What is MemoriPy used for?

MemoriPy is used to give AI agents a 'memory' so they can remember past conversations, user preferences, and specific facts across different sessions, making them more context-aware.

Does MemoriPy support local models?

Yes, MemoriPy supports Ollama, allowing you to run your memory layer and embedding models locally without relying on external APIs.

How does memory decay work in MemoriPy?

The system applies a decay factor to memories over time. If a memory is not accessed or reinforced frequently, its relevance score drops, eventually allowing the system to prioritize newer or more important information.

Can I use MemoriPy with LangChain?

Yes, MemoriPy is designed to be compatible with common AI frameworks and relies on dependencies like LangChain for certain functionalities.

What storage formats does it support?

Out of the box, it supports JSONStorage for persistent file-based memory and InMemoryStorage for temporary, high-speed access during a single session.

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AI Memory

Free

MemoriPy

An open-source AI memory layer for agents featuring short/long-term storage, semantic clustering, and memory decay to ensure context-aware, human-like information retrieval.

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Rating

Reviews

Upvotes

3 months ago

Listed

AI Memory

AI Agents

LLM Memory

Open Source

Python Library

Long-term Memory

Context Management

Vector Database

Semantic Search

Knowledge Graphs

Tool information

Provider

CaspianMoon

Platforms

Web

Windows

macOS

Linux

Languages

English

API Available

Yes

Added to directory

3 months ago

Last updated

about 1 month ago

Rating

Not available

Pricing

Free

About MemoriPy

What the tool does and who it's for

MemoriPy is an open-source Python library that serves as a sophisticated memory layer for AI agents and context-aware applications. It enables AI systems to manage and retrieve information using a dual-storage approach consisting of short-term and long-term memory. The tool stands out by implementing human-like memory mechanics, such as memory decay for aging out irrelevant data and reinforcement for frequently accessed information. It utilizes semantic clustering, graph-based associations (spreading activation), and hierarchical structures to ensure that retrieved context is highly relevant to the current user query. Designed for developers, it integrates seamlessly with OpenAI, Azure OpenAI, OpenRouter, and Ollama, supporting both in-memory and persistent JSON storage.

Key capabilities

Short-term and Long-term Memory management for human-like persistence.

Hierarchical Clustering to group semantically similar memories for efficient retrieval.

Memory Decay and Reinforcement to prioritize relevant data and phase out old information.

Graph-Based Associations using spreading activation to find non-obvious context links.

Multiple LLM Support including OpenAI, Azure OpenAI, OpenRouter, and Ollama.

Flexible Storage options with built-in support for In-Memory and JSON file persistence.

Concept Extraction to automatically identify key entities and ideas from interactions.

Seamless Integration with popular tools like LangChain and FAISS for vector search.

Pricing

Free — plans below

Open Source

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