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MemoriPy

AI memory layer for smarter agents.

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December 30, 2024

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MemoriPy

AI memory

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Agent API

AI memory layer for smarter agents.

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Agent API Local AI Memory Layer Human-like Memory Learn And Recall

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Author

Khazar Allahverdiyev

🛠️ 4 tools

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MemoriPy is an open-source AI memory layer designed to bring human-like memory and adaptability capabilities to AI systems. By providing AI agents with both short-term and long-term memory, it can transform repetitive, routine systems into platforms that are more aware of their context and are capable of acting as intelligent assistants.

MemoriPy enhances every interaction to help build smarter and more efficient AI systems by producing context-rich responses and therefore eliminating repetitive queries.

It improves accuracy by utilizing a unique concept that involves clustering and memory decay to refine responses and prioritize the most pertinent information.

MemoriPy is designed to work seamlessly with other platforms such as OpenAI and Ollama. It can be optimally used in diverse AI-driven projects. For instance, it can upgrade chatbots to remember past interactions and resolve queries faster, develop AI that evolves over time by adjusting to user preferences, construct autonomous systems that become smarter with each engagement, and enhance retail recommendations by recalling customer purchase history.

To sum up, MemoriPy is an effective AI tool that grants AI systems a memory feature, paving the way for AI that can adapt and evolve over time based on the learned and recalled information.

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Initial release

December 30, 2024

Khazar Allahverdiyev

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Initial release of MemoriPy.

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Comments(1)

Emily Richardson

🙏 48 karma

Dec 30, 2024

Rated it

Memoripy is a GAMECHANGER. I see why it has so many stars on GitHub- we use it for all of our agents to ensure they are context aware! Great job 👏

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MemoriPy was manually vetted by our editorial team and was first featured on December 30th 2024.

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Pros and Cons

Pros

Open-source

Human-like memory enhancement

Adaptive capabilities

Short-term and long-term memory

Transforms repetitive systems

Facilitates intelligent assistance

Produces context-rich responses

Eliminates repetitive queries

Utilizes concept clustering

Implements memory decay

Refinement of responses

Prioritizes most pertinent information

Compatibility with Ollama

Optimizes chatbots

Facilitates construction of autonomous systems

Enhances retail recommendations

Recalls customer purchase history

Efficiency improvement

Accuracy improvement

Supports learning and recall

Effortless integration

Cost efficiency

Optimizes LLM calls

Reduces token usage

Integrates past interaction recall

Facilitates building smarter systems

View 21 more pros

Cons

Lacks clear documentation

No mention of a UI

Possible memory management issues

Scalability concerns

May lack some integrations

Dependent on platform compatibility

No versioning information

Unclear error handling

Limited use cases

Potential slow response time

View 5 more cons

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Full Stack Gen Kit

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I'm using MemoryPlugin since a year. It is brilliant how can it improve the work with LLM-s. With any LLM-s. Asad did a lot of improvement, he is open for discussion about enhancements.

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Q&A

What is MemoriPy?

Khazar Allahverdiyev

🛠️ 4 tools

wrote:

MemoriPy is an AI memory library that enhances agents with human-like short-term and long-term memory capabilities. It reduces hallucinations and improves contextual understanding by storing, clustering, and retrieving relevant information for more accurate and grounded responses.

What is the idea behind MemoriPy’s human-like memory feature?

Khazar Allahverdiyev

🛠️ 4 tools

wrote:

The idea behind MemoriPy’s human-like memory feature is to emulate the brain's ability to store, cluster, and recall information contextually. It uses semantic embeddings for short-term relevance and persistent storage for long-term context, enabling AI to reason and respond with greater accuracy and depth.

How does MemoriPy provide both short-term and long-term memory?

Khazar Allahverdiyev

🛠️ 4 tools

wrote:

MemoriPy provides short-term memory by storing recent context in in-memory structures and retrieving it efficiently using semantic embeddings and vector similarity (FAISS). Long-term memory relies on JSON-based and in-memory storage to persist and retrieve information, enabling contextual reasoning over extended periods.

What does it mean that MemoriPy can transform repetitive systems into context-aware platforms?

Khazar Allahverdiyev

🛠️ 4 tools

wrote:

MemoriPy transforms repetitive systems into context-aware platforms by enabling AI to retain and retrieve relevant information from past interactions. This allows systems to adapt responses based on historical context, reducing redundancy and improving personalization and efficiency in decision-making.

How does MemoriPy work to eliminate repetitive queries?

Khazar Allahverdiyev

🛠️ 4 tools

wrote:

MemoriPy eliminates repetitive queries by leveraging semantic memory and reasoning. It identifies and clusters previously encountered queries using embeddings, stores the relevant responses, and retrieves them when similar queries arise, ensuring efficient and context-aware responses without redundant processing.

What unique concept does MemoriPy use to enhance accuracy?

Khazar Allahverdiyev

🛠️ 4 tools

wrote:

MemoriPy enhances accuracy by leveraging semantic clustering and context-aware retrieval from its memory layers. This approach ensures that the most relevant information is used in AI responses, improving contextual understanding and reducing errors.

+ Show 14 more

How does MemoriPy use clustering and memory decay?

Khazar Allahverdiyev

🛠️ 4 tools

wrote:

MemoriPy uses clustering to group semantically similar information, ensuring efficient retrieval of contextually relevant data. Memory decay gradually removes less relevant or outdated information from short-term storage, optimizing memory usage and maintaining focus on current priorities.

Is MemoriPy compatible with platforms like OpenAI and Ollama?

Yes, MemoriPy is designed to function seamlessly with platforms like OpenAI and Ollama.

In what sort of AI-driven projects can MemoriPy be best used?

MemoriPy can be optimally used in a diverse array of AI-driven projects. These include upgrading chatbots to remember past interactions and resolve queries faster, developing AI that evolves over time by adjusting to user preferences, constructing autonomous systems that become more intelligent with each engagement, and enhancing retail recommendations by remembering customer purchase history.

How does MemoriPy upgrade chatbots' capabilities?

MemoriPy upgrades chatbots' capabilities by empowering them to remember past interactions. This allows the chatbots to resolve queries faster as they can recall prior interactions and information which may be relevant to the current query.

How can MemoriPy help develop AI that evolves over user preferences?

MemoriPy can help develop AI that evolves over user preferences by using its memory feature to recall and learn from past user interactions. This allows the AI to adjust its responses based on preferences demonstrated over time.

How does MemoriPy assist in the construction of smarter autonomous systems?

MemoriPy aids in the construction of smarter autonomous systems by providing a memory feature to AI. This allows the system to learn from each interaction, growing smarter and more efficient with each engagement.

Can MemoriPy enhance retail recommendations based on customer purchase history?

Yes, MemoriPy can enhance retail recommendations based on customer purchase history. By remembering the past purchases of each customer, MemoriPy can provide more personalized and relevant product recommendations.

What are some key features of MemoriPy?

Key features of MemoriPy include human-like memory, short-term and long-term memory, query optimization, concept clustering, memory decay, OpenAI and Ollama compatibility, ability to aid in the development of adaptive AI and autonomous systems, capability to enhance retail recommendation systems, and contribution to improving efficiency and accuracy in AI systems.

Does MemoriPy improve both efficiency and accuracy of AI systems?

Yes, MemoriPy does improve both the efficiency and accuracy of AI systems. By utilizing concept clustering and memory decay, MemoriPy refines responses and prioritizes pertinent information. This not only enhances the accuracy of AI responses but also prevents repetitive queries, thus improving overall efficiency.

Can I integrate MemoriPy seamlessly with other AI tools and platforms?

Yes, MemoriPy was designed to integrate seamlessly with other AI tools and platforms. It specifically mentions compatibility with platforms like OpenAI and Ollama.

How does MemoriPy enable learning and recall in AI systems?

MemoriPy enables learning and recall in AI systems by providing both short-term and long-term memory. This allows the AI to remember and learn from past interactions and data, and recall this information when required.

How does MemoriPy aid in making AI interactions smarter?

MemoriPy enhances AI interactions by providing AI systems with memory. This allows the AI to produce meaningful, context-rich responses by recalling prior interactions or data, making every interaction smarter.

Can I use MemoriPy for adaptive and evolutionary AI projects?

Yes, MemoriPy is well-suited for adaptive and evolutionary AI projects. It equips AI systems with memory, allowing them to adapt and evolve over time based on learned and recalled information.

Where can I get started with MemoriPy?

You can get started with MemoriPy at its GitHub page. The direct link is 'https://github.com/caspianmoon/memoripy' as provided on their website.

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