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Software like MemClaw
What else does this job. Matched on what each project does, not on who links to whom.
Closest first
- 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.
- MemClawmemclaw.meMemClaw is a memory tool for OpenClaw that keeps project context separate across workstreams. It is described as giving OpenClaw a second brain, with project-scoped memory that isolates conversations, details, and decisions so they do not blend across projects or clients. The page says it separates memory automatically across 5 projects and 6 clients. One short prompt can bring back the whole project memory, and the web app can be opened at any time to review and manage every memory in one organized view. It is also designed to support collaboration: teammates can be invited into MemClaw to work from the same shared memory. The listed use cases include sales, knowledge work, and multi-project development, with examples such as tracking clients in parallel, keeping papers and notes tied to the right project, and running several projects at once without confusing background context. MemClaw is delivered through a web app and is installed into OpenClaw. The setup flow shown on the page involves sending the repository to OpenClaw, obtaining a dedicated key through the Felo platform, and sending that key back to OpenClaw to activate it. The page also includes an OpenClaw memory guide, a setup guide, a security page, and an FAQ. Pricing is stated as free: it can be installed and used right away at no cost. The footer identifies Felo Inc. as the publisher.
- memclawgithub.commemclaw 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.
- 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.
- 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.
- 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.
- 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.
- Production Memory Infrastructure for AI Agentshindclaw.proHindClaw is production memory infrastructure for AI agents. It centers on server-side access control, Terraform-managed banks, and multi-agent memory with per-user permissions, and it is built on Hindsight by Vectorize. The project describes itself as three independent packages that can be used separately or together. Its server-side extension provides JWT authentication, permission enforcement, tag injection, and a management REST API, and it can be installed on any Hindsight server. The Terraform provider manages users, groups, banks, permissions, directives, mental models, and entity labels as code, with the documentation noting that terraform apply makes the stack live. The OpenClaw gateway plugin is described as a thin adapter that signs JWTs and auto-starts the embed daemon with extensions loaded. The product’s workflow routes a message from channels such as Telegram or Slack to the right agent, signs a JWT with sender, agent, channel, and topic context, resolves the sender to a user, checks group memberships, and applies a four-layer permission cascade. Memory operations then run server-side: recall returns filtered results with tag groups, while retain stores data with injected tags and strategy. Additional features listed on the page include per-agent memory banks, multi-bank recall with per-bank permission checks, named retain strategies, and entity labels with multilingual aliases, tag generation, and graph-traversable entities. The page also says HindClaw is built on Hindsight by Vectorize and contrasts that system’s memory engine with HindClaw’s access-control and infrastructure layer. Delivery is through the Hindsight server extension, the Terraform provider, and the npm-published OpenClaw gateway plugin. A link to PyPI appears for the extension, and the site also references Hindsight Cloud as a managed option. No pricing or license terms are stated in the provided text.
- 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.
- 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.
- 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.
- 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.
- Captain Clawcaptain-claw.comCaptain 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.
- MateClawmate.vipMateClaw is a self-hosted team AI operating system. It is described as keeping team AI on the user’s own machine and within the deployment boundary, with agent actions that are verifiable and recoverable, a self-maintaining wiki knowledge graph, and long-term memory isolated by user. The product combines agents, wiki, memory, tools, channels, and models in one core system. Its workflow centers on goal execution, knowledge management, memory, and runtime governance. Goals use a criteria checklist for completion and can automatically continue, replan, or delegate when work gets stuck. The wiki layer supports page types, read and write permissions, a processing pipeline, self-repair of renamed, deleted, or broken links, and extraction of entity knowledge graphs with customizable entity types. Memory includes owner-based isolation and PERSONAL, TEAM, and GLOBAL visibility controls, along with injection budgets, nightly consolidation, and file limits. The runtime layer exposes model routing, skills, MCP, ACP, channels, tools, and approvals, and administrators can view system health, agent management, tool governance, channel configuration, and execution details. MateClaw also includes a content studio for producing publishable WeChat Official Account and Xiaohongshu content. The listed flow is topic selection, collection, writing, illustration, de-AI processing, formatting, and delivery. For WeChat, the output can go into a draft box as inline HTML and the main image can be uploaded automatically; for Xiaohongshu, it packages content into at least three vertical cards with online preview. The product also mentions content calendar deduplication and compliance scanning at delivery. Channels are described as including web, desktop, embeddable WebChat, and enterprise IM services such as Feishu, DingTalk, WeChat Work, WeChat, Telegram, Discord, and Slack, with unified handling for images, voice, and files. Delivery methods named on the page include Docker, JAR, source code, and a desktop app. It is shown running through docker compose on localhost:18080, and the desktop release is said to include JRE 21 and support Windows 10+, macOS 14+ on Apple Silicon, and Linux via AppImage. The page also refers to an open-source, auditable system and shows a default admin login for the local deployment. A separate enterprise offering called 太一 is mentioned for private deployment in government and enterprise settings, but the main product description remains centered on the self-hosted MateClaw system.
- 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.
Ranked by how close each one sits to MemClaw in the index, not by popularity. Back to MemClaw →