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Software like Mnemoverse
What else does this job. Matched on what each project does, not on who links to whom.
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- Mnemo Cloudmnemocloud.comMnemo Cloud is an open-source framework that provides persistent memory and retrieval-augmented generation (RAG) capabilities for AI agents. It supports the Model Context Protocol (MCP) and is designed for developers building LLM-based applications that require scalable, persistent memory. The project is MIT-licensed and integrates with agent frameworks.
- 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.
- 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.
- Mnemosynemnemosyne.siteMnemosyne is a native AI memory system for Hermes Agent and other AI agents. It is described as a universal memory layer for AI agents that need fast, reliable, persistent memory, and it is built around SQLite with a zero-dependency design. The system emphasizes local storage and low-latency retrieval. It uses direct SQLite access for sub-millisecond queries, and the page states that all data stays on the user’s machine or in the browser in the demo. Mnemosyne also presents itself as 100% local and private, with no cloud services, no external services, and no network roundtrips. A browser demo is available, but it is explicitly a client-side simulation rather than the real install. Its named features include native vector search through sqlite-vec, hybrid ranking that combines vector similarity, full-text search, and importance scoring, and BEAM, a three-tier memory architecture with working memory for hot context, episodic memory for long-term storage, and a scratchpad for reasoning. It also supports automatic consolidation through sleep cycles, real-time incremental updates via DeltaSync, streaming results as they arrive, and ignore_patterns for filtering noisy or irrelevant content. The page says it works offline and includes examples of using remember and recall, and it also lists compatibility with Hermes, Claude Code, Cursor, Codex, OpenWebUI, and OpenClaw + MCP. Mnemosyne is delivered with one pip install, and the install command shown is mnemosyne-memory. The page says there are no configuration files, no environment variables, and no cloud accounts required. It is free and open source, and it is now an OpenSource Collective on OpenCollective. The bottom-line comparison section describes the tool as intended for individual developers and local agents, and it notes that users manage their own backup as a single SQLite file.
- Mnemexamnemexa.comMnemexa is an intelligent memory operating system for AI systems. It is described as the brain for an AI agent, meant to keep the agent from starting from zero in each session, repeating questions, contradicting itself, and accumulating noisy or duplicate information. The product centers on memory handling for agents rather than simple storage. It says it remembers the right things, forgets noise automatically, and gets smarter with every session. Named capabilities include a noise filter, deduplication, importance-based decay rules, memory compression, and intelligent query routing. The page also describes a self-optimizing system that scans and diagnoses memory pools automatically, plus a Memory Health Dashboard that tracks stale memories, duplicates, never retrieved items, overlong entries, and optimized memories. An Automatic Recommendation Engine surfaces one-click fixes such as archive, merge, split, and rewrite. The compression service is said to cluster near-duplicates into one summary and reduce storage by 40–60% without information loss. Retrieval is described as intent-aware, with different handling for recency, persistent policy, and exact facts. Mnemexa supports connection through REST API, MCP, and any AI agent. Setup is presented as taking seconds, through one prompt or one command. The page names prompt and command setup, MCP, a Python SDK, and REST API. It also says an agent can paste into OpenClaw, Claude Code, or any AI agent. For terminal use, it shows an npx command for an MCP package, and for Python it offers installation from PyPI with a native client for backend code, scripts, and RAG pipelines. The REST API example says it works from any language, any framework, and any agent runtime. The pricing shown is a free start with no credit card required. The page also refers to a 53-workspace production context and presents usage claims such as lower token costs, faster retrieval, and less memory noise. It names business developers, enterprises, agencies, AI sales agents, and customer support teams as intended users, and also references use across research, sales, support, engineering, finance, HR, legal, and marketing agents.
- mnemosmaking-minds.aiMnemos is a local-first memory framework designed specifically for coding agents, with a focus on reliable, scoped memory management. The tool addresses the challenge of agent memory drift and scope confusion by keeping project, workspace, and global memory partitions separate, ensuring that knowledge relevant to each context remains distinct and does not accumulate contradictions. Mnemos is intended for solo coding-agent workflows, where maintaining accurate and adaptive memory across projects and sessions is critical. The platform operates using a single local SQLite file for persistence, allowing memory to survive restarts while remaining compact and efficient. It features a guided user interface (mnemos ui) for setup and host configuration, making operational readiness and host integration more accessible. Mnemos integrates with Claude Code, Claude Desktop, generic Model Context Protocol (MCP) hosts, and provides documentation for Codex setups. Its architecture is inspired by neuroscience, employing modules such as a Surprisal Gate for predictive coding, Mutable RAG for dynamic memory reconsolidation, an Affective Router for state-dependent retrieval, a Sleep Daemon for episodic memory consolidation and pruning, and Spreading Activation for associative memory priming. These modules enable Mnemos to selectively encode only surprising or salient information, adapt stored knowledge as new context emerges, blend semantic and affective cues for retrieval, and keep memory stores clean by consolidating and pruning episodic traces. The system is designed to avoid the pitfalls of standard append-only memory layers, which can lead to bloated, contradictory, and operationally opaque memory pools. Mnemos is open source and can be installed via pip. It does not require extra services, as all retrieval and consolidation processes are handled locally. The framework includes built-in tools such as a graph edges doctor and a health check utility (mnemos_health) to help users inspect and maintain operational readiness. Its tier 1 host support includes Claude Code, Claude Desktop, and generic MCP hosts, with additional documentation for integrating with Codex.
- mnemonnpypi.orgmnemonn is an MIT-licensed Python package that gives AI coding agents persistent project memory and a knowledge graph. It stores project context in SQLite and supports MCP-based integration for developer workflows.
- 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.
- getmnemogetmnemo.xyzgetmnemo is an open-source Python SDK that provides long-term memory infrastructure for AI agents, supporting retrieval-augmented generation and persistent vector storage. It is designed for developers building advanced AI systems requiring contextual memory.
- 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.
- memnodememnode.devmemnode provides persistent memory infrastructure for AI agents through a hosted API and local MCP server. It supports typed recall, provenance and lineage inspection, scoped tokens, shared tenants, and correction workflows for coding, support, and research agents.
- 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.
- Mnexiummnexium.comMnexium is an AI memory API for LLM apps and agents. It adds persistent memory and context across sessions so applications can remember facts, preferences, chat history, user profiles, records, and live context without building memory infrastructure separately. The site describes it as working with OpenAI, Anthropic, and Gemini, and as sitting between an app and the model to add memory and context to requests. Its API includes options to learn facts from conversations, recall relevant memories, and prepend chat history. The product page also describes chat history preservation, agent memory that learns facts and recurring context over time, records for structured app data such as accounts, tickets, tasks, and deals, agent state for tracking tasks and workflow progress, integrations for external APIs and webhooks, and observability for tracing decisions, memory recall, tool usage, and API calls. Search by semantic similarity is shown in the docs, along with named methods for subject-based memory access. The examples are presented in cURL, Node.js, and Python, and the page shows a drop-in integration using the Mnexium SDK and a base URL at mnexium.com/api/v1. Pricing is listed in tiers. A Free plan requires no signup and is described for building and testing AI apps with persistent memory. Paid plans include Builder at $29 per month, Growth at $149 per month, and an Enterprise plan with custom pricing, custom deployment models, priority SLA, and onboarding support. The page also says API access is available on the free plan. Mnexium is therefore an AI memory and context layer for applications and agents, delivered as an API and SDK-based service rather than as a standalone end-user app.
- memosqpypi.orgmemosq is an open-source framework that provides persistent, cross-agent memory for AI coding assistants. It leverages semantic search and SQLite to store and retrieve contextual information, enabling more effective and context-aware AI agent collaboration.
- 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.
- memoriuspypi.orgmemorius is an open-source SDK and CLI tool that offers a self-contained memory vault for AI agents, featuring vector search, session diaries, and agent-agnostic hooks. It is designed for AI developers needing persistent, searchable memory for agent workflows.
- yourmemoryyourmemory.aiyourmemory is an open source Python package that provides persistent memory capabilities for Claude-based and MCP-native AI agents. It features semantic deduplication, Ebbinghaus forgetting curve support, and integrates with SQLite and PostgreSQL. Designed for developers building advanced AI agent workflows.
- 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.
- 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.
Ranked by how close each one sits to Mnemoverse in the index, not by popularity. Back to Mnemoverse →