autourgos-buffer-memory

In-memory short-term buffer for Autourgos agents.

Two classes — a message-count bounded ring buffer and an unbounded conversation buffer. Fast, zero I/O, ideal for single-session use.

Install

pip install autourgos-buffer-memory

Quick Start

my_llm below is any chat-model instance, e.g. OpenAIChatModel from autourgos-openaichat (pip install autourgos-openaichat, needs OPENAI_API_KEY set).

Classes

RuntimeShortTermMemory

Keeps the last N messages in RAM. Oldest messages are dropped when the cap is exceeded.

from autourgos_buffer_memory import RuntimeShortTermMemory
from autourgos_react_agent import ReactAgent
from autourgos_openaichat import OpenAIChatModel

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

agent.invoke("My name is Jitin")
agent.invoke("What is my name?")
# → "Your name is Jitin."

ConversationBufferMemory

Same as RuntimeShortTermMemory but with no truncation — keeps every message for the session.

from autourgos_buffer_memory import ConversationBufferMemory

memory = ConversationBufferMemory()
agent  = ReactAgent(llm=my_llm, memory=memory)  # my_llm as shown in Quick Start above

For long conversations, use autourgos-summary-memory or autourgos-token-memory to stay within context window limits.

Parameters

RuntimeShortTermMemory

ConversationBufferMemory

API

memory.add_user_message("Hello")
memory.add_agent_message("Hi there!")
memory.add_tool_message("search", "Found 5 results")
memory.add_system_message("You are a helpful assistant")

messages = memory.get_messages()   # list of role/content dicts
context  = memory.format_for_llm() # formatted string for LLM prompt
memory.clear()

Links

• PyPI: https://pypi.org/project/autourgos-buffer-memory/
• GitHub: https://github.com/devxjitin/autourgos-buffer-memory
• Issues: https://github.com/devxjitin/autourgos-buffer-memory/issues

License

MIT — see LICENSE