MemoryAI is a memory engine designed to provide persistent, unified memory for AI agents across a range of tools and models. Below are 19 other ai apps with similar functionality to hmc-memory, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
ai-dememory is an open-source toolchain and MCP server that enables AI agents to store, manage, and retrieve memory locally across multiple large language models. It is designed for developers building personal or autonomous AI systems that require persistent, local-first memory infrastructure.
AgentMem provides a universal memory layer designed for AI agents, addressing the challenge of maintaining persistent and shared context across different frameworks. It enables agents to store, search, and sync memories, ensuring that context is not lost between sessions or fragmented across various agent implementations. The platform is intended for developers building agentic systems who require a consistent and searchable memory solution that works seamlessly with multiple frameworks. Key features of AgentMem include persistent memory storage that survives agent restarts, semantic search powered by vector embeddings to find memories by meaning, and cross-agent synchronization that allows sharing of context between different agents. The service integrates with frameworks such as LangChain, CrewAI, OpenAI, Claude, AutoGPT, and LlamaIndex. AgentMem offers a unified API for storing and retrieving memories, as well as syncing context across agents, making it straightforward to manage agent memory from a single interface. AgentMem is delivered as a cloud-based API with fast response times, built on Fastify and supported by edge caching for global performance. Security features include API key authentication, tenant isolation, and encryption at rest. The platform also provides usage analytics to track memory usage, search patterns, and agent activity. Deployment is backed by a global CDN, utilizing Railway and Cloudflare for fast and reliable access. Pricing is structured into three tiers: a free plan allowing up to 10,000 memories, semantic search, support for three agents, and community support; a Pro plan at $29 per month offering unlimited memories and agents, cross-agent sync, priority support, and usage analytics; and an Enterprise plan at $199 per month, which adds self-hosted deployment, custom integrations, a service-level agreement, and dedicated support. AgentMem is developed by Gritza.
hydramem is an open-source library that provides local, privacy-first long-term memory for AI agents. It combines hybrid graph and vector search, two-stage verification, and autonomous maintenance to help developers build agents with persistent, efficient, and private memory storage. Ideal for AI developers focused on privacy and autonomy.
HindClaw provides a production memory infrastructure designed specifically for AI agents, focusing on secure, multi-agent memory management and granular access control. The platform addresses the need for AI systems to manage and recall information efficiently and securely in environments with multiple agents and users, supporting per-user permissions and customizable memory behaviors for each agent. The system is built on top of Hindsight by Vectorize, leveraging its memory engine while adding an access control and infrastructure layer. HindClaw introduces server-side access control mechanisms, including JWT authentication, user and group permissions, tag-based recall filtering, and retain strategy enrichment, all enforced through extensions on the Hindsight server. Memory operations are handled server-side, with recall functions returning filtered results based on tag groups and retain operations storing data with injected tags and strategies. HindClaw's infrastructure-as-code approach is enabled through a dedicated Terraform provider, allowing users to manage banks, configurations, permissions, directives, mental models, and entity labels as code. This enables version control and reviewability for memory infrastructure changes. Each AI agent operates with its own memory bank, supporting custom missions, entity labels, dispositions, and directives, which allows for distinct memory behaviors across agents. The platform also supports multi-bank recall, enabling agents to read from multiple banks in parallel, with permissions enforced on a per-bank basis to prevent unauthorized access. Additional features include named retain strategies that route conversation topics to different extraction profiles for tailored analysis and storage, as well as an entity labeling system with controlled vocabulary, multilingual aliases, tag generation, and graph-traversable entities. HindClaw is delivered through several components: a server-side extension for Hindsight servers (installable via PyPI), a Terraform provider, and the OpenClaw gateway plugin available via npm. The platform is suitable for teams or organizations deploying AI agents that require secure, scalable, and customizable memory infrastructure with fine-grained access control.
mymem0ry 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.
kaelis-memory is an open-source AI-native memory system designed for multi-agent architectures. It features a four-layer memory, an MCP server, and a hallucination guard to improve reliability in agent-based AI systems. The package is suitable for developers building advanced AI agents and knowledge graphs.
MemMachine is an open-source memory layer designed for AI agents, allowing them to store, recall, and manage user data and preferences across sessions. It supports multi-agent and multi-session memory, helping developers build more context-aware and personalized AI assistants.
AutoMem is an open-source infrastructure tool that provides persistent memory for AI agents via MCP and HTTP interfaces. It supports both local and cloud deployments, enabling agents to store and recall structured and semantic data efficiently.
getmem-ai provides a persistent memory API designed to serve as the memory layer for AI agents. The platform addresses the challenge of maintaining and retrieving structured, contextual knowledge from conversational turns, enabling applications to deliver more accurate and personalized responses based on what is already known about each user. It processes conversation data by extracting structured knowledge, storing it in a typed graph and a hybrid vector index, and returning ranked, grounded context for every interaction, typically in under 300 milliseconds and with isolation per end user. The API operates through two main endpoints: one for ingesting conversation turns and another for retrieving context. When a conversation turn is ingested, the system extracts typed facts across a 12-category taxonomy, including tone and confidence, and resolves entities such as people, places, organizations, and topics with canonicalization and stable IDs. This information is written in parallel to a hybrid vector index—combining dense and sparse representations with 17-field payload filters—and a typed knowledge graph. Retrieval is handled on a fast, LLM-free path that includes heuristic decomposition, fuzzy entity matching (handling typos and variants), parallel hybrid search across multiple indexes, bounded graph expansion, and a ranking step. The result is a structured prompt block containing deduplicated, contextually relevant facts, along with meta information about latency and token counts for observability. getmem-ai is intended for developers building AI agents that require deep, accurate personalization, particularly in domains where response quality depends on prior user knowledge. Example use cases include healthcare agents that remember patient histories and legal research agents aware of case facts and client preferences. The system is designed with features such as per-patient or per-user isolation and audit logging for sensitive applications. The service is accessible via API, and integration is described as straightforward with a free credit available to start. Overall, getmem-ai is positioned as a specialized memory infrastructure for AI and conversational applications, enabling structured retrieval-augmented workflows for agents that benefit from persistent, contextual memory.
memoryhub-cli is an open-source CLI client for MemoryHub, offering a centralized and governed memory store for AI agents. It supports the MCP protocol and enables agents to share and retrieve structured data efficiently. Designed for AI agent developers and researchers.
yourmemory 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.
supermem 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.
Memori is an agent-native memory infrastructure layer for AI systems, turning agent execution and conversations into structured, persistent state. It enables developers to capture, classify, and recall facts, preferences, rules, and summaries from chat interactions, supporting targeted recall and semantic search. Designed for production AI, it helps teams manage and enrich conversational context efficiently.
Engram Memory is an infrastructure tool designed to provide persistent memory for AI agents, enabling them to remember conversations, decisions, and context across projects, sessions, and machines. It is built for users who require local control over their data and want to avoid storing information on third-party servers. The platform emphasizes privacy by ensuring that data remains on the user's own infrastructure unless specific features are opted into, such as overflow storage, which is encrypted and only used with user consent. The tool offers several capabilities, including an API that processes text into vector representations for semantic search, deduplication to prevent storing duplicate information, and proprietary compression technology that reduces memory storage size to one-sixth without recall loss. Engram Memory supports automatic tiering between local hot storage and optional cloud warm storage, allowing older memories to be offloaded and retrieved as needed. An upcoming feature will enable end-to-end encrypted synchronization of memories across self-hosted devices without requiring a central data store. js. It is delivered as a self-hosted solution, giving users full control over where their data is stored. The core of Engram Memory is open source and licensed under the MIT license. The platform is described as HIPAA-ready, GDPR-compatible, and suitable for environments with strict privacy requirements, including those that require attorney-client privilege or FedRAMP architecture compliance. Performance benchmarks indicate that Engram Memory achieves fast storage speeds, high recall accuracy, and efficient memory management compared to other solutions. Its design is intended for developers and teams building AI systems that demand persistent, private, and scalable memory infrastructure.
claude-mem and cmem are open-source tools that provide persistent memory and Model Context Protocol (MCP) integration for AI agents. They enable agents to retain, sync, and recall context across sessions, supporting both local and cloud deployments for developers building advanced agent frameworks.
memosq 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.
Memoir is an open-source infrastructure library that provides taxonomy-structured, Git-versioned memory for AI agents. It enables explainable, local-first storage with features like branching and recall by path, supporting developers building advanced AI agents and custom runtimes.
Memanto 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.
agent-memory-guard is an open-source CLI tool that provides runtime defense for AI agent memory, protecting against poisoning, tool abuse, and privilege escalation. It is designed for AI security engineers and follows OWASP guidelines.
