With Long-term Memory
AI Assistant.
AI memory is reshaping how smart assistants learn, adapt, and assist teams in managing knowledge more effectively. Unlike traditional AI models that process each input in isolation, AI systems with memory can retain past interactions, learn from them, and apply that knowledge to future conversations. This allows AI assistants to provide context-aware, personalized support, making them valuable tools for businesses and professionals who rely on accurate, real-time knowledge retrieval.
Tanka, an AI-powered messenger with long-term memory, takes this concept further by acting as a Chief Memory Officer. It does not just process requests—it remembers previous interactions, connects discussions across platforms like Slack, Gmail, and WhatsApp, and ensures teams have access to the right information at the right time. Instead of treating conversations as isolated exchanges, Tanka creates an ongoing knowledge network that helps teams collaborate, retain key insights, and stay organized without constant manual effort.
In this article, we will break down how AI memory works, the different types of AI memory systems, and how Tanka uses this technology to improve team collaboration, customer interactions, and knowledge management.
Types of AI memory and how they work
AI memory systems have advanced considerably, allowing for more intelligent and context-aware interactions. Below are the three primary types of AI memory and how they operate:
Short-term AI memory
Short-term AI memory, also known as working memory, refers to session-based context retention, where AI remembers inputs within a single interaction but forgets once the session ends. This type of memory is crucial for maintaining context and coherence in immediate tasks.
How it works:
AI chatbots and virtual assistants use short-term memory to maintain conversation flow within a session.
Information is temporarily stored in a buffer, allowing the AI to reference recent inputs and generate relevant responses.
Once the session ends or after a short period, this information is discarded.
Common in tools like ChatGPT (without memory), Siri, and Google Assistant.
Limitations:
The AI cannot recall past interactions once the conversation resets or the session ends.
This type of memory is limited in its ability to learn from past experiences or provide personalized responses over time.
Use case examples:
NotePin, developed by Plaud AI, exemplifies how short-term AI memory can be used to capture and transcribe conversations in real-time. It demonstrates the practical application of short-term AI memory in everyday scenarios. Users don't have to rely on human memory and take notes, it can generate bullet points and summaries of recorded conversations, facilitating quick review and information retrieval.
Related: AI Knowledgebase
Long-term AI memory
Long Term Memory (LTM) is a crucial component in artificial intelligence systems that enables them to retain and utilize information over extended periods. It allows AI to recall past interactions, connect previous knowledge with current tasks, and maintain context across multiple exchanges.
This capability is essential for creating a sense of continuity in conversations, supporting multi-turn reasoning, and facilitating lifelong learning. LTM enhances AI performance by improving accuracy, coherence, and personalization of responses.
Furthermore, it enables AI systems to build and refine a comprehensive world model, allowing them to adapt to new scenarios and provide more intelligent and contextually aware assistance.
Benefits:
Recalls and connects knowledge by retaining past interactions and linking them to present tasks, ensuring continuity.
Handles multi-turn reasoning, enabling extended conversations without losing context.
Facilitates lifelong learning, allowing AI to grow more intelligent and adaptive over time.
Improves performance by providing more accurate and relevant responses.
Personalizes responses by remembering user preferences and tailoring interactions.
Understands context by interpreting subtle cues for more meaningful conversations.
Builds a world model, creating a dynamic representation of the world to anticipate needs.
Simulates consciousness by merging past and present contexts for proactive and intuitive actions.
Enables shared memory across platforms, ensuring consistency and seamless collaboration.
Engages proactively by suggesting actions based on past conversations.
Maintains consistency in conversations by preserving context across multiple sessions.
Preserves organizational memory, aiding in knowledge retention, onboarding, and decision-making.
Memory in multi-agent AI systems
Some AI models use multi-agent architectures where different AI components work together, sharing and managing memory across tasks.
How it works:
Different AI agents specialize in storing, retrieving, and applying knowledge, improving overall decision-making.
Memory is distributed across multiple specialized components, each handling specific aspects of information processing.
Use case examples:
AI-powered customer support assistants that remember past tickets and inquiries, providing more informed and efficient support.
AI team assistants that retain company knowledge, facilitating better collaboration and decision-making across projects and departments.
By utilizing short-term and long-term memory, AI systems like Tanka can provide more personalized, context-aware, and efficient support for teams and businesses. The incorporation of shared memory in multi-agent systems allows AI to adapt to user needs, learn from past interactions, and make more informed decisions over time.
Read also: 6 Best AI Knowledge Management Tools for 2025
How Tanka uses AI memory to improve productivity
Tanka is an AI-powered messenger with long-term memory, designed to help teams retain knowledge, manage workflows, and automate decision-making. Unlike traditional AI tools that process information in isolated interactions, Tanka’s AI assistant learns from past conversations, providing context-aware support and intelligent recommendations based on accumulated team knowledge.
Launched in February 2025, Tanka is the first enterprise chat system with long-term AI memory capabilities. Its core technology, MemGraph, is inspired by neuroscience and the Thousand Brains Theory, allowing for hierarchical knowledge representation and contextual understanding. By integrating with Slack, Gmail, WhatsApp, Notion, and other platforms, Tanka creates a shared knowledge hub where teams can access relevant insights without searching through scattered information.
How Tanka uses AI memory solutions
Tanka’s AI-powered memory system transforms team collaboration by ensuring that critical insights, past decisions, and contextual knowledge are never lost. Here’s how it improves productivity and workflow efficiency:
Context-aware responses based on past interactions
Tanka’s AI assistant retains memory of past discussions, meeting summaries, and workflow details, ensuring that conversations continue without repetition or loss of context. This allows teams to:
Pick up discussions where they left off without having to recall previous conversations manually.
Use Smart Reply to generate personalized, AI-driven responses that reflect past interactions and decisions.
Reduce back-and-forth communication by surfacing relevant insights automatically.
Knowledge retention across platforms
One of Tanka’s strengths is its ability to store and retrieve information across multiple communication tools like Slack, Gmail, WhatsApp, and Notion. Instead of losing valuable insights in disconnected conversations, Tanka creates a unified knowledge base that allows teams to:
Retain, recall, and leverage historical interactions to inform present tasks, improving decision-making, collaboration, and operational efficiency.
Prevent knowledge loss as Tanka evolves continuously, adapting to changes in organizational structure, priorities, and workflows to keep its knowledge base relevant in dynamic environments.
Have on-demand information with the help of advanced retrieval mechanisms.
Personalized recommendations
Leveraging its AI memory, Tanka delivers tailored recommendations based on previous interactions, empowering more informed team decisions.
For example, it can:
Recommend relevant discussion when a specific topic is brought up.
Suggest potential collaborators for a project based on expertise and past contributions.
Provide AI-driven productivity tips tailored to team workflows.
Importantly, Tanka AI Assistant’s recommendations are also customized according to a user's role and responsibilities. This context-aware support enables teams to work more efficiently by ensuring that past knowledge is consistently applied to new challenges in a way that is relevant to each team member.
Use case: preserving institutional knowledge in product teams
When a key product manager leaves, they often take years of user testing insights, strategic decisions, and workflow knowledge with them. Without proper documentation, the new PM must spend weeks—or even months—trying to reconstruct past decisions and understand the context behind product choices.
With Tanka’s AI long-term memory, this transition becomes seamless:
Preserving institutional knowledge – Tanka continuously records and organizes past user test results, feedback patterns, and decision-making processes, ensuring no insights are lost.
AI-assisted knowledge transfer – The team admin can transfer the previous PM’s AI assistant memory to the new team member, providing them with instant access to key learnings and unresolved challenges.
Faster onboarding and decision-making – Instead of sifting through old emails and documents, the new PM can ask Tanka questions like, “What were the main user concerns in our last usability test?” or “Why did we change the onboarding flow last quarter?” and get immediate, context-rich answers.
Why choose Tanka:
In product teams, important decisions, user feedback, and strategic ideas often get buried in endless messages, emails, and meeting discussions. This makes it difficult to track progress and maintain continuity.
With Tanka’s AI long-term memory, teams can seamlessly capture, retain, and retrieve key information from daily conversations, ensuring that no critical insights are lost.
Unlike traditional AI search tools, Tanka:
Learns from past interactions to provide accurate, context-rich responses.
Preserves institutional knowledge and prevents the loss of critical company insights.
Improves decision-making by connecting relevant past discussions to current projects.
By using MemGraph and the OMNE multi-agent framework, Tanka offers superior information retrieval and reasoning compared to traditional RAG models. This makes it an essential tool for businesses looking to organize communication, preserve institutional knowledge, and improve productivity.
Limitations:
Tanka is currently in beta, with ongoing refinements to expand its AI memory capabilities and integrations. As the platform evolves, users can expect even more advanced features, ensuring that AI-driven memory becomes an even more powerful asset for teams.You might find it interesting: NotebookLM Business
Challenges and future developments in AI memory
As AI memory systems advance, they bring new opportunities and challenges in scalability, privacy, accuracy, and efficiency. Products like Tanka and Mem0 are leading the way in tackling these challenges, shaping the future of AI memory solutions. Mem0 focuses on self-improving memory layers for LLM applications, while Tanka integrates AI memory into business communication, enabling teams to retain and apply knowledge across multiple platforms.
Current challenges in AI memory solutions
Balancing memory retention and privacy
AI memory platforms store large datasets of personal and business information. Safeguarding sensitive data and ensuring compliance with regulations like GDPR is crucial. Users want the benefits of long-term AI memory without the risk of unauthorized access or data misuse.
AI models require robust security protocols to prevent breaches while maintaining access to relevant historical data.
AI memory must align with global data protection regulations such as GDPR and CCPA while ensuring usability for businesses.