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Software like Memorr.AI
What else does this job. Matched on what each project does, not on who links to whom.
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- Memobasememobase.aiMemobase is a persistent AI memory and context-continuity tool for agents and other AI tools. It is described as a synaptic layer for AI agents, intended to avoid the reset that happens when each new session loses earlier context, architecture, style, and progress. Its core architecture centers on passive context capture and a background Dream Phase that distills noisy session logs into durable, high-signal rules and project insights. Memobase also uses deterministic HTTP hooks for passive intelligence, hybrid graph retrieval that combines vector similarity with Knowledge Graph relationships, and an MCP-native protocol. The site says it works natively with Claude, ChatGPT, and Cursor. It also includes an insight dashboard for visualizing a knowledge graph and vector space, managing API keys, and monitoring usage in real time. The Quick Setup section says it can be added to an AI assistant with no code required, and the CLI supports a private local mode with SQLite as well as cloud sync for cross-device access. The CLI examples include installing memobase-cli, starting a local memory server, logging in to a cloud account, auto-configuring Claude Code hooks, scanning a repository for project IQ, and pushing local memories to the cloud. The page presents Memobase for developers and for everyone else. For developers, it offers the CLI and guidance for adding the Memobase MCP server to an AI configuration. For other users, it offers a one-click flow and drag-and-drop history import to build a knowledge graph. The setup instructions also show integrations with Claude and Claude Desktop, including connector setup, an API key workflow, and hooks for events such as UserPromptSubmit, Stop, TaskCompleted, and SessionEnd. Pricing is listed in monthly plans. Free is $0 forever and includes 500 credits per month, semantic vector search, per-user memory isolation, and community support. Pro is $9 per month and includes 5,000 credits, a monthly usage dashboard, email support, and priority response. Unlimited is $29 per month with unlimited credits, dedicated support, SLA availability, and custom onboarding. The page also states that PostgreSQL Row-Level Security is used for memory isolation and that self-custody options are on the roadmap.
- Memica AImemica-ai.comMemica AI is an AI memory assistant for remembering, organizing, and retrieving chats, notes, and ideas. It presents itself as a personal memory companion and a mind library, aimed at helping people recall information from earlier conversations and keep ongoing context over time. Its core features include an AI-powered memory system, multi-model chatbots, and a personal knowledge base. Memica AI says it saves conversations, notes, and ideas for recall; switches between advanced AI models for brainstorming, learning, and productivity; and turns conversations into structured, searchable knowledge. It also includes privacy and security controls, with memories kept encrypted and private, and a productivity integration described as supporting meeting summaries, task management, and collaboration. The memory system keeps recent conversations in full detail, summarizes older memories, and organizes information by time, topic, and relevance. The system is described as dynamic and evolving, and each new interaction updates and refines its understanding. Memica AI also describes several use cases: daily journaling, continuous conversations, project memory, personal growth, and smart recall of past topics, quotes, or ideas. The page names individuals in its testimonials, including a writer, a product designer, a student, and a developer, which suggests use across personal and professional note-keeping and recall. It is described as a cloud-stored private AI memory system accessible only to the user. The page identifies the product as an AI memory assistant and an AI chat product.
- Mem0mem0.aiMem0 is an infrastructure platform that provides persistent memory for AI agents and applications. It enables context retention across sessions, making it easier for developers to build smarter, more personalized AI systems. Designed for integration via API and SDK.
- AutoMemautomem.aiAutoMem is a persistent memory layer designed for AI agents, enabling them to recall both facts and their context rather than starting each session without memory. The tool addresses the challenge of agents forgetting previous interactions by capturing and organizing relevant information as users work, allowing for more effective and context-aware recall in subsequent sessions. AutoMem integrates into agent workflows by providing a memory architecture that combines a knowledge graph for relationships and a vector index for semantic meaning. The platform stores every memory in a graph structure, mapping out entities, relationships, and temporal data using FalkorDB, while also leveraging Qdrant for vector-based semantic search. This hybrid approach allows agents to retrieve not only semantically similar information but also the specific threads or contexts to which that information belongs. AutoMem consolidates new memories in the background, clustering related ideas, strengthening frequently accessed connections, and allowing irrelevant data to decay over time, resulting in increasingly relevant and refined recall. AutoMem is compatible with a range of agent clients and platforms, including Claude, Cursor, ChatGPT, Codex, and any client supporting the Model Context Protocol (MCP). It can be deployed locally via Docker, as a managed cloud service through Railway, or self-hosted within a user's own infrastructure, including Kubernetes environments. All deployment options expose the same MCP endpoint, ensuring consistent integration regardless of setup. The tool supports macOS, Linux, Windows (WSL2), and is accessible from both desktop and mobile clients that are MCP-compatible. The software is open source and distributed under the MIT License. AutoMem has been benchmarked using the neutral Agent Memory Benchmark (BEAM), where it achieved a high accuracy rate and was ranked second among competitors. Its design is informed by research focused on enhancing recall for AI agents, ensuring that memory compounds and becomes more useful over time.
- MEMMmemm.devMEMM is an open-source, AI-native application designed to serve as a persistent, structured memory system for AI tools. It addresses the challenge of AI "amnesia," where large language models and AI assistants repeatedly lose context between sessions, requiring users to re-explain information and maintain redundant knowledge across different tools. MEMM captures and organizes reasoning, conventions, and project knowledge in plain text Markdown files with YAML frontmatter, making the memory transparent, editable, and versionable by the user. The platform employs a scoring engine based on six signals—BM25, semantic similarity, graph relationships, recency, importance, and frequency—to rank and tier memories for each query. This approach aims to deliver high retrieval precision and context accuracy, while reducing token usage and latency. MEMM's engine operates with sub-millisecond query latency, and its tiered memory system ensures that only the most relevant information is injected into AI queries, avoiding overstuffed or irrelevant context. A governance layer tracks the health of the memory, surfacing stale entries, contradictions, and redundancies, and providing suggestions for consolidation and improvement over time. MEMM is designed for engineers and users who work with AI tools such as ChatGPT, Claude, Cursor, Codex, and local LLMs, allowing them to connect their AI assistants to a single source of structured knowledge via an MCP server. This eliminates the need to manually synchronize knowledge across multiple platforms and provides a unified, evolving memory accessible to all connected AI tools. The system supports categorizing knowledge as entities, concepts, sources, or syntheses, enabling AIs to reason over structured ontologies rather than flat text. The application is available for Mac, Windows, and Linux, and is built to be local and portable, ensuring that all knowledge remains owned and controlled by the user. MEMM does not rely on databases, embeddings, or black-box retrieval, instead prioritizing transparency and user ownership. Its open-source nature and focus on context engineering position it as a tool built specifically for the needs of the AI era.
- MemKeepermemkeeper.euMemKeeper is a browser-based tool designed to help individuals understand what information leading AI platforms—specifically ChatGPT, Claude, and Gemini—retain about them. By importing data exports from these services, users can view, compare, and analyze the memories each AI has accumulated, highlighting both shared and unique details across platforms. The tool addresses the challenge that, while AI systems remember user interactions, they typically do not display this information transparently to users. All data processing in MemKeeper occurs locally within the user's browser. The tool does not upload any information to external servers, ensuring that user data remains private and secure. Users can paste summaries or full data exports directly into the interface, and MemKeeper instantly updates a side-by-side comparison, showing differences and overlaps in what each AI remembers. The tool supports ZIP and JSON exports from the respective platforms, and guides are provided for obtaining these exports under data protection laws. MemKeeper requires no installation or account creation. The user's data vault is stored in the browser's IndexedDB, and the comparison engine operates deterministically using normalized text and a difflib ratio, without invoking embeddings or large language models. The engine is a TypeScript port of AgentKeeper and is open source under the MIT license. MemKeeper is built in the EU and is a product of ThinkLance AI. This tool is particularly useful for individuals concerned with privacy and transparency in their AI interactions, giving them direct insight into the personal information stored by different AI platforms. Its open-source nature and in-browser operation emphasize user control and data sovereignty.
- MemMachinememmachine.aiMemMachine is an open-source memory layer built to enhance advanced AI agents by enabling them to learn, store, and recall data and user preferences across sessions. Its primary function is to transform AI-powered applications, such as chatbots and assistants, into context-aware and personalized agents capable of delivering more precise and meaningful interactions. By persisting memory across multiple sessions, agents, and large language models, MemMachine helps applications build evolving user profiles that inform future responses and actions. The platform is designed to support sophisticated personalization and context retention. It features two distinct types of memory: Episodic Memory, which captures conversational context, and Profile Memory, which stores long-term user facts and preferences. These memory types allow agents to recall relevant information, enabling them to provide tailored responses and manage complex, long-running workflows. For example, MemMachine can be used in healthcare AI assistants to remember patient preferences and history, or in team collaboration tools to deliver proactive, context-aware insights that improve with each interaction. MemMachine is accessed through a RESTful API, a Python SDK, or an MCP Server, providing flexibility in how developers integrate memory capabilities into their AI agents. The memory data is persisted to databases, supporting robust and reliable storage of user and interaction data. The platform is suitable for engineering teams and developers building AI agents that require persistent, context-rich memory to support personalized and intelligent behavior. As an open-source solution, MemMachine is available for integration into a variety of AI-powered applications. Its architecture and features are designed to abstract complexity while allowing flexibility for developers to use components independently. The tool is positioned within the class of memory infrastructure solutions for AI agents, focusing on enabling context-aware, personalized, and sophisticated automation in AI-driven systems.
- CoreMemcoremem.appCoreMem is a context management platform for loading a user’s stored context into AI tools, editors, agents, and share links instead of re-explaining it at the start of each session. It stores that context as mems, which are named collections of files, documents, and notes. Its sharing methods include direct integrations, public URLs, scoped share links, and MCP. The page also says that agents can propose updates to mems, but each change is approved by the user before anything is written. For AI toolmakers and agent workflows, CoreMem provides AI-readable documentation at /coremem/hi and a plain-text summary file at /llms.txt. The service exposes an MCP server at api.coremem.app/api/mcp, and mem or profile pages use coremem.app/<username/<slug while scoped share links use coremem.app/s/<token. The product links include Get started free, Sign in, and Pricing, indicating a free entry point and a pricing page. CoreMem describes itself as a context management platform and is presented as a tool for sharing preferences and background context with AI systems.
Ranked by how close each one sits to Memorr.AI in the index, not by popularity. Back to Memorr.AI →