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Software like Memobase
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- 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.
- 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.
- AgentMemagentmem.devAgentMem is a memory layer for AI agents. It is built for developers who want agents to keep context instead of starting from zero, and it describes its purpose as giving persistent, searchable memory that can sync across frameworks including LangChain, CrewAI, OpenAI, Claude, AutoGPT, and LlamaIndex. The product stores and retrieves memories through an API. Its listed capabilities include semantic search, which finds memories by meaning rather than keywords and is powered by vector embeddings, and cross-agent sync for sharing context between agents. The page also says memories are persistent and survive restarts, that the API is simple to use with an API key, and that memories are auto-embedded when stored. A short code example shows calls for storing a memory, searching memories, and syncing across multiple agents. AgentMem’s production-oriented features include fast API response times under 100 ms, edge caching, API key authentication, tenant isolation, encrypted-at-rest storage, usage analytics for memory usage, search patterns, and agent activity, and a global CDN setup. It is deployed on Railway with a Cloudflare proxy. The page also includes a worked example of registering an agent through a POST endpoint and then using POST endpoints to store memories and search them. Pricing is presented in three plans. A free plan costs $0 per month and includes 10,000 memories, semantic search, support for three agents, and community support. Pro costs $29 per month and adds unlimited memories, unlimited agents, cross-agent sync, priority support, and usage analytics. Enterprise costs $199 per month and adds a self-hosted option, custom integrations, an SLA guarantee, and dedicated support. The page identifies AgentMem as being built by Gritza and says it is in public beta.
- 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.
- 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.
- 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.
- 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.
- 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.
- MEMANTOmemanto.aiMemanto is an open-source, on-premises memory agent designed to provide persistent memory for AI agents. It addresses the challenges of agent memory retention by enabling agents to remember and recall information across sessions, minimizing the need to re-explain codebases or lose context between interactions. The tool is compatible with a range of AI agents, including Claude Code, Cursor, Codex, and more than 14 others, and is built on an information-theoretic search engine that delivers sub-90ms recall latency. Key features include instant ingestion of memories, deterministic search, temporal queries, built-in retrieval-augmented generation (RAG), conflict resolution, autonomous categorization into 13 semantic types, and verifiable sources for every memory entry. Memanto prioritizes freshness, ensuring new facts outrank outdated information, and resolves contradictions as they arise. The system compresses data at a 32x rate and offers confidence scoring, daily summaries, and cross-platform compatibility. Local embeddings and answers are generated via Ollama models, ensuring that no data leaves the user's machine. Memanto is delivered as a Python package installable via pip, with a command-line interface for agent management, memory storage, and retrieval. It can be deployed using Docker on the user's local machine, with no need for API keys, external vector databases, or backend services. Users can also access an interactive local web dashboard to manage agents and memories, view conflicts and connections, and try a live demo on localhost. The platform integrates with a broad array of AI development tools and frameworks, such as VS Code, GitHub Copilot, Gemini, Hermes Agent, CrewAI, LangChain, LangGraph, LlamaIndex, and n8n. Memanto is available 100% free of charge and is open source, providing developers with a privacy-focused, self-hosted solution for persistent agent memory without recurring costs or reliance on cloud infrastructure.
- 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.
- 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.
- Memoraememorae.aiMemorae acts as a memory layer above all your apps, consolidating reminders, lists, and briefings into a single, organized system. It helps users manage information overload by unifying context and providing actionable insights across platforms. Suitable for professionals seeking better productivity and organization.
- Supermemorysupermemory.aiSupermemory provides a hosted context and memory platform for AI agents, including persistent knowledge-graph memory, hybrid RAG retrieval, user profiles, connectors, extractors, and structured context. It offers APIs, developer plugins, MCP support, and a personal second-brain app.
- Memcode AImemcode.aiMemcode is a terminal-based coding agent that maintains a persistent, evolving model of your entire codebase across sessions. Unlike typical chat wrappers that start from scratch each time, it remembers architecture decisions, prior changes, and context, allowing it to continue work efficiently without re-analyzing the full repository. It runs as a fast, single-binary TUI with no browser or IDE dependency. It is aimed at developers who want an AI pair programmer that truly understands their project's history and grows more effective 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.
- Claude-memcmem.aiClaude-mem and cmem provide an open-source memory layer for AI agents, enabling persistent, structured recall of agent actions and decisions across sessions, machines, and development environments. This system addresses the challenge of AI agents losing context between sessions by capturing every decision, bugfix, and dead end as structured observations in a temporal database, allowing agents to resume work with full historical context regardless of where or when they last operated. The core offering consists of the claude-mem open-source engine, which can be installed with a single command and requires no configuration or account to begin. This engine writes observations automatically as agents work, storing them in a database that supports both full-text and recency-based hybrid search. The system is designed for compatibility with a wide range of agent frameworks, IDEs, and Model Context Protocol (MCP) clients, including named integrations such as Claude Code, Cursor, Windsurf, OpenCode, Codex CLI, and Gemini CLI. Developers can run claude-mem locally or use CMEM Cloud, which provides a cloud sync layer that mirrors the local database and offers a private MCP endpoint for access from any machine or agent. CMEM Cloud is available in early access and is free during this period. 0 license. The platform emphasizes privacy and flexibility, supporting offline-first operation and end-to-end private access via personal MCP links. Teams can share a single memory across multiple agents and editors, facilitating collaborative development and long-term traceability of project decisions and changes. The system's hybrid search enables agents to recall relevant information in milliseconds, even from projects that have not been touched in months. By integrating directly with agent frameworks and development tools, claude-mem and cmem streamline the process of building and maintaining persistent AI agent memory, reducing the need for manual context management and enabling agents to maintain continuity across diverse workflows.
- Memseekmemseek.aiMemseek is an open-source context engine for AI agents that records observations, derives knowledge, and assembles cited context at prompt time. It runs locally with Postgres and Docker, supports versioned YAML memory designs and MCP tools, and is intended for developers building persistent agent workflows.
- 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.
- 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.
- memsproutmemsprout.comMemsprout provides a shared brain for a team's AI agents and engineers. Individuals capture knowledge once, which is scoped into Spaces and delivered via an MCP server to tools like Cursor, Claude Code, GitHub Copilot, and ChatGPT. It eliminates context gaps between team members and their AI tools with live, authenticated memory retrieval.
- ClawMemclawmem.aiClawMem is an open-source framework that provides a shared memory layer for AI agents, enabling context and memory to persist across sessions and agent handoffs. It supports integration with multiple agent frameworks and the Model Context Protocol (MCP), making it valuable for developers building complex agent systems.
- Mnemoversemnemoverse.comMnemoverse is a persistent memory API for AI agents. It is built to let a preference, decision, or lesson be written once and recalled across tools, with a single API key connecting Claude Code, Claude Desktop, Cursor, VS Code, Windsurf, ChatGPT, Python, and REST. The service describes itself as memory for AI agents rather than a vector database. Its memory system uses Hebbian associations and Rescorla-Wagner-style outcome feedback, with HDBSCAN consolidation and Von Restorff-based forgetting. The page also describes a write-learn-recall loop: write stores a preference, decision, or lesson; learn updates valence from reported outcomes; and recall uses natural-language queries, learned associations, and valence-boosted ranking to surface what mattered before. It says an importance gate filters noise, look-alike memories can be merged, and memories that lead to good outcomes rank higher over time. For integration, Mnemoverse offers an MCP server through the @mnemoverse/mcp-memory-server npm package. That server exposes six tools: memory_write, memory_read, memory_feedback, memory_stats, memory_delete, and memory_delete_domain. The page lists support for MCP-compatible clients including Claude Code, Claude Desktop, Cursor, VS Code + Copilot, and Windsurf, and also mentions a VS Code Marketplace listing, a PyPI package, and the official MCP Registry. Pricing is public on the site. A free tier costs $0 and includes 1,000 queries per day, 10,000 atoms, and 60 requests per minute, with full API access and the MCP server and no credit card required. Paid plans shown are Pro at $29 per month and Team at $149 per month, and an Enterprise option is offered with custom terms, including SSO/SAML, audit logs, SLA, data residency, and BAA/SOC 2 on request. The page also says early-stage startups can start on Team free on signup, with six months if approved.
- mymem0rypypi.orgmymem0ry is an open-source personal memory system for AI coding agents, providing offline semantic search, cross-agent handoffs, and zero API key requirements. It is designed for developers building autonomous AI agents that need persistent, shareable memory.
- 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.
- DeepMemodeepmemo.ioDeepMemo is an AI-powered second brain for saving web highlights and finding them later by meaning. It is built for people who want to keep track of things they read or think about, including students, writers, creators, researchers, and other curious minds. Its core workflow starts in the browser: a Chrome extension lets users highlight text on any webpage, then right-click or use Alt+Shift+S to save it to DeepMemo. Each save captures the highlight, surrounding text, source URL, a screenshot, AI-generated tags, and a semantic embedding. The service then uses semantic search so memories can be found by concept rather than by keywords. Users can also ask questions in natural language and get answers with citations back to their saved content. The site says DeepMemo also connects saved items to one another, and it lists a coming-soon voice feature that will transcribe spoken thoughts and turn them into searchable memories. DeepMemo describes itself as a knowledge layer for memories and ideas, with search, chat, and connection-making over a personal collection of saved material. The page frames it as a way to avoid losing useful notes, web content, and half-remembered ideas, and it specifically mentions use cases for lecture notes, swipe files, papers, and web research. It is delivered through the Chrome extension and a hosted version, and it can also be self-hosted for full control. The service says user data is encrypted in transit and at rest and is not used to train AI models. Pricing is simple: a free plan is available and includes 100 memories with no credit card required to start. Paid Pro and Business plans are also listed, with Pro allowing 5,000 memories and Business offering unlimited storage.
- 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.
- MemoraX Codememorax.netMemoraX Code is a memory plugin for AI coding agents that stores and recalls project context, repository knowledge, engineering decisions, and personal procedures. It integrates with Codex, Claude Code, and other supported agents through a command-line setup and helps developers resume work across sessions.
- MemoVmemov.aiMemoV is an AI-assisted version control platform designed to manage and enhance the coding memory of AI coding agents. It addresses the challenge of tracking prompts, agent plans, and code changes without requiring traditional manual commits, allowing teams to maintain a clean git history while capturing all relevant coding context. By integrating with various AI coding agents via the MCP protocol—including tools like Claude Code, Cursor, CodeX, Windsurf, and others—MemoV enables seamless collaboration and context sharing across different environments. The platform automatically traces prompts, code iterations, and agent decisions, creating a detailed history timeline of coding activities. This traceability supports lossless rollback, allowing users to revert to any point in their coding journey and explore alternative development paths. Abandoned changes are preserved in the history, providing a comprehensive record of all decision points. MemoV also auto-generates skills from the coding history, extracting patterns of success and highlighting past issues to help AI agents learn and avoid repeating mistakes. These skills can be reused by description, which optimizes token usage during iterative development. MemoV features a wiki that transforms coding history into living documentation, ensuring that records are always current with the latest changes. The platform supports cross-agent context sharing, so teams can leverage a unified memory across multiple AI coding tools. Real-time code review is available before pull requests, helping teams align intent and prevent unwanted changes from AI agents. Additionally, MemoV offers the ability to query multiple large language models—including ChatGPT, Claude, Grok, and Gemini—simultaneously for faster debugging with unified context. Designed for teams working with AI coding agents, MemoV aims to streamline collaboration, documentation, and code evolution tracking. The service is accessible via a web platform, and users can try it for free.
- 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.
- 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.
- supermempypi.orgsupermem is an open-source command-line tool that provides persistent AI memory using a four-tier retrieval system, integrating SQLite FTS5, graph, vector, and LLM agent layers. It is designed for AI developers and researchers seeking advanced memory and retrieval capabilities for language models.
- mempalace-aipypi.orgmempalace-ai is an open-source CLI tool that adds memory and retrieval capabilities to AI agents. It mines projects and conversations into a searchable database, supporting vector-based retrieval without requiring an API key.
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