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Software like Supermemory
What else does this job. Matched on what each project does, not on who links to whom.
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- Specmemsuper-agentic.aiSpecMem is an open-source memory layer designed for AI coding agents, enabling them to retain and search through specifications, decisions, and tests that are typically dispersed across a code repository. By consolidating these artifacts into a persistent, searchable context, SpecMem addresses the challenge of agents forgetting previously generated information, allowing them to recall relevant context and avoid redundant work. The platform supports multiple specification formats and coding agents, including Kiro, SpecKit, Tessl, Claude Code, Cursor, Codex, Factory, Warp, and Gemini CLI. It features adapters for parsing specs from these frameworks and agents, making it agent-agnostic and eliminating vendor lock-in. SpecMem also integrates with various vector databases such as LanceDB, ChromaDB, Qdrant, and AgentVectorDB, and supports embedding models from providers like OpenAI, Google, Together AI, and local models. Users can run SpecMem locally or connect it to cloud-based resources, and interact with it via a command-line interface or a Python API. Key capabilities include vector-based semantic search, a SpecImpact graph for mapping relationships between specs, code, and tests, and coverage validation through built-in quality rules. The platform provides a Model Context Protocol (MCP) server so agents can query and retrieve optimized context. An interactive web dashboard offers live synchronization and project health grading, while advanced features such as SpecDiff Timeline, test mapping across frameworks like pytest, jest, and vitest, coding guidelines aggregation, spec lifecycle management, Kiro session search, static dashboard export, GitHub Action integration for CI, and a streaming context API further enhance its utility for development teams. SpecMem employs a conservative quality gate for optimized skills, ensuring that only validated and improved artifacts are added to memory, with provenance tracking for each change. 0 open-source license and can be installed via pip. Its design allows users to quickly set up, scan their repositories, and begin querying their specs, with the option to export results for documentation or CI workflows. SpecMem is positioned as a framework and SDK for persistent, intelligent memory in agentic coding workflows.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- SelfMemoryselfmemory.comSelfMemory is a web-based application that enables users to store, organize, and retrieve their memories and knowledge using AI-powered semantic search. It helps individuals connect thoughts and never lose important details.
- 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.
- 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.
- smfssmfs.aismfs (Supermemory File System) presents an approach to integrating memory and semantic search capabilities directly into the filesystem, allowing agents and developers to interact with AI memory as if it were a standard directory. By mounting a Supermemory container as a real directory, smfs enables users and agents to perform familiar file operations such as ls, cat, and grep, making memory management accessible through standard terminal commands. This design eliminates the need for agents to interact with complex vector databases, external SDKs, or specialized APIs, streamlining workflows by collapsing multiple memory-related services into a single mount point. A notable feature is the semantic search upgrade to the grep command within the mount. When grep is used, it performs meaning-based searches across the contents, while grep -F retains standard exact-match behavior. The system also provides a live profile capability, where reading profile.md synthesizes a fresh digest from all stored memories each time it is accessed, rather than serving a static file. This live profile offers up-to-date context for agents, and writes to the filesystem are treated as new memories, with reads functioning as recall operations. smfs supports real POSIX mounts, utilizing NFS on macOS and FUSE on Linux, and is implemented in Rust with strict safety measures that forbid unsafe code. It does not require kernel extensions or macFUSE. The tool is resilient to network issues, supporting offline reads and synchronization through a local SQLite cache and employing exponential backoff for syncing when connectivity is unstable. Files are cloud-synced within the Supermemory container, ensuring that all agents have access to the latest information. The system is designed for a range of use cases, including legal document search, financial analysis, research and documentation, support and knowledge base management, HR operations, and personalized agent experiences. It allows agents to search across various document formats—such as PDFs, videos, screenshots, audio, and text—without requiring separate OCR, transcription, or parsing pipelines. smfs is available under MIT or Apache-2.0 licenses.
- su-memorysu-memory.aisu-memory is an open-source semantic memory engine featuring a plugin-based architecture and support for distributed storage backends like PostgreSQL, Redis, and SQLite. It is designed for AI developers building knowledge bases and retrieval-augmented generation systems.
- 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.
- AgentRecallagentrecall.cloudAgentRecall is an open-source SDK that equips AI agents with persistent, intelligent memory, allowing them to store, search, and recall information across sessions. It supports graph relationships, semantic search, and multi-agent scenarios, making it ideal for developers building advanced AI agent systems.
- Memoryportmemoryport.aiMemoryport is permanent-memory software for AI conversations, built for persistent context across sessions with any LLM. It is local-first and open source, and its core promise is that each session can continue where the last one left off. Installation is described as a one-command setup for macOS and Linux, followed by an interactive wizard for configuring integrations. Once in use, conversations are indexed locally and context is injected only when relevant. The feature set includes a local LanceDB vector index with zero cloud dependency, optional Arweave storage for permanent backup, cross-device sync, and rebuilds from the chain. It also uses AES-256-GCM encryption with Argon2id key derivation. The tool includes what it calls intelligent retrieval, using a three-gate system with query expansion so the AI receives relevant context rather than everything at once. It also supports multi-tool memory across Claude Code, Cursor, Ollama, and OpenAI, and lists integrations for Claude Code MCP Server, Cursor MCP Server, Open WebUI, an Ollama proxy, an Ollama native API proxy, a ChatGPT OpenAI proxy, and any MCP client through a standard protocol. The page also reports benchmark results, including 97.9% session recall on LongMemEval, 500 million tokens tested, under 100 ms p95 retrieval, and 0.82 recall@10. Memoryport is licensed under Apache 2.0 and is presented with a full Rust codebase that can be read, self-hosted, or contributed to. The pricing section lists a free plan with unlimited local storage, Claude Code integration via MCP, Cursor integration, Ollama and Open WebUI support, knowledge graph visualization, session analytics, and encrypted-at-rest storage. A Pro plan is also offered at $9.99 per month and adds permanent memory backup, memories available forever after cancellation, about 250 million tokens per month, cross-device sync, and cold start rebuilds from the chain.
- bettermemorypypi.orgbettermemory is an open-source, local-first memory layer for AI coding agents, offering per-hit staleness verdicts, claim-level use attribution, and episodic run-state tracking. It is MCP-native and designed for developers building autonomous AI agents.
Ranked by how close each one sits to Supermemory in the index, not by popularity. Back to Supermemory →