MemClaw Alternatives

ClawMem 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.

MemClaw is a web-based tool designed to provide persistent, project-scoped memory for users of OpenClaw. It addresses common challenges faced by those managing multiple projects and clients through OpenClaw, such as blending conversations, forgotten details, and the loss of important information within extensive chat histories. By automatically separating memory across up to five projects and six clients, MemClaw ensures that each project maintains its own isolated context, preventing mix-ups and confusion between different workstreams. A key feature of MemClaw is its ability to recall the entire context, details, and decisions of a project with a single prompt, eliminating the need for users to repeatedly explain or reintroduce information. The platform offers a centralized web interface where users can review and manage every memory associated with their projects, making it easier to track progress, retrieve past work, and maintain continuity. MemClaw also supports collaboration by allowing users to invite teammates to share and contribute to the same project memory, enhancing team handoffs and collective knowledge retention. The tool is suitable for a range of professional workflows, including sales, research, and multi-project development. Use cases highlighted include tracking multiple clients in parallel, building a cumulative knowledge base from notes and source materials, and keeping project contexts distinct for developers managing several simultaneous initiatives. Example prompts supported by MemClaw include creating new client projects, saving reports to specific projects, listing all projects, collecting research insights, opening recent projects, and summarizing core resources within a project. MemClaw is installed by sending its repository to OpenClaw and following a brief setup process that involves obtaining a dedicated key via the Felo platform. The full installation and activation can be completed in under five minutes. The tool is developed by Felo Inc.

memclaw is an open-source, local-first AI-powered personal memory assistant for the command line. It enables users to organize, search, and retrieve personal knowledge using vector search and AI, all while keeping data private and local. Ideal for developers and privacy-conscious users.

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.

Claude-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.

MemBrain 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.

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.

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.

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.

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.

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.

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.

Captain Claw is an open-source platform designed to orchestrate multiple AI agents for complex tasks, offering users a command center called the Flight Deck to spawn, monitor, and coordinate specialist teams. Unlike single-agent chat tools, it enables the management of a fleet of AI agents, each with its own model, role, and toolset, all from a unified dashboard on the user's machine. The platform supports six distinct orchestration modes, allowing users to tailor the collaboration and workflow of agents to the specific demands of their tasks, such as ensemble reasoning, structured deliberation, and software engineering pipelines. Each agent within Captain Claw can be assigned a unique combination of models—including options like GPT, Claude, Gemini, DeepSeek, and Ollama—and a selection from 48 built-in tools. The system provides live monitoring, per-agent chat, file inspection, and log access without interrupting the work of other agents. Users can analyze token usage and costs at the agent level, with a comprehensive activity log that traces every tool invocation, consultation, and hand-off. Quick Chat functionality allows for rapid agent instantiation using predefined archetypes, which can be promoted to the main workspace as needed. The platform features a library of 31 editable agent archetypes covering roles in research, writing, engineering, data analysis, operations, finance, and multimedia. Users can build teams by selecting archetypes manually or by describing an objective in plain English, which prompts the system to design a team with appropriate roles, models, tools, and standard operating procedures. Structured deliberation is facilitated through the Agent Council mode, where agents participate in moderated rounds and vote, with the ability to export session minutes as markdown. For high-confidence outputs, Basna mode enables independent, blind responses from specialists that are merged by reliability, while Vatra mode supports collaborative drafting on a shared blackboard with review rounds. Captain Claw is distributed under the MIT License and runs locally. Its open-source nature and flexible orchestration modes make it suitable for users who need to coordinate specialized AI agents for tasks spanning research, engineering, analysis, and complex decision-making.

MateClaw is a self-hosted team AI operating system designed to keep organizational AI processes and data within the user's own infrastructure. It offers a unified platform that brings together autonomous AI agents, a self-maintaining wiki knowledge base, and memory management with per-user isolation, all running from a single deployable JAR file. The tool emphasizes verifiable and recoverable agent actions, automated wiki graph maintenance, and memory boundaries that separate individual user data. Its architecture is structured in four layers, covering goal execution, knowledge management, personalized memory, and runtime governance. Key features include agent orchestration with criteria-based checklists for task completion, automated replanning and delegation, and a visual Kanban board for tracking multi-step plans. The wiki component supports automated entity extraction, customizable entity types, permission controls, and a knowledge graph that self-repairs broken links and propagates stale status. Memory is managed with owner-based isolation, visibility controls (personal, team, global), and safeguards such as injection budgets, nightly consolidation, and file limits to prevent uncontrolled context growth. MateClaw integrates with various communication channels, including web, desktop, enterprise IM platforms (such as Feishu, DingTalk, WeChat Work, WeChat, Telegram, Discord, Slack), and embeddable web chat widgets. It supports desktop applications for Windows, macOS (Apple Silicon recommended), and Linux (AppImage), as well as deployment via Docker, JAR, or source code. The system is built on technologies like Spring Boot, Vue 3, TypeScript, MySQL, and Electron, and is open-source and auditable. Administrators have access to a centralized console for system health, agent management, tool governance, and channel configuration. Additional capabilities include a "Content Studio" workflow for producing and publishing content to platforms like WeChat Official Accounts and Xiaohongshu, with features for AI de-biasing, compliance scanning, and automated formatting. The platform also provides skills management, external tool integration via MCP and ACP protocols, and runtime approval workflows for high-risk actions. MateClaw is positioned as a comprehensive, self-contained AI operating system for organizations that require private, auditable, and controllable AI collaboration and knowledge management.

Memra 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.