

from autourgos_memory import BaseRetriever, Document

class MyVectorDB(BaseRetriever):
    def retrieve(self, query: str, top_k: int = 5) -> list[Document]: ...

Document

from autourgos_memory import Document

doc = Document(content="Paris is the capital of France.", score=0.92, source="wiki")

License

Apache License 2.0, Copyright (c) 2026 Jitin Kumar Sengar