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Software like Memica AI
What else does this job. Matched on what each project does, not on who links to whom.
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- Memnomemno.aiMemno is an AI-powered executive assistant for everyday life. It is described as a personal intelligence assistant that connects information scattered across apps and helps with everyday coordination, using memory to surface relevant details when they are needed. The product is presented around examples such as checking when car insurance expires, finding a budget document sent to a contact, setting reminders, locating nearby coffee shops, sharing a town hall recap, and booking a table for two. Memno says it remembers everything, works around the clock, and adapts to how a person works. It handles messages, meetings, reminders, and more, and it organizes the day with smart actions and useful information. It also says it can find buried documents and past emails, and that it can get things done by text, email, or call, including across languages. Memno connects to a user’s calendar, contacts, and habits, and it also refers to connecting from a calendar to a location to deliver what is needed. The page emphasizes private, isolated, encrypted environments, and says user data never trains the models. It also states that each user lives in their own universe and that a distributed Request Engine means no single provider sees the complete picture. The service is available now on iPhone. It also presents Memno as a memory-centered system built around connected intelligence rather than a standard chatbot.
- 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.
- Mnemonic AImnemonic.aiMnemonic AI is a marketing intelligence platform that connects and unifies data from various sources to generate AI-powered customer insights. It helps marketing teams create personas, analyze customer behavior, and automate growth strategies using advanced analytics and reporting.
- 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.
- Memrausememra.comMemra is a developer API and CLI tool that offers persistent, privacy-first memory for AI agents and LLM applications. It provides long-term semantic recall, PII masking, and is EU-hosted for compliance. Memra is designed for developers building advanced agentic systems requiring reliable memory infrastructure.
- 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.
- Memerimemeri.aiMemeri is a workspace platform designed for developers who use AI coding agents like Claude, ChatGPT, and Codex. It enables multiple agents to collaborate on projects with a shared memory, visible work logs, and real-time updates. Users can connect different agents, track their activities, and steer their work in a unified environment.
- 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.
- Memorr.AImemorr.aiAI is a desktop application designed to address the challenge of context loss in extended AI conversations. Available for both Mac and Windows, the tool enables users to maintain continuity across multiple AI chat sessions by storing all conversations and contextual memories locally on their devices. 5, GPT-4, GPT-4 Turbo), Anthropic (Claude 3 Opus, Sonnet, Haiku), Google (Gemini Pro, Gemini Ultra), and Perplexity, and even use multiple models within the same session. A central feature of the application is its visual memory canvas, which occupies the majority of the split-screen interface (70% canvas, 30% chat). On this canvas, users can create, edit, and organize memory nodes, visually mapping out important information and context from their AI interactions. The branching functionality lets users explore different conversational paths without losing track of the original thread, supporting workflows that require tracking decisions, ideas, or project milestones over time. The application is positioned for a variety of users, including developers managing complex projects, writers and creators evolving content strategies, marketers analyzing campaign performance, entrepreneurs tracking startup pivots, students mastering academic subjects, and product managers overseeing feature development. AI emphasizes privacy and control, as all data—including conversations and memory structures—is stored locally, ensuring that content is not sent to external servers. Users bring their own API keys (BYOK) for the supported AI providers, which allows direct interaction with the AI services, full cost control, and access to the latest models as soon as they are released. The tool also offers export functionality, enabling users to save their chats and memories in JSON or markdown formats for external use or backup. The software is sold via a one-time purchase model for $89, which grants a lifetime license for use on up to two devices (with the ability to reassign devices as needed). The purchase includes free updates and support for one year, after which users retain indefinite access to their licensed version. AI.
- 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.
- mnemoai-assistantpypi.orgmnemoai-assistant is an open-source CLI tool that provides a local agentic AI assistant capable of learning, remembering, and interacting with users. It supports multiple AI model providers, integrates with LangGraph and MCP, and is designed for developers and technical users who want a customizable, local-first AI assistant.
- Moment AImeetmomentai.comMoment AI is an AI-powered personal knowledge assistant for individuals who need to organize notes, links, screenshots, and documents. It uses AI to extract actions, filter information, and help users make decisions from their collected data. Available as a Mac app and web extension, it targets productivity-focused users.
- Memosamemosa.aiMemosa is an AI-powered note-taking application that converts users' messy thoughts, voice memos, and written notes into organized, editable text. It features voice transcription, AI-driven summarization, and a rich text editor, making it ideal for students and professionals who want to streamline their note-taking and idea management.
- getmem-aigetmem.aigetmem.ai is a persistent memory API for AI agents. It is described as a memory layer for agent applications that need to keep track of prior conversation turns and return grounded context on each new turn, with isolated per-end-user memory and reduced context token use. The service works by taking conversation turns as input, extracting structured knowledge, and storing it in both a typed graph and a hybrid vector index. Its retrieval path is presented as LLM-free and includes heuristic decomposition, fuzzy entity matching, parallel hybrid search, bounded graph expansion, ranking, and assembly of a structured prompt block. The page also says that each response includes per-stage meta information such as latency and token count. Other named details include a 12-category taxonomy, typed facts with tone and confidence, entity canonicalization with aliases and stable IDs, and a hybrid vector index with dense and sparse search plus a 17-field payload index. getmem.ai is shown as a tool for developers building AI agents and apps that depend on persistent user context. The page names OpenAI, Anthropic ChatGPT apps, LangChain, and Vercel AI SDK in an integrations section, and it presents two endpoints as the full integration surface: POST /v1/memory/ingest and POST /v1/memory/get. Ingestion is asynchronous and returns 202, while retrieval is synchronous. The page also notes idempotent ingestion and says retrieval is under 300 ms, with examples of tenant filtering and per-end-user isolation. Pricing is represented by a free credit to start. The page also includes a GitHub link and login link. It refers to a no-training API and mentions audit logging and per-patient scoping in a healthcare example, but those details are presented in the context of example use cases rather than as a separate product specification.
- Memo AImemo.cardsMemo AI is an AI-powered knowledge workspace that allows users to upload PDFs, videos, websites, and text, then generate flashcards, notes, practice tests, and guided walkthroughs. Designed for students, educators, and professionals, it streamlines learning and information management with AI-driven tools. Available for desktop platforms.
- 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.
- Memoir Aimemoir-ai.devMemoir is memory infrastructure for AI agents. It presents itself as Git for AI memory, with a local-first, taxonomy-structured, Git-versioned store that lets agents explain, rewind, and branch their memory. The product is framed around problems such as context contamination, token rent, and memory drift. Its core capabilities include recall by path rather than similarity, time travel to reproduce bugs, and branching to test risky strategies. The page also describes automated branch shadowing, where memory branches follow git branches automatically via Claude Code hooks, and a merge workflow that moves lessons from a feature branch into the main knowledge base. Memoir also supports semantic path retrieval with hierarchical paths such as api.v2.auth. The text says it ships as a Claude Code plugin with automatic skills and hooks that follow git workflow. Memoir is built for coding agents and custom runtimes. It is shown with plugins or integrations for Claude Code, Codex, OpenCode, Hermes, OpenClaw, and LangGraph, and it also offers a Python SDK, a CLI, and an MCP Server for any MCP host. The installation examples include pip install memoir-ai for Python, and the page says the Python install is for the SDK or CLI. It also describes slash commands and hooks for session memory capture, context injection on start, and explicit recall or onboarding actions. The page lists Apache 2.0 licensing and Python 3.10+ support. It also refers to open-source coding agent support in the plugin ecosystem and to community-maintained plugins for some runtimes.
- The private AI that remembersanuma.aiAnuma is a privacy-first AI assistant that brings together multiple AI models, including ChatGPT, Claude, and Gemini, into a single platform with unified memory. It offers personalized, context-aware assistance for a variety of personal and professional tasks, ensuring user data privacy.
- Memexmemexlab.aiMemex is an open-source AI journal app designed for iOS and Android, focusing on privacy and local-first data storage. It enables users to capture text, photos, voice notes, reminders, and daily fragments, automatically organizing these entries into structured timeline cards using a multi-agent AI system. The platform is intended for individuals seeking a private and intelligent way to manage their personal records and gain insights from their daily activities. The app supports multi-modal recording, allowing users to input text, photos, and voice in a single flow, with features such as long-press audio recording, auto EXIF extraction, on-device OCR, and image labeling. Memex’s AI agents categorize records into various types of timeline cards, including tasks, events, metrics, people, places, galleries, articles, and transactions, among others. Its insight engine surfaces patterns across entries as trend charts, radar maps, timelines, highlight quotes, composition breakdowns, and narrative summaries, helping users discover connections and trends they might not have noticed otherwise. All records are stored locally as interconnected Markdown files and a local SQLite database, ensuring data portability and zero vendor lock-in. Users can export their data with one click and back up or sync via iCloud Drive, a custom folder, or app storage. Memex operates on a bring-your-own-LLM model, supporting connections to more than a dozen large language model providers, such as OpenAI, Claude, Gemini, Ollama, and others. Prompts are sent directly from the device to the chosen provider, and the platform does not access user data or API keys. Users can also build custom AI agents within the app, leveraging event-driven triggers, custom prompts, JavaScript execution, and inter-agent workflows, all running on their device. 0 license and is available through the App Store, Google Play, and GitHub. Its open-source nature and local-first approach make it distinct among journal apps, offering privacy-conscious users control over their data and AI integrations.
- Memori Labsmemorilabs.aiMemori Labs provides an agent-native memory infrastructure designed for production AI systems. The platform offers a layer that is agnostic to large language models (LLMs), enabling agent execution and conversations to be transformed into structured, persistent state. This infrastructure is intended to help AI agents and their developers capture, organize, and recall information from interactions and documents efficiently, without the need for additional external services. A core feature of Memori is its ability to automatically capture each turn in a chat and classify the information into facts, preferences, rules, and summaries. Users retain control over what data is stored, its retention duration, and storage location. When context is needed for prompts, the system retrieves only the most relevant information across conversations and documents. Memori enhances search accuracy through selective semantic search, enriching queries with semantic context to improve results and reduce token costs. Every recall provides an explanation of why specific information was included, offering traceability by entity, time, and source. 95% accuracy rate on the LoCoMo benchmark and a 95% reduction in token usage compared to full-context retrieval. Developers can integrate Memori with a single line of code using its SDK, which manages model calls and callbacks with zero configuration. Memori Cloud allows instant storage and search of memories, requiring no additional setup. The tool also features an interactive memory graph to visualize relationships and analytics to monitor memory creation, recall usage, and cache performance. Memori is positioned to help enterprises reduce costs by over 95% through tokenless recall and structured memory, and aims to deliver fast responses by caching concise snippets. The platform supports secure memory for payments and sensitive information, with PCI and SOC 2 compliance. It is designed for developers and teams building AI agents, and has been noted for potential integration with ecosystems such as MongoDB. The service emphasizes explainable results, intelligent routing, and instant context from historical content, catering to the needs of production-scale AI applications.
- Memos Airecordergo.appMemos Ai is a progressive web app for personal note-taking, allowing users to create and organize text, image, handwritten, and audio notes. It offers synchronization across devices and offline access, making it suitable for users who need a versatile and accessible notes solution.
- MemBrainmem-brain.ioMemBrain is a persistent memory infrastructure designed for AI agents, offering a self-evolving knowledge graph that enables long-term storage, traversal, and recall of reasoning paths and semantic relationships. The platform addresses the challenge of maintaining continuity and context for AI agents over extended periods, allowing them to recall information and reasoning across weeks or months without session resets. Rather than simply storing documents, MemBrain parses incoming events, calls, and state changes into a neural graph composed of semantic nodes, edges, and type definitions, supporting autonomous learning, linking, and pruning of context based on actual reasoning patterns. Key features include an event observation and parsing engine that transforms raw data and tool calls into typed nodes, a causal reasoning engine that identifies connections and traces causal paths between temporal events, and a graph traversal system for memory recall that returns logical paths through the memory graph. The platform supports interactive exploration of stored memories as a graph, with capabilities such as Graph Search and Regex Scoping for finding and highlighting nodes. MemBrain's architecture is engineered for agentic speed and reliability, with features like pre-cached logic for rapid response times, surgical retrieval to prevent context window bloat, and temporal accuracy that distinguishes structural shifts from temporary anomalies in agent behavior. MemBrain integrates natively via the Model Context Protocol (MCP), enabling compatibility with any large language model (LLM). Users can connect through exposed interfaces such as search_narrative, observe_state, and evolve_memory for querying, reading, and writing to the memory graph. The platform is accessible via API and command-line interface (CLI), and offers a read-only demo graph for interactive exploration without an API key. Pricing is provisioned in Indian Rupees (INR) and is structured into four tiers: a Free plan for individuals with 1,000 total memories and API/MCP access, a Pro plan for developers with 10,000 total memories, a Scale plan for high-volume users with unlimited total memories and increased weekly creation limits, and an Enterprise plan with custom limits, dedicated support, security audits, and white-labeling options. The product is developed by Alphanimble.
- MemDatamemdata.aiMemData is a decentralized memory infrastructure designed for autonomous AI agents and developers seeking persistent, searchable storage of unstructured data. The platform enables agents and applications to ingest files—including PDFs, images, screenshots, audio, and text—automatically handling optical character recognition (OCR) for images and PDFs, as well as audio transcription. Uploaded content is chunked, embedded, and indexed without the need for manual configuration or tuning, allowing users to query their data in natural language and receive relevant context with source citations. MemData is accessible via a REST API and supports integration with AI tools such as Claude, ChatGPT, Gemini, Cursor, and automation platforms like n8n and Make. Developers can interact with the service using API calls or the MCP server, and file ingestion and querying are demonstrated through simple command-line examples. The platform supports a variety of file types, including PDFs, PNG, JPG, MP3, WAV, M4A, and text files, and provides persistent, account-isolated storage. 3 and at rest with AES-256. Data is stored on US-based infrastructure using SOC 2 compliant providers, and users retain ownership of their data, which is not used for model training or sold. Data can be deleted at any time through the API or dashboard, with purging completed within 24 hours. MemData offers a tiered pricing model. The Free plan includes 100 MB of storage, 250 queries per month, a 10 MB file size limit, and access to OCR, audio transcription, and both API and MCP interfaces. The Pro plan, at $29 per month, expands storage to 10 GB, allows 10,000 queries per month, and increases the file size limit to 100 MB, adding priority support. The Scale plan, at $99 per month, provides 100 GB of storage, 50,000 queries per month, a 500 MB file size limit, priority support, and custom integrations. No credit card is required to start, and users can upgrade as needed. Positioned as a complete memory pipeline rather than just a vector database, MemData is built for AI builders and agents requiring long-term, semantic memory and context retrieval capabilities.
- memicmemic.aimemic is an open-source Python SDK for the Memic Context Engineering API, providing tools for context-aware embeddings and semantic search. It is designed for developers building retrieval-augmented generation (RAG) and semantic search solutions.
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