Alternatives to Membase
Compare Membase alternatives for your business or organization using the curated list below. SourceForge ranks the best alternatives to Membase in 2026. Compare features, ratings, user reviews, pricing, and more from Membase competitors and alternatives in order to make an informed decision for your business.
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Maximem
Maximem
Maximem is an AI context management and memory platform designed to give generative AI systems a persistent, secure memory layer that retains and organizes information across conversations, applications, and models. Large language models typically operate with limited session memory, meaning they lose context between interactions and require users to repeatedly provide the same background information. Maximem addresses this limitation by creating a private memory vault that stores relevant context, preferences, historical data, and workflow information so AI systems can reference it in future interactions. It operates between AI models and applications, ensuring that conversations, knowledge, and user data are consistently available across different tools and sessions. This persistent memory allows AI assistants to deliver responses that are more personalized, accurate, and context-aware because the system can retrieve previously stored information. -
2
claude-mem
cmem.ai
claude-mem is an offline-first cloud memory for AI agents, built around an open source engine and a cloud sync layer that links agent memory everywhere through one private MCP link. It is designed so coding agents and AI assistants do not start from zero every session, every machine, or every editor. claude-mem takes notes while an agent works, capturing decisions, fixes, dead ends, environment notes, architecture choices, and other structured observations in a temporal database. CMEM Cloud then mirrors that local memory behind a private Model Context Protocol endpoint, allowing any compatible agent or IDE to read and write the same memory across tools such as Claude Code, Cursor, Windsurf, OpenCode, Codex CLI, Gemini CLI, and VS Code. It works locally first, with or without a network, while keeping memory synchronized when cloud access is available.Starting Price: Free -
3
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.Starting Price: $2,500 per month -
4
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. -
5
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.Starting Price: $49 per month -
6
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.Starting Price: $20 per month -
7
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. -
8
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.Starting Price: $19 per month -
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myNeutron
Vanar Chain
Tired of repeating to your AI? myNeutron's AI Memory captures context from Chrome, emails, and Drive, organizes it, and syncs across your AI tools so you never re-explain. Join, capture, recall, and save time. Most AI tools forget everything the moment you close the window — wasting time, killing productivity, and forcing you to start over. MyNeutron fixes AI amnesia by giving your chatbots and AI assistants a shared memory across Chrome and all your AI platforms. Store prompts, recall conversations, keep context across sessions, and build an AI that actually knows you. One memory. Zero repetition. Maximum productivity.Starting Price: $6.99 -
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PlatformPilot
DynG AI
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 knowledgeStarting Price: $100 -
11
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.Starting Price: $19.99 per month -
12
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.Starting Price: Free -
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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. -
14
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.Starting Price: Free -
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Backboard
Backboard
Backboard is an AI infrastructure platform that provides a unified API layer giving applications persistent, stateful memory and seamless orchestration across thousands of large language models, built-in retrieval-augmented generation, and long-term context storage so intelligent systems can remember, reason, and act consistently over extended interactions rather than behave like one-off demos. It captures context, interactions, and long-term knowledge, storing and retrieving the right information at the right time while supporting stateful thread management with automatic model switching, hybrid retrieval, and flexible stack configuration so developers can build reliable AI systems without stitching together fragile workarounds. Backboard’s memory system consistently ranks high on industry benchmarks for accuracy, and its API lets teams combine memory, routing, retrieval, and tool orchestration into one stack that reduces architectural complexity.Starting Price: $9 per month -
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EverMemOS
EverMind
EverMemOS is a memory-operating system built to give AI agents continuous, long-term, context-rich memory so they can understand, reason, and evolve over time. It goes beyond traditional “stateless” AI; instead of forgetting past interactions, it uses layered memory extraction, structured knowledge organization, and adaptive retrieval mechanisms to build coherent narratives from scattered interactions, allowing the AI to draw on past conversations, user history, or stored knowledge dynamically. On the benchmark LoCoMo, EverMemOS achieved a reasoning accuracy of 92.3%, outperforming comparable memory-augmented systems. Through its core engine (EverMemModel), the platform supports parametric long-context understanding by leveraging the model’s KV cache, enabling training end-to-end rather than relying solely on retrieval-augmented generation.Starting Price: Free -
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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.Starting Price: $0 -
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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.Starting Price: $10 per month -
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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.Starting Price: Free -
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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.Starting Price: $20 per month -
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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.Starting Price: $12 per month -
22
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.Starting Price: $7 per month -
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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. -
24
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.Starting Price: $249 per month -
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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.Starting Price: $20 per month -
26
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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Anuma
Anuma
Anuma is a privacy-first, multi-model AI platform that unifies access to leading proprietary and open-source AI systems within a single interface while giving users full ownership and control over their data. It allows users to interact with models such as ChatGPT, Claude, Gemini, Grok, and open source alternatives like DeepSeek or Qwen without switching tools or losing context, enabling seamless workflows across different AI engines. At its core is a Private Memory Layer that stores user preferences, conversation history, and context in an encrypted, user-controlled environment, ensuring that sensitive data is not accessible to providers or stored centrally. This memory persists across sessions and models, allowing users to continue tasks without re-explaining information and maintaining continuity in complex workflows. It supports comparing multiple models simultaneously, building custom mini-apps and automations without code.Starting Price: $9.99 per month -
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Mira
Mira
Mira is a truly personal AI agent that acts inside Telegram across personal and group chats with zero setup. Built to live in the messenger where users already work, Mira remembers, understands, adapts, and grows with every chat, turning Telegram into an AI execution layer for planning, creating, searching, summarizing, and acting. Its structured memory layer learns from real conversations across personal and group contexts, building shared memory with AI search, insights, and context retrieval so users do not have to repeat preferences, project details, recurring workflows, or communication style every session. Mira can manage tasks, set reminders, draft and send messages, search the web, summarize documents, generate content, and automate multi-step workflows while staying available inside the same chat interface. The AI Content Studio helps users create high-quality content instantly inside Telegram, while the Content Creator Agent can create, edit, and schedule content end to end. -
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Slock
Botiverse
Slock is a real-time collaboration platform built around an “agent-native” approach, where AI agents are treated as full participants in the workspace rather than external tools. It provides familiar collaboration structures such as channels, direct messages, and threads, but redefines them so that both humans and AI agents operate within the same conversation layer, with no need for context switching or copying information between systems. Agents are persistent entities that live inside these channels, continuously observing messages, responding naturally, and retaining memory across sessions, allowing them to maintain long-term context and contribute meaningfully over time. A key aspect of the platform is its execution model, which runs locally on the user’s own machine through a lightweight daemon, giving users full control over compute and ensuring that sensitive data does not leave their environment.Starting Price: Free -
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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.Starting Price: Free -
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Coral
Coral
Coral is an open-source query layer that allows AI agents and developers to access data across APIs, databases, and file systems using SQL. The platform turns connected sources such as GitHub, Slack, Linear, Datadog, Sentry, Stripe, and PagerDuty into readonly tables that can be explored and joined together. Instead of building custom integrations, ETL pipelines, or API wrappers, teams can use Coral to query multiple systems from one runtime. Coral supports CLI and MCP access, making it usable with tools such as Claude Code, Codex, and other agent frameworks. The platform handles authentication, pagination, rate limits, schema mapping, caching, and semantic hints to improve accuracy and reduce cost. Coral helps engineering teams give AI agents safer, faster, and more useful context for production workflows.Starting Price: $249/month -
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HybridClaw
HybridAI
HybridClaw is an enterprise-grade AI agent platform designed to function as a persistent digital coworker that unifies workflows across communication channels, tools, and execution environments into a single intelligent system. It provides a “shared assistant brain” that operates consistently across Discord, Teams, iMessage, WhatsApp, email, web interfaces, and terminal environments, ensuring that all users interact with the same memory, behavior, and execution logic. It combines persistent workspace memory, semantic recall, and knowledge-graph relationships to maintain context across long-running conversations and tasks, allowing it to remember projects, decisions, and interactions over time. HybridClaw enables end-to-end task execution by securely running tools, commands, and workflows within sandboxed environments, applying guardrails, permission controls, and audit logs to ensure safe and controlled automation.Starting Price: Free -
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MiMo Code
Xiaomi Technology
MiMo Code is a terminal-native AI coding assistant designed to live inside the developer’s computer, understand the project more deeply over time, and improve as it works. It can read and write code, run commands, manage Git, and keep a persistent project context across sessions through a built-in memory system. Instead of relying on the model to remember on its own, MiMo Code uses project memory, conversation checkpoints, scratch notes, task progress, and SQLite FTS5 full-text search to preserve rules, architecture decisions, session state, and ongoing work. When context nears the limit, it reconstructs the working state from the latest checkpoint, memory, task progress, and recent messages so the agent can continue rather than start from scratch. Multiple agents support different workflows, build for full-permission development, plan for read-only analysis, and compose for specs-driven development. -
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Acontext
MemoDB
Acontext is a context platform for AI agents. It stores multi-modal messages/artifacts, monitors agents' task status, and runs a Store → Observe → Learn → Act loop that identifies successful execution patterns, so autonomous agents can act smarter and succeed more over time. Developer Benefits: Less Tedious Work: Store multi-modal context and artifacts in one place by integrating all context data without configuring Postgres, S3, or Redis, and it only requires a few lines of code. Acontext handles repetitive, time-consuming configuration tasks, so developers don’t have to. Self-Evolving Agents: Similar to Claude Skills, which require predefined rules, Acontext allows agents to automatically learn from past interactions, reducing the need for constant manual updates and tuning. Easy Deployment: Open-source, one-command setup, One-line install. Ultimate Value: Improve agent success rates and reduce running steps, then save costs.Starting Price: Free -
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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.Starting Price: Free -
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Hamster
Hamster
Hamster is an AI-first workspace designed to help developers and teams plan, structure, and execute projects by providing persistent context to AI coding agents across tools and workflows. It allows users to define a clear plan, brief, and context that can be injected into multiple AI development tools such as Claude, Codex, Gemini, Copilot, and others, ensuring that every agent operates with the same understanding of the project. Instead of relying on isolated prompts, Hamster centralizes instructions and project knowledge so agents can generate more accurate, consistent, and goal-aligned outputs throughout the development process. It works as a coordination layer for AI-driven building, enabling users to move their plans seamlessly between tools while maintaining continuity and reducing context loss. By supporting a wide range of AI coding environments, Hamster acts as a universal interface that connects different models and systems into a cohesive workflow.Starting Price: Free -
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StateFabric
J Gregory Technology Ltd.
StateFabric a small infrastructure layer for AI agents that need more context than chat history. Once an agent starts using tools, running for longer sessions between restarts, ‘keep the messages in memory’ doesn’t really cut it. You need to know: - what happened? - what state changed? - which tools were called? - what context should go into the next model turn? StateFabric stores an append-only event log for agent runs, then derives working context from it. Today it supports: - Durable sessions and event storage - User/model/tool event timelines - Reconstructed session state from stored events - Compacted model-facing context - A dashboard for inspecting sessions, raw payloads, compaction artefacts, and usage - Google ADK integration via @statefabric/adk - Direct Node/REST usage via @statefabric/client (for custom runtimes)Starting Price: £10/month -
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Moxt
Moxt
Moxt is an AI-native workspace designed to help teams build and collaborate with autonomous AI agents that can research, write, analyze, and execute tasks alongside humans in a shared environment. It acts as an “operating system for agents,” bringing together files, memory, tools, and skills so AI teammates can perform real work without constant prompting or re-explaining context. It introduces persistent AI assistants (called “momo”) for each user, as well as shared AI teammates that collaborate across the organization, learn from interactions, and improve over time through a shared memory layer. These agents can autonomously generate reports, build dashboards, draft documents, analyze data, and prepare workflows, often completing tasks proactively or on a schedule without direct input. Moxt integrates with tools like Slack, allowing users to interact with AI agents directly in existing workflows, while outputs are saved as structured files in a centralized workspace. -
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Mistral Agents API
Mistral AI
Mistral AI has introduced its Agents API, a significant advancement aimed at enhancing the capabilities of AI by addressing the limitations of traditional language models in performing actions and maintaining context. This new API integrates Mistral's powerful language models with several key features, built-in connectors for code execution, web search, image generation, and Model Context Protocol (MCP) tools; persistent memory across conversations; and agentic orchestration capabilities. The Agents API complements Mistral's Chat Completion API by providing a dedicated framework that simplifies the implementation of agentic use cases, serving as the backbone of enterprise-grade agentic platforms. It enables developers to build AI agents capable of handling complex tasks, maintaining context, and coordinating multiple actions, thereby making AI more practical and impactful for enterprises. -
40
Memgraph
Memgraph
Memgraph is a high-performance, in-memory graph database that powers real-time AI context. It serves as the graph engine for GraphRAG pipelines, AI memory systems, and agentic workflows - delivering sub-millisecond multi-hop traversals with full provenance for any system that needs structured, connected context alongside semantic search. The same architecture that makes Memgraph the context layer for AI also drives real-time graph analytics across fraud detection, network analysis, infrastructure monitoring, and other operational use cases where speed and connectivity matter. -
41
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.Starting Price: Free -
42
Tobira
Tobira
Tobira is an AI agent networking platform that enables autonomous agents to discover, communicate, and collaborate with one another through a shared infrastructure designed for structured interaction and task execution. It introduces a system where agents can have unique addresses, similar to email, allowing them to be identified, contacted, and coordinated across different workflows and environments. It includes a public or semi-public memory layer that agents can use to store and expose relevant information, enabling better context sharing and more intelligent interactions between agents. Tobira functions as a matchmaking and discovery layer, surfacing relevant agents, opportunities, or tasks based on structured data and defined capabilities, effectively connecting demand with execution in an automated way. By acting as a communication protocol and coordination layer, it allows agents to operate beyond isolated tasks, forming networks that can collaborate and exchange data.Starting Price: Free -
43
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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PrimeClaws
PrimeClaws.com
PrimeClaws is a managed hosting platform for OpenClaw autonomous AI agents that lets users deploy and run their OpenClaw instances in the cloud with minimal setup and no DevOps knowledge; it focuses on providing a simple, one-click deployment process so an AI assistant built on OpenClaw can run 24/7 without requiring your laptop or local server to stay on. With support for major LLMs (like Claude, GPT, and Gemini) and persistent memory across sessions, agents can continue working and remembering context over time, and it integrates with messaging channels such as WhatsApp, Telegram, Slack, and others, so your AI assistant can be accessed and interacted with through familiar communication apps. Hosting through ClawHost abstracts infrastructure management, offering global cloud operations with persistent uptime, root access on self-hosted VPS environments, and full control over your agent’s environment, while automatically keeping the AI instance running.Starting Price: $9.99/month -
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XHawk
XHawk
XHawk is an AI-native developer platform designed to transform scattered code, documentation, and team knowledge into a unified, searchable system of context. It captures every coding session, commit, and decision, automatically organizing them into a living knowledge graph that evolves with the codebase. It converts code changes and development activity into structured, indexed documentation, ensuring that knowledge stays synchronized with every pull request and eliminating gaps between code and documentation. It provides a shared context layer that enables both humans and AI coding agents to plan, code, review, test, and operate systems with a consistent understanding, reducing hallucinations caused by missing context. XHawk includes features such as session intelligence, where every git commit syncs session history and agent reasoning, creating a permanent, searchable record of how software is built. -
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Koog
JetBrains
Koog is a Kotlin‑based framework for building and running AI agents entirely in idiomatic Kotlin, supporting both single‑run agents that process individual inputs and complex workflow agents with custom strategies and configurations. It features pure Kotlin implementation, seamless Model Control Protocol (MCP) integration for enhanced model management, vector embeddings for semantic search, and a flexible system for creating and extending tools that access external systems and APIs. Ready‑to‑use components address common AI engineering challenges, while intelligent history compression optimizes token usage and preserves context. A powerful streaming API enables real‑time response processing and parallel tool calls. Persistent memory allows agents to retain knowledge across sessions and between agents, and comprehensive tracing facilities provide detailed debugging and monitoring.Starting Price: Free -
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Macro
Macro
Macro is an open-source collaborative workspace that brings email, messaging, documents, tasks, calls, AI agents, and CRM together in a single application. The platform creates a shared AI memory that continuously connects information across the workspace, giving users and AI agents complete context for collaboration and decision-making. Macro features a keyboard-first email client with AI-assisted productivity, unified inbox management, and fast search designed to streamline communication. It also includes collaborative documents, integrated team chat, task management, and CRM capabilities that are tightly connected instead of existing as separate tools. Built-in AI agents can summarize conversations, manage tasks, generate content, and provide company-wide knowledge based on shared workspace memory. Macro helps teams replace multiple disconnected workplace applications with a unified, AI-powered collaboration platform.Starting Price: $40/user/month -
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Cisco AI Canvas
Cisco
The Agentic Era marks a transformative shift from traditional application-centric computing to a new frontier defined by agentic AI, autonomous, context-aware systems capable of acting, learning, and collaborating within complex, dynamic environments. These intelligent agents don’t just respond to commands; they perform complete tasks, retain memory and context via large language models tailored for specific domains, and can scale across industries into the tens of millions. This evolution brings the need for a new operational mindset, AgenticOps, and a reimagined management interface built around three guiding principles, keeping humans thoughtfully in the loop to provide creativity and judgment, enabling agents to operate across siloed systems with cross-domain context, and deploying purpose-built models fine-tuned for their distinct tasks. Cisco brings this to life through AI Canvas, the industry’s first generative, shared workspace driven by a multi-data, multi-agent architecture. -
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Trylli AI
Trylli AI
Trylli AI is a next-generation AI voice calling system that replaces traditional telecalling with intelligent, human-like agents. It enables businesses to run inbound and outbound calls at scale, handling sales, support, reminders, HR interviews, and more. Agents can be built using ready templates, chat-based setup, or advanced workflows, with options for multi-agent deployment, shared or isolated memory, and even a “Super Agent” for context switching. Trylli AI integrates a knowledge base for domain-specific queries, supports English and Hindi (with future global languages), and offers customizable voices for personalized conversations. Batch calling allows large-scale campaigns like collections, renewals, or verifications. With detailed analytics, call recordings, role-based access control, and integrations via APIs, Slack, and CRM systems, Trylli AI provides businesses with a scalable, multilingual, and context-aware AI telecaller that works 24/7.Starting Price: $49/Month - 750 Minutes -
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Implement AI
Implement AI
Implement AI offers a tool that helps businesses deploy a scalable digital workforce of coordinated AI agents across sales, support, operations, and success functions, turning isolated AI tools into an AI Operating System (AIOS) that works with real business data and systems like CRM, email, voice, and messaging to execute tasks autonomously and collaboratively. Its AI agents are multi-skilled and role-specific, designed to find missed revenue opportunities, launch outbound campaigns, follow up inbound leads, deliver 24/7 customer support, triage tickets, analyze conversations for revenue signals, flag compliance risks, build dynamic knowledge bases, and transform call and email data into actionable insights. Unlike standalone chatbots, the AIOS provides shared memory and an agentic task engine that lets agents access live customer context, coordinate workflows, trigger tasks using business rules, and scale across departments.
