Alternatives to claude-mem

Compare claude-mem alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to claude-mem in 2026. Compare features, ratings, user reviews, pricing, and more from claude-mem competitors and alternatives in order to make an informed decision for your business.

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    Gemini Enterprise Agent Platform
    Gemini Enterprise Agent Platform is a comprehensive solution from Google Cloud designed to help organizations build, scale, govern, and optimize AI agents. It represents the evolution of Vertex AI, combining advanced model development with new capabilities for agent orchestration and integration. The platform provides access to over 200 leading AI models, including Google’s Gemini series and third-party options like Anthropic’s Claude. It enables teams to create intelligent agents using both low-code and code-first development environments. With features like Agent Runtime and Memory Bank, businesses can deploy long-running agents that retain context and perform complex workflows. The platform emphasizes security and governance through tools like Agent Identity, Agent Registry, and Agent Gateway. It also includes optimization tools such as simulation, evaluation, and observability to ensure consistent agent performance.
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    CMEM Cloud

    CMEM Cloud

    cmem.ai

    CMEM Cloud is the cloud sync layer for claude-mem, built to link AI agent memory everywhere through one private MCP link. claude-mem is the open source engine that takes notes while an agent works, and CMEM Cloud mirrors that local memory so agents can recall it across every session, machine, editor, and MCP-compatible client. Instead of making users re-explain context, paste old notes, or restart from zero, the system captures decisions, bug fixes, dead ends, environment notes, architecture choices, and other structured observations as the agent works. Those observations are stored in a temporal database, searched by meaning through vector recall, and made available through a private MCP endpoint that any compatible agent can read and write through. It starts with installing the local engine, letting a second model write structured notes out of band, syncing the local database to CMEM Cloud, and then recalling that memory anywhere.
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    Beacon

    Beacon

    Asymptote Labs

    Beacon is an open-source telemetry and memory layer for AI coding agents. It records agent sessions across local machines, CI, and cloud environments, creating a searchable history for developers and teams. Users can review workflows, debug agent behavior, analyze usage, investigate security risks, and turn sessions into reusable skills and agent memory. Beacon supports Claude Code, Cursor, Codex, OpenCode, and more than 20 other agent harnesses; the Observe SDK supports instrumentation of cloud agents. It runs locally or connects to Beacon Cloud for hosted session history, findings, analytics, sharing, and MCP access. The free Beacon Cloud plan includes 50 findings, session search over MCP, and shareable sessions. Pro adds unlimited findings, agent memories, reusable skills, full MCP access, and real-time alerts. Enterprise adds organization-wide findings, endpoint and CI deployment, approval controls, SSO, role-based access, audit logs, and dedicated support.
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    past.dev

    past.dev

    past.dev

    past.dev is a long-term memory API for developers building AI agents and applications. Applications send timestamped text from conversations, email, meetings, support records, CRM notes, and documents; the service derives memories and returns ranked results with dates and excerpts from the source data. Its API supports ingestion, batch ingestion, recall, deletion, project and identity management, audience-based access controls, and webhooks. Developers can connect through JSON endpoints or authenticated Model Context Protocol servers, and install companion plugins for Claude Code, Codex, Cursor, JetBrains IDEs, and Hermes Agent. The managed cloud service offers a console for API keys, usage, and request logs; enterprise deployments can run in a customer's AWS, Google Cloud, or Azure account. It is aimed at software teams adding persistent, source-grounded memory to AI agents and other applications.
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    PlatformPilot
    PlatformPilot is a company brain for AI-first teams. It captures how your company actually works, your decisions, playbooks, and tribal knowledge, and turns it into a living memory your team and your AI agents can use to answer questions and take action across all your tools. Unlike search tools that only retrieve, PlatformPilot reasons across your systems, shows the why behind every answer, and acts on your own playbooks, in your own cloud, getting sharper every time it is used. It connects to your stack through the Model Context Protocol (MCP), so it works as a shared memory layer inside the tools your team already uses, including Claude Code, Claude Desktop, and OpenAI-based agents. Memory evolves as you work. - Living memory that learns from outcomes, not just stores notes - Reasoning across all your tools. We support +200 tools. - Plain-language search over your team's decisions, playbooks, and history - Self-organizing knowledge
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    EverOS

    EverOS

    EverMind

    EverOS is a persistent memory infrastructure platform for AI agents that provides long-term, multimodal, cross-platform context across agent workflows. The system retrieves relevant memories before model calls, allowing agents to reuse prior context without repeatedly loading large histories into the prompt window. EverOS also includes self-evolving skills, which capture successful execution trajectories as cases and promote recurring patterns into reusable procedural knowledge. The platform can ingest PDFs, images, documents, spreadsheets, slides, Markdown, and URLs and convert them into searchable memory for inference-time retrieval. It supports cloud and self-hosted deployment, exports memories in Markdown, and integrates with tools such as Claude Code, Codex, OpenClaw, Hermes, MCP, and OpenAI- or Anthropic-compatible SDKs. EverOS achieves 93.05% on the LoCoMo long-term memory benchmark (research: arXiv:2601.02163).
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    Memmy

    Memmy

    Memmy

    Memmy is a local-first AI memory layer that lets every AI tool remember the same version of you. Built for people who work with several assistants every day, it automatically reads authorized collaboration history from tools such as Cursor, Claude, and Codex, then turns scattered conversations, preferences, project context, technical decisions, progress, and recurring pitfalls into structured memory. Its workflow has three stages: Scan reviews selected histories on the user’s device; Organize distills, deduplicates, categorizes, and indexes useful information; and Inject gives the active AI only the most relevant memories through precise, on-demand matching instead of sending a complete data dump. This allows users to switch tools without rebuilding context, merge plans discussed across agents, document recent decisions, preserve writing preferences, and continue unfinished work.
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    Graphify

    Graphify

    Graphify

    Graphify is an open source knowledge graph engine that turns any input, including code, docs, papers, meetings, images, browser tabs, and commits, into one traversable graph with complete recall. It is built as persistent memory for AI coding assistants, giving tools like Claude Code, Codex, OpenCode, Cursor, Gemini CLI, GitHub Copilot CLI, Aider, Factory Droid, Kimi Code, Kiro, Pi, and Google Antigravity a queryable understanding of a project instead of making them repeatedly grep through files. Users can point Graphify at any directory, and it builds an initial corpus through AST extraction, semantic analysis, and Leiden clustering, transforming an entire codebase or document corpus into a graph in one pass. Unlike RAG pipelines that re-embed everything on every change, Graphify maintains a living graph that updates only affected nodes and edges when files change, allowing the rest of the corpus to stay intact even at enterprise scale.
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    OpenMemory

    OpenMemory

    OpenMemory

    OpenMemory is a Chrome extension that adds a universal memory layer to browser-based AI tools, capturing context from your interactions with ChatGPT, Claude, Perplexity and more so every AI picks up right where you left off. It auto-loads your preferences, project setups, progress notes, and custom instructions across sessions and platforms, enriching prompts with context-rich snippets to deliver more personalized, relevant responses. With one-click sync from ChatGPT, you preserve existing memories and make them available everywhere, while granular controls let you view, edit, or disable memories for specific tools or sessions. Designed as a lightweight, secure extension, it ensures seamless cross-device synchronization, integrates with major AI chat interfaces via a simple toolbar, and offers workflow templates for use cases like code reviews, research note-taking, and creative brainstorming.
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    Membase

    Membase

    Membase

    Membase is a unified AI memory layer platform designed to help AI agents and tools share and persist context so they “understand you” across sessions without forced repetition or isolated memory silos, enabling consistent conversational experiences and shared knowledge across AI assistants. It provides a secure, centralized memory layer that captures, stores, and syncs context, conversation history, and relevant knowledge across multiple AI agents and integrations with tools such as ChatGPT, Claude, Cursor, and others, so all connected agents can access a common context and avoid repeating user intents. Designed as a foundational memory service, it aims to maintain consistent context across your AI ecosystem, reducing friction and improving continuity in multi-tool workflows by keeping long-term context available and shared rather than locked within individual models or sessions, and letting users focus on outcomes instead of re-entering context for each agent request.
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    ByteRover

    ByteRover

    ByteRover

    ByteRover is a self-improving memory layer for AI coding agents that unifies the creation, retrieval, and sharing of “vibe-coding” memories across projects and teams. Designed for dynamic AI-assisted development, it integrates into any AI IDE via the Memory Compatibility Protocol (MCP) extension, enabling agents to automatically save and recall context without altering existing workflows. It provides instant IDE integration, automated memory auto-save and recall, intuitive memory management (create, edit, delete, and prioritize memories), and team-wide intelligence sharing to enforce consistent coding standards. These capabilities let developer teams of all sizes maximize AI coding efficiency, eliminate repetitive training, and maintain a centralized, searchable memory store. Install ByteRover’s extension in your IDE to start capturing and leveraging agent memory across projects in seconds.
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    MemClaw

    MemClaw

    Caura AI

    MemClaw is a persistent-memory service for LLM-based agents and a governed shared memory layer for agent fleets. It is designed to help AI agents learn from each other by turning isolated agent context into a Company Brain with memory, governance, provenance, contradiction detection, and visibility scopes built in from day one. MemClaw separates an organization’s agent force, including tenants, fleets, nodes, and agents, from the governed memory plane through MCP Server, REST API, OpenClaw plugin, MemClaw Core, and persistent storage. Agents can write to and recall from the Company Brain through MCP-compatible tools, direct HTTPS calls, or OpenClaw integration, while MemClaw Core runs enrichment such as entity extraction, contradiction detection, PII scanning, and lifecycle transitions before anything is stored. Every memory can be stamped with a visibility scope, auto-classified into types such as fact, episode, decision, preference, rule, plan, commitment, action, and outcome.
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    Memory AGI

    Memory AGI

    Memory AGI

    Memory AGI is a runtime memory layer for AI agents, built around the idea of giving agents real muscle memory. Hand over a slice of company data, and Memory AGI builds the organization’s knowledge and runtime memory layer, grounds agents in the business, and keeps that context current automatically. Your AI is only as good as the context you give it; without it, agents stay stuck at an intern-level, guessing at how the company runs. Memory AGI turns processes into knowledge agents that can actually execute, so they run reliably, show their work, and can be trusted with what they ship. It is built on three layers of muscle memory. Dynamic Ingestion captures and structures the company’s unique knowledge from voice notes, internal documents, or the tools where data already lives. The Runtime Memory Layer gives agents access to a live, de-duplicated context layer; a company knowledge base that humans, agents, and automations can all draw on to perform tasks like the best employees.
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    Hindsight

    Hindsight

    Vectorize

    Hindsight is an agent memory system built to create smarter AI agents that learn over time instead of starting every conversation from zero. Most agent memory systems focus on recalling conversation history, but Hindsight is focused on making agents learn, not just remember. It gives AI agents persistent long-term memory using biomimetic data structures, helping them retain facts, recall relevant context, and reflect on experience as part of reasoning. Hindsight is designed for agents that need to understand who a user is, what has been discussed, what preferences have emerged, what decisions were made, and how behavior should adapt across sessions. It provides three core operations: retain, recall, and reflect. Retain stores new information, recall retrieves the right memories when needed, and reflect helps agents synthesize observations, form mental models, and learn from prior interactions.
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    Vokal

    Vokal

    Vokal

    Vokal is a collaboration space for teammates and AI agents, built so founders and product teams can run agent work where the team can see it, review it, and reuse what matters. It gives human-agent work a shared place to start, move, stay visible, and become reusable context, instead of leaving agent runs, assumptions, and decisions trapped in private sessions across Claude Code, Codex, Cursor, ChatGPT, or other tools. Vokal connects channels, tasks, docs, files, apps, agents, memory, Knowledge Base, identity, access, runtime, and event logs around the work, helping teams keep output aligned, reviewed, controlled, and reusable. Agents can work in shared channels with named owners, roles, instructions, sources, statuses, permission scopes, app grants, memory scope, local project-file grants, and visible activity. Teams can use pre-built roles for engineering, product, growth, support, operations, research, and customer work, or bring their own local Codex, Claude Code, Hermes, etc.
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    MythOS

    MythOS

    MythOS

    MythOS is a shared memory system between you and every AI you use, built to help people stop re-explaining themselves across models, agents, and channels. It is designed for people who write to think, giving them a modular thinking system for structured notes, memos, contextual maps, and AI-powered workflows. Users can capture what they read, connect what they think, and publish what matters while keeping their library one click away from every AI. MythOS works as a personal knowledge operating system where memory, notes, ideas, resources, and context can be organized into structured documents that stay useful over time. Its approach treats knowledge as a process, not a one-time activity, so living documents can remain in progress, evolve, and connect with related people, projects, topics, and ideas. It supports contextual maps, public memos, private knowledge, AI-ready memory, exportable data, and workflows that help users build a durable layer of context.
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    MemPalace

    MemPalace

    MemPalace

    MemPalace is a local-first storage and retrieval system for AI workflows, built to give AI a memory while keeping the user’s words under their own control. It stores conversations verbatim instead of reducing them to summaries, then organizes that memory into a navigable “palace” structure inspired by the ancient memory palace technique. Conversations can be arranged into wings for people, projects, or topics, with rooms and drawers used to make information easier to locate, narrow, and retrieve later. It is designed for people who believe their words are theirs, with local-first storage, zero telemetry, and a privacy-focused approach that keeps memory on the user’s machine. MemPalace supports AI workflows through MCP tooling, including tools for palace reads and writes, knowledge-graph operations, cross-wing navigation, drawer management, and agent diaries.
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    bb

    bb

    bb

    bb is a local-first, fully customizable IDE for working with AI coding agents, designed to control, automate, and even modify itself. Almost anything in the environment can be changed with a single prompt: users can add panels, CLI commands, skills, plugins, and workflows that immediately become available to their agents. Many of bb’s own capabilities, including GitHub integration, agent memory, scheduled jobs, and remote access, are built as plugins using the same tools available to users. Its CLI is open to external programs such as shell scripts, cron jobs, or bots in Telegram, Signal, and Slack, allowing them to spawn work threads that remain available in the sidebar. bb supports multiple coding agents, including Claude Code, Codex, Cursor, Pi, OpenCode, Grok, omp, and Hermes, letting users assign tasks to the best-suited agent or have one agent spawn and manage another in separate threads. Work runs on the user’s own machine and can continue independently until they return.
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    HQ

    HQ

    Indigo AI

    HQ is the shared AI context layer for teams, giving the whole team and every AI tool one workspace to work from, with knowledge, skills, and workflows compounding in one place, and any agent running on top. It works as an operating system for AI workers over Claude Code, Cursor, Codex, ChatGPT, and Claude chat through MCP, so every teammate and every agent can start from the same shared context instead of separate chat histories, scattered files, and siloed workflows. HQ turns one person’s best work into team infrastructure: any prompt or workflow can become a reusable /command, then /hq-sync ships it to the whole team so anyone can run it in one step. Knowledge that usually lives across decisions, docs, playbooks, policies, projects, code, and ideas accumulates in HQ as the team works, creating one source of truth that every agent can search, reuse, and build on. Agents can be deployed into email and Slack, acting on top of the team’s skills and knowledge with full context.
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    Preloop

    Preloop

    Preloop

    Preloop is the open source AI agent control plane for agents that take real actions. It combines an MCP firewall for tool access, an AI model gateway for cost, safety, and attribution, policy-as-code with human approvals, runtime session observability, and audit trails in a single self-hostable platform. AI agents can deploy code, change infrastructure, move money, touch production data, and burn model spend in seconds, so Preloop helps teams control what agents can do, how much they spend, and which actions require human approval. It works with OpenClaw, Hermes, Claude Code, Codex CLI, Cursor, Gemini CLI, Windsurf, Cline, OpenCode, and any MCP-compatible agent or managed runtime. Access rules can inspect arguments and context, not just tool names, with CEL expressions for fine-grained conditions. Teams can start with observability, then layer in approvals and deny rules without SDKs or invasive app changes.
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    LangMem

    LangMem

    LangChain

    LangMem is a lightweight, flexible Python SDK from LangChain that equips AI agents with long-term memory capabilities, enabling them to extract, store, update, and retrieve meaningful information from past interactions to become smarter and more personalized over time. It supports three memory types and offers both hot-path tools for real-time memory management and background consolidation for efficient updates beyond active sessions. Through a storage-agnostic core API, LangMem integrates seamlessly with any backend and offers native compatibility with LangGraph’s long-term memory store, while also allowing type-safe memory consolidation using schemas defined in Pydantic. Developers can incorporate memory tools into agents using simple primitives to enable seamless memory creation, retrieval, and prompt optimization within conversational flows.
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    octobrain
    octobrain is an open-source persistent memory server for AI assistants, delivered as a Rust command-line application and Model Context Protocol (MCP) server. It stores notes, decisions, project context, and indexed documents so compatible assistants can retrieve information across conversations. Features include semantic and hybrid BM25/vector search, linked memories, project-scoped stores, goal consolidation, and a knowledge base that indexes URLs, PDFs, and common document formats. Teams can distribute shared knowledge through Git-backed knowledge boxes. octobrain runs with local embedding models or optional cloud embedding providers, and exposes tools for saving, searching, and deleting memories. It is designed for developers and teams extending MCP-compatible AI assistants with persistent, searchable context; installation and operation are managed on the user's own machine or environment.
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    Hyperspell

    Hyperspell

    Hyperspell

    Hyperspell is an end-to-end memory and context layer for AI agents that lets you build data-powered, context-aware applications without managing the underlying pipeline. It ingests data continuously from user-connected sources (e.g., drive, docs, chat, calendar), builds a bespoke memory graph, and maintains context so future queries are informed by past interactions. Hyperspell supports persistent memory, context engineering, and grounded generation, producing structured or LLM-ready summaries from the memory graph. It integrates with your choice of LLM while enforcing security standards and keeping data private and auditable. With one-line integration and pre-built components for authentication and data access, Hyperspell abstracts away the work of indexing, chunking, schema extraction, and memory updates. Over time, it “learns” from interactions; relevant answers reinforce context and improve future performance.
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    Vity

    Vity

    Maximem

    Vity is a personal AI memory assistant for people who use several AI platforms. Its Chrome extension and cloud dashboard capture and organize conversation context, preferences, work history, and tasks, then make that information available across supported AI chats so users can continue work without re-entering the same background. Vity supports ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Manus, Genspark, Blackbox, Kimi, and Grok, along with Gmail and Google Calendar. Users can review, manage, export, or delete saved memory, and the extension captures context with user consent. Vity is designed for AI enthusiasts and power users, including professionals who use multiple assistants for research, writing, planning, and technical work. It is free to get started, with no credit card required.
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    Letta

    Letta

    Letta

    Create, deploy, and manage your agents at scale with Letta. Build production applications backed by agent microservices with REST APIs. Letta adds memory to your LLM services to give them advanced reasoning capabilities and transparent long-term memory (powered by MemGPT). We believe that programming agents start with programming memory. Built by the researchers behind MemGPT, introduces self-managed memory for LLMs. Expose the entire sequence of tool calls, reasoning, and decisions that explain agent outputs, right from Letta's Agent Development Environment (ADE). Most systems are built on frameworks that stop at prototyping. Letta' is built by systems engineers for production at scale so the agents you create can increase in utility over time. Interrogate the system, debug your agents, and fine-tune their outputs, all without succumbing to black box services built by Closed AI megacorps.
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    Cronloop

    Cronloop

    Cronloop

    Cronloop lets you automate work with unattended AI agents that run on a schedule. Describe the job in plain Markdown, pick Codex or Claude Code, connect your tools, and watch your agents run. Every agent keeps a memory and self-improves between runs.
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    Membase

    Membase

    Unibase

    Membase is a long-term memory service for people and developers building AI agents. It brings information from conversations, files, web pages and connected apps into a persistent memory that users can review, correct, delete and export. A web app and Chrome extension let users manage memories, save browser content and share selected information with supported AI tools. Developers connect custom agents through MCP, the REST API, command-line interface, or Python and TypeScript SDKs. Separate memories and access controls support personal, project and client contexts, with controls to grant or revoke access. The hosted service stores and processes source content to build and retrieve memory.
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    BrainAPI

    BrainAPI

    Lumen Platforms Inc.

    BrainAPI is the missing memory layer for AI. Large language models are powerful but forgetful — they lose context, can’t carry your preferences across platforms, and break when overloaded with information. BrainAPI solves this with a universal, secure memory store that works across ChatGPT, Claude, LLaMA and more. Think of it as Google Drive for memories: facts, preferences, knowledge, all instantly retrievable (~0.55s) and accessible with just a few lines of code. Unlike proprietary lock-in services, BrainAPI gives developers and users control over where data is stored and how it’s protected, with future-proof encryption so only you hold the key. It’s plug-and-play, fast, and built for a world where AI can finally remember.
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    Memories.ai

    Memories.ai

    Memories.ai

    Memories.ai builds the foundational visual memory layer for AI, transforming raw video into actionable insights through a suite of AI‑powered agents and APIs. Its Large Visual Memory Model supports unlimited video context, enabling natural‑language queries and automated workflows such as Clip Search to pinpoint relevant scenes, Video to Text for transcription, Video Chat for conversational exploration, and Video Creator and Video Marketer for automated editing and content generation. Tailored modules address security and safety with real‑time threat detection, human re‑identification, slip‑and‑fall alerts, and personnel tracking, while media, marketing, and sports teams benefit from intelligent search, fight‑scene counting, and descriptive analytics. With credit‑based access, no‑code playgrounds, and seamless API integration, Memories.ai outperforms traditional LLMs on video understanding tasks and scales from prototyping to enterprise deployment without context limitations.
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    MemMachine

    MemMachine

    MemVerge

    An open-source memory layer for advanced AI agents. It enables AI-powered applications to learn, store, and recall data and preferences from past sessions to enrich future interactions. MemMachine’s memory layer persists across multiple sessions, agents, and large language models, building a sophisticated, evolving user profile. It transforms AI chatbots into personalized, context-aware AI assistants designed to understand and respond with better precision and depth.
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    Papr

    Papr

    Papr.ai

    Papr is an AI-native memory and context intelligence platform that provides a predictive memory layer combining vector embeddings with a knowledge graph through a single API, enabling AI systems to store, connect, and retrieve context across conversations, documents, and structured data with high precision. It lets developers add production-ready memory to AI agents and apps with minimal code, maintaining context across interactions and powering assistants that remember user history and preferences. Papr supports ingestion of diverse data including chat, documents, PDFs, and tool data, automatically extracting entities and relationships to build a dynamic memory graph that improves retrieval accuracy and anticipates needs via predictive caching, delivering low latency and state-of-the-art retrieval performance. Papr’s hybrid architecture supports natural language search and GraphQL queries, secure multi-tenant access controls, and dual memory types for user personalization.
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    MemU

    MemU

    NevaMind AI

    MemU is an intelligent memory layer designed specifically for large language model (LLM) applications, enabling AI companions to remember and organize information efficiently. It functions as an autonomous, evolving file system that links memories into an interconnected knowledge graph, improving accuracy, retrieval speed, and reducing costs. Developers can easily integrate MemU into their LLM apps using SDKs and APIs compatible with OpenAI, Anthropic, Gemini, and other AI platforms. MemU offers enterprise-grade solutions including commercial licenses, custom development, and real-time user behavior analytics. With 24/7 premium support and scalable infrastructure, MemU helps businesses build reliable AI memory features. The platform significantly outperforms competitors in accuracy benchmarks, making it ideal for memory-first AI applications.
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    OpenViking

    OpenViking

    OpenViking

    OpenViking is an open source context database designed specifically for AI agents, built around a file-system paradigm that unifies the management of memories, resources, and skills. Instead of treating context as scattered chunks in a fragmented vector store, OpenViking organizes agent context into a virtual file system under the viking protocol, giving agents a structured way to store, navigate, retrieve, and observe the information they need. It is designed to help developers move beyond the hassle of manual context management by giving agents a minimalist interaction model for context, similar to reading and writing files. OpenViking supports hierarchical context loading, semantic retrieval, recursive retrieval, sessions, metrics, and observability, making it possible for AI agents to access the right level of information without stuffing everything into the prompt.
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    OpenChiip Harness
    OpenChiip Harness is a self-hosted AI agent runtime and orchestration framework for developers building local or coordinated agent workflows. It combines LLM conversations, skill orchestration, tool execution, multi-layer memory, resource monitoring, and multi-agent collaboration in a React administration dashboard. Run it as a local service or connect it to the OpenChiip platform for collaborative scheduling. Its CLI, web dashboard, HTTP API, and WebSocket interface support chat, tools, sessions, memory, task management, and monitoring. It supports Baidu ERNIE, OpenAI-compatible model providers, and delegation to Codex or Claude Code. Install via pip or deploy with Docker, Linux packages, systemd, or Windows installers; macOS supports pip installation. Licensed under AIGCGPL-1.0.
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    Vibe Island

    Vibe Island

    Vibe Island

    Vibe Island is a macOS notch panel for AI coding agents. It tracks sessions from 26 agents - Claude Code, Codex, Gemini CLI, Cursor, OpenCode, Kimi Code, DeepSeek, Copilot, and more - in one place, with real-time status, per-agent brand colors, and one-click precise jump to the exact terminal tab, split pane, or VS Code window where each session runs (20+ terminals supported, tmux included). Built for developers running several agents in parallel, it also shows remaining usage quota for Claude, Codex, Kimi, GLM, and DeepSeek subscriptions with live reset countdowns, and lets you approve permissions, answer agent questions, and review plans without leaving the notch. Works with local sessions and remote servers over SSH. Native Swift, no Electron, under 100 MB RAM. Free trial, then a one-time purchase - no subscription.
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    Densar BK

    Densar BK

    Chrononyte

    Densar BK backs up what git and file-sync tools leave out: the AI agent's memory, the junctions, the secrets and local configs. It scans the root folder and finds them by itself, then writes an encrypted archive (age standard, password required) and skips what can be rebuilt: in one real run, 0.38 GB archived against 21.73 GB of caches left out. On the new PC it does more than put files back: it recreates the junctions instead of duplicates that drift apart, remaps the agent's memory to the new paths, reinstalls the toolchain (winget, npm, cargo, pip), and lists the steps still missing to run each project. Since 3.0.0 it restores straight from your own server, guided, opens an archive like a zip to pull single files out, and carries the memory of Cursor, Gemini CLI, Codex CLI and Copilot next to Claude Code. Analysis is free and shows what would be saved before you decide. A backup is a one-time EUR 59. Restoring is free, on any machine, no licence. Windows only. Nothing leaves the PC.
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    superagnt_

    superagnt_

    superagnt_

    An agent with no hands is just a chat window. superagnt_ is one MCP server that gives the agent you already run social and web data, lead enrichment, a real Postgres database, shared memory, schedules, queues, webhooks and an inbox on a single key. Point Claude Code, Codex, Cursor, OpenClaw or any MCP client at the superagnt MCP server. Credits charged only on a successful response. Free and pay-as-you-go available; builder base from $19/mo.
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    Engram

    Engram

    Weaviate

    Engram is a fully managed memory and context service purpose-built to help AI agents remember, learn, and improve over time. Instead of treating memory as an ever-growing pile of raw conversations and events, it turns noisy interaction data into structured, durable, and evolving memories. Applications can send raw text, complete conversations, or pre-extracted facts through a REST API or Python SDK without preprocessing. Engram then runs asynchronous pipelines that extract relevant information, transform it by deduplicating and reconciling it with existing knowledge, and commit a clean memory state without blocking the application’s main workflow. It resolves inconsistencies, adapts to changing preferences and time-evolving facts, and keeps context relevant and efficient. Agents can retrieve ranked memories in real time through vector similarity, BM25 keyword search, or hybrid retrieval, reducing the need to resend entire conversation histories.
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    Spawn

    Spawn

    OpenRouter

    Spawn is an experimental OpenRouter tool for deploying AI coding agents on your own infrastructure with a single command. Pick an agent, choose a cloud, and Spawn provisions a virtual machine, installs the agent and its dependencies, authenticates to OpenRouter and the cloud using a CLI OAuth flow, configures endpoints and model routing, and then opens an SSH session so you can start working. Each agent-and-cloud combination is implemented as a self-contained script, avoiding Terraform and YAML while keeping deployment portable. Supported agents include Claude Code, OpenClaw, Codex CLI, OpenCode, Kilo Code, Hermes Agent, Junie, Pi, Cursor CLI, and T3 Code, making it easy to explore coding-agent workflows or switch between them with one command. Spawn supports cloud environments such as DigitalOcean, Sprite, Hetzner Cloud, AWS Lightsail, GCP Compute Engine, and Daytona, as well as a local machine or a throwaway local Docker sandbox.
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    Mem0

    Mem0

    Mem0

    Mem0 is a self-improving memory layer designed for Large Language Model (LLM) applications, enabling personalized AI experiences that save costs and delight users. It remembers user preferences, adapts to individual needs, and continuously improves over time. Key features include enhancing future conversations by building smarter AI that learns from every interaction, reducing LLM costs by up to 80% through intelligent data filtering, delivering more accurate and personalized AI outputs by leveraging historical context, and offering easy integration compatible with platforms like OpenAI and Claude. Mem0 is perfect for projects such as customer support, where chatbots remember past interactions to reduce repetition and speed up resolution times; personal AI companions that recall preferences and past conversations for more meaningful interactions; AI agents that learn from each interaction to become more personalized and effective over time.
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    Cloudroom

    Cloudroom

    Cloudroom

    Cloudroom is an open-source workspace for developers who use AI coding agents. Its desktop app brings local and cloud agent sessions into one interface, where users can start, monitor, and steer coding tasks across projects. Cloud threads run in separate cloud sandboxes with their own repository copy and Git branch, and can keep working after the user's computer is closed; local threads run on the user's machine. Cloudroom supports agent harnesses including Claude Code, Codex, Pi Agent, Cursor, and OpenCode. Users can bring their existing model subscriptions or provider credentials, review changes, retain chat history and traces, and transfer work between local and cloud environments. Cloudroom Core can be self-hosted on Ubuntu, while hosted cloud execution is available as a managed service. It is designed for software developers coordinating multiple AI-assisted coding tasks.
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    Subspace

    Subspace

    Subspace

    Subspace is an AI-native agent workspace designed to help developers and teams manage, coordinate, and collaborate with multiple coding agents in a single unified environment while preserving context across sessions. Instead of treating each AI interaction as isolated, the platform builds persistent memory in the background by compressing every conversation into structured observations such as decisions, blockers, and progress, which are continuously synthesized into a clear, evolving project state. This shared memory belongs to the workspace rather than any individual tool, allowing different agents like Claude Code, Codex, or others to seamlessly pick up where previous sessions left off without requiring repeated explanations or manual context transfer. Subspace integrates terminals, files, documentation, browser views, and git workflows into organized workspaces, enabling users to run multiple agents side by side and switch between projects almost instantly.
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    AgentScreenshots

    AgentScreenshots

    AgentScreenshots

    AgentScreenshots gives AI coding agents a practical visual feedback loop for frontend development. The `agentshot` CLI captures screenshots of localhost, preview, staging, or production pages, saves PNGs into your project, and lets the agent inspect the rendered pixels before claiming the UI is done. It is built for Claude Code, Codex, Cursor, OpenCode, and other coding agents.
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    Campfire SMS

    Campfire SMS

    Campfire SMS

    Campfire SMS is a hosted, two-way SMS bridge for developers who use AI coding agents. It connects an agent running in a local session—or a configured cloud agent—to a verified phone number, sending task updates and questions to the user’s regular text inbox and passing replies back to the agent. The service supports MCP-compatible clients, with setup instructions for Codex, Claude Code, Cursor, Grok Build, OpenCode, and Grok Bot. Local agents require the computer and session to remain active; configured Grok Bot routines can run in the cloud while the computer is off. Users can receive build or test completion notices, respond to agent questions, and guide ongoing coding work away from the desk. SMS delivery uses a hosted account and message credits; the agent’s own model, tools, and task execution remain in the agent environment.
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    Memdex

    Memdex

    Memdex

    Memdex turns every AI conversation into reusable local memory by auto-saving chats and bringing the right context back when users need it across ChatGPT, Claude, Gemini, and more. It solves the problem of scattered AI conversations that are hard to find, stuck inside separate tools, and difficult to reuse when starting a new chat. Users can click the Memdex button to save a conversation or turn on auto-save so every AI conversation is captured automatically across supported tools. Memdex then detects relevant context as the user types in any AI tool, highlighting matching words from saved conversations, like spell-check, but for context. When a match appears, users can attach the full previous conversation with one click, allowing the AI to pick up where the earlier discussion left off without re-explaining background, preferences, or project details.
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    HOL Guard
    HOL Guard is a local-first runtime security layer for AI agents that watches what an AI assistant is about to do and stops risky actions before they happen. It sits between the agent and the computer, evaluating supported tool calls and local artifacts for threats such as secret and credential exposure, destructive commands, prompt-injection-driven actions, malicious or changed packages, risky MCP configuration, and unsafe plugins, skills, hooks, and settings. Known threats can be blocked automatically, while ambiguous actions are paused for user approval so people remain in control. Guard runs entirely on the developer’s machine, works offline, and does not upload files, prompts, or passwords. Local checks typically complete in under 50 milliseconds and require no changes to existing code or routines. It supports coding agents including Claude Code, Cursor, Codex, Gemini CLI, OpenCode, Hermes, and OpenClaw, with tailored integrations that inspect actions before execution.
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    Multilith

    Multilith

    Multilith

    Multilith gives AI coding tools a persistent memory so they understand your entire codebase, architecture decisions, and team conventions from the very first prompt. With a single configuration line, Multilith injects organizational context into every AI interaction using the Model Context Protocol. This eliminates repetitive explanations and ensures AI suggestions align with your actual stack, patterns, and constraints. Architectural decisions, historical refactors, and documented tradeoffs become permanent guardrails rather than forgotten notes. Multilith helps teams onboard faster, reduce mistakes, and maintain consistent code quality across contributors. It works seamlessly with popular AI coding tools while keeping your data secure and fully under your control.
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    SenseLab

    SenseLab

    SenseLab

    SenseLab is a cloud-based memory and learning layer for AI agents and agent-driven applications. It provides shared knowledge spaces (“Rooms”) where agents and teams can record, retrieve, and share context across sessions, models, and tools. Agents can store findings, decision context, and outcomes; the service maintains versioned records and uses outcome feedback to adjust confidence in knowledge. SenseLab provides an MCP server and Python and TypeScript SDKs for connecting agent clients and applications through API-key access. Teams can use it to carry context between agents, coordinate work, review decision histories, and retain structured traces for evaluation or model-training workflows. It is intended for developers and organizations building systems with multiple AI agents. The vendor also documents a self-hosting option through its open-source codebase.
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    Briefkin

    Briefkin

    Briefkin

    Briefkin is a cloud dashboard for monitoring AI coding agents and automation jobs. It collects progress updates, completed work, blockers, decisions and metrics from tools such as Claude Code, Codex, Cursor, OpenCode and Gemini CLI, as well as scripts and cron jobs, and organizes them into project cards. Live task checklists and status updates help developers follow work across sessions, while action requests can be answered from the dashboard or through Telegram alerts. Users can send structured events through a documented HTTP API, create custom widgets, view metric trends, and run scheduled routines on their own computers with their chosen agents. A command-line installer configures supported agents, skills and hooks. Briefkin is aimed at developers and independent makers coordinating multiple agents and automations. The hosted service is free during its open beta.
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    Orchestrator.inc

    Orchestrator.inc

    Orchestrator.inc

    Orchestrator.inc is a desktop application for coordinating multiple AI coding agents across software projects. Developers can assign tasks to agents such as Claude Code, Codex, Cursor, Aider, and OpenCode, while each agent works in an isolated Git worktree. A unified workspace brings together agent sessions, project status, code changes, pull requests, CI results, review feedback, and merge readiness. Developers can supervise agents, route CI failures or review requests to the relevant session, launch reviewer agents, and preview project web apps. Orchestrator.inc supports GitHub and GitLab and offers iOS and Android apps for connecting to desktop sessions. The local app is free under Apache 2.0; paid cloud plans add remote agent execution and cloud pull-request and CI workflows. It is designed for software developers and teams running parallel AI-assisted development tasks.