In-depth Report
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Unabyss is a universal context layer built based on the MCP (Model Context Protocol) protocol, which solves the increasingly prominent pain point of "information islands" among AI tools. It automatically extracts information from 25+ applications that users use daily, such as Slack, Gmail, Notion, and GitHub, structures it into searchable context, and then distributes it to 10+ AI tools such as Claude, ChatGPT, and Cursor through the MCP protocol. It debuted in May 2026 and won the first place on Product Hunt on the same day. It was reinstalled as "Unabyss for Claude" on July 17, with pricing ranging from the free version to the Max version of $79/month. Simply put, it makes it possible for "every AI to understand you" instead of starting from scratch for every conversation.
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The company behind Unabyss is OneType Prosta Spółka Akcyjna, registered in Warsaw, Poland (ul. Fabryczna 4A/11, 00-446 Warszawa), and co-founder Philip Kubinski is a serial entrepreneur. The product received funding from ElevenLabs Grants. In May 2026, it debuted on Product Hunt and won the No. 1 product of the day. In less than two months, it was completely restructured based on user feedback - from an independent configuration page mode to a "Claude-first, MCP-first" architecture. On July 17, it was released for the second time with the new positioning "Unabyss for Claude". It ranked sixth on the day and ninth on the weekly list. Unabyss is targeting a rapidly expanding market: heavy AI users switching between multiple LLM and AI tools simultaneously, each of which learns about the user from scratch. Many developers have written rudimentary context synchronization solutions (handwritten .md files, git management prompt templates), but they lack a systematic product solution. The Unabyss team judges that as the MCP protocol gradually becomes an industry standard, the demand for the context layer will change from "icing on the cake" to "infrastructure level" rigid demand. As of July 2026, the platform has synchronized more than 400,000 memory sources.
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The core capabilities of Unabyss can be divided into several levels: Automatic extraction and structuring. After connecting to 25+ applications such as LinkedIn, Notion, Gmail, Slack, GitHub, Obsidian, Google Drive, X/Twitter, etc., Unabyss can complete the first context extraction in about 90 seconds and automatically generate structured description files such as persona.md, voice.md, company.md, etc. These files are not cold JSON snapshots, but human-readable text that users can review and manually adjust. The key is "self-update" - after the source application data changes, the context will be automatically refreshed without the need for secondary intervention by the user. MCP native delivery. This is the most distinctive technical label of Unabyss. Each connected AI tool obtains an independent MCP token (format such as una_live_...), and the docking can be completed with a single command of claude mcp add. Supports mainstream tools such as Claude Desktop, Claude Code, Cursor, ChatGPT, Codex, Gemini, Perplexity, OpenClaw, VS Code, Hermes, etc. The advantage of MCP-first is that it follows open standards rather than being locked into a single ecosystem. Contextual segmentation and permission control. Unabyss will automatically label each piece of contextual information according to five dimensions: topic, confidence, sensitivity, source and personal vs professional. When retrieving, only the information fragments related to the current problem are pulled, rather than the user's complete history. The permission level provides four switchable scopes: unlimited, exclude private information, exclude company confidential information, exclude the entire source application - executed during retrieval, sensitive context will not enter the model prompt. In the words of co-founder Philip in the PH comment area: "The scope of opening can be wider for Claude, narrowed for ChatGPT, and permissions can be independently controlled at the token level."Token efficient retrieval. Unabyss claims that its compression capabilities can reduce token usage to one-tenth that of traditional RAG schemes—instead of throwing all loosely matched fragments into the prompt, it extracts only those lines that actually answer the question. The actual effect depends on the quality of the data, but in the era of large token pricing models, this direction is undoubtedly the right one. Judging from the usage experience, the getting started process is very simple: register (email or Google login, no payment method required) → connect 1-2 data sources → wait for about 90 seconds for automatic extraction → review the generated context → generate scoped MCP tokens for each AI tool → copy the token to the MCP configuration of the target tool. The whole process does not require writing code, but there is still a certain threshold for non-technical users to understand the concept of MCP. Compared with competing products, the biggest difference of Unabyss is "cross-tool" - ChatGPT Memory only works in ChatGPT, Claude Projects only works in Claude, and the same message on Unabyss "I am the founder, focus on growth, Stack is MCP" can be read in Claude, Cursor and ChatGPT. Its positioning is not to replace the memory function of an AI, but to provide a portable context layer at the bottom.
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Unabyss offers a free trial and two-tier paid plans, plus team customization: Free trial: Sign up and get $25 in credit, no card required, and experience full features and integrations. The free tier has a maximum of 100 projects, which is basically enough for light user testing. Pro ($13/month paid annually / $15/month paid monthly): Up to 3 MCP connected proxies, up to 20 connected accounts, premium usage tier. Max ($79/month paid annually / $89/month paid monthly): Unlimited agents, unlimited connected accounts, unlimited usage, priority support and early access to new features. Team (customized price): Includes all Max features and supports team sharing context layer. All paid plans come with a 7-day free trial. In terms of pricing, Pro is aimed at heavy individual AI users, Max is aimed at “effervescent” level users who indulge in AI tools every day, and Team is aimed at small teams. One thing worth noting is that the unit price of pay-as-you-go credits is not disclosed on the pricing page. The early version used a $5 credit limit + pay-as-you-go plan (free card binding), which has now been adjusted to a fixed subscription system. For users who rely heavily on production environments, it is recommended to confirm with the team whether there are usage limits, the billing method after the limit is exceeded, and whether there is an SLA commitment before use.
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Product Hunt’s second launch (July 17) garnered 507 votes and 116 comments, with an overwhelming response from the community but also some pointed questions. Positive feedback focused on: solving real pain points - "I finally don't have to paste my background story in every conversation"; after connecting Gmail and notes, Claude directly remembered the project context without retyping; the granularity of permission control is better than expected, "it is not a one-size-fits-all 'share everything' type product"; the design direction of MCP-first is recognized by the developer community, which believes that it follows the ecological trend rather than creating a closed memory system. Neutral/questioning voices deserve more attention: Many senior users raised the problem of "contradiction propagation" in context management in the comments - when Claude writes one fact and GPT writes another contradictory fact, how does Unabyss arbitrate? Who guarantees context consistency? A developer who claimed to have handwritten a similar solution (plain files, one fact per file) said bluntly: "The failure mode is not retrieval, but propagation - if one agent writes a slightly biased fact, other agents will confidently inherit it." The co-founder admitted in his reply that this is a "hard problem" and that versioning and decay mechanisms are being developed but have not yet been launched. In addition, the payment model has been questioned - users are already paying subscription fees for ChatGPT/Claude/Cursor, and Unabyss consumes additional tokens in the middle to build and maintain shared memory, which is equivalent to "secondary payment". The team did not provide a clear response to this question.
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Third-party reviews have generally been positive about Unabyss, but with caution. Toolworthy.ai gave a Pro/Con evaluation, recognizing that it solves real daily frictions, has wide integration coverage, and has a good sense of MCP-first direction. However, it also pointed out that the early product integration depth was uneven, there was no audit log/team management/SSO, and it was not ready for deployment to regulated enterprises. Rightaichoice.com gives a Viability Score of 95/100 (based on momentum, funding runway, wrapper dependency and other signals) and believes that "the probability of still operating after 12 months is high." An in-depth analysis by Kingy.ai positioned it as "contextual infrastructure rather than AI memory", emphasizing that its difference from developer SDKs such as Mem0 and Letta is that it is oriented to end users rather than developers. From the perspective of the competitive landscape, the "AI context management layer" where Unabyss is located is rapidly becoming crowded. Direct competing products include Mem (mem.ai), Rewind (rewind.ai), Limitless (limitless.ai), and developer SDK categories Mem0, Zep, and Cognee. The difference of Unabyss lies in: MCP-first instead of private API, lower docking cost; it also supports reading and writing (the agent can write new memory) instead of only the consumer side; it is oriented to end users rather than developers, lowering the threshold for use. But the disadvantages are also obvious: brand trust is still shallow, it relies on the stability of third-party APIs, and the "unified context layer" is essentially a trust-intensive product (users need to hand over the reading rights of Gmail/Slack/GitHub to a Polish startup).
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There are three core risks facing Unabyss. Trust risk. This is what all context management products face. Giving read permissions (or even write permissions) to Gmail, LinkedIn, Slack, and GitHub to a third-party service is an extremely high level of trust in itself. Unabyss's privacy policy states that data is stored in EEA (Contabo hosting), sub-data processors include OpenAI/Anthropic/Google/Stripe/Cloudflare, etc., and transmission and storage encryption (TLS 1.3 + AES-256). These measures are compliant at the textual level, but true trust takes time and security incidents to test. For teams involved with customer confidentiality or with strict compliance requirements, it is recommended to connect only low-risk sources until enterprise-level controls (audit logs, SSO, data localization) go live. Contextual consistency issues. As mentioned before, when multiple AI tools write contradictory facts on the same topic, Unabyss lacks version control, conflict arbitration, and traceability mechanisms. Currently there is no clear interface for users to check "which application wrote this fact at what time". In the absence of provenance mechanisms, shared context may amplify rather than eliminate the illusion. The co-founders say the team is working on the issue, but it's still in the planning stages as of July 2026. Business model sustainability. The free tier is $25, and the Pro is only $13/month. The pricing seems low for an infrastructure product. According to the observations of a few users, the platform has recently experienced a pricing adjustment from pay-as-you-go to subscription-based, indicating that the team is still exploring the most suitable monetization path. If the unit economic model fails (the storage and computing costs of context synchronization are not low), there is a risk of price increases or the free tier being closed. MCP ecological dependence. Unabyss's architecture is highly dependent on the popularity of the MCP protocol in the industry. If mainstream AI tools do not natively support MCP or launch their own closed context protocols, Unabyss's docking advantages will be weakened. Fortunately, Anthropic is strongly promoting MCP standardization. Many tools will have native support in 2026. This direction seems to be healthy.
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Unabyss is most suitable for three types of people: multi-agent developers (who write code with Cursor, Claude Code, and ChatGPT at the same time, and hope that each tool knows the project context), multi-tool founders (who need AI to uniformly understand company strategy, product roadmap, and customer portraits), and cross-customer consultants (maintain independent context slices for each customer, without confusion when switching). Not suitable for: Users who only use one AI tool (built-in memory function is enough), regulated teams with strict compliance (no audit logs and data localization), privacy-sensitive users who are unwilling to hand over personal information to third-party services. Suggestions for use: Start small, first connect to sources with "moderate information density" such as Notion or Obsidian, review the automatically generated context files, confirm that there are no problems, and then gradually expand the scope of data sources. Each agent is granted the minimum necessary permissions and context accuracy is checked periodically. For facts on which critical business decisions depend, it is recommended to retain external sources and cross-verify them - shared context reduces the hassle of repeating instructions, but there is no guarantee that every preserved fact is correct.
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Unabyss does not do AI or RAG middleware. What it does is to build a context pipeline between "your data" and "AI tools" that you can own and control. In 2026, when "multi-model and multi-tool" will become standard, this direction hits a real pain point that amplifies over time. The product idea is clear, the execution rhythm is tight (debut in May → major version reconstruction in July), and the community feedback is positive. But it is still in the early stages: uneven integration depth, lack of context consistency mechanisms, and pricing models are still being explored. If it can solve the two hard problems of provenance and conflict arbitration, Unabyss may become a moderate standard piece in the AI infrastructure stack. Until then, it's a contextual tool worth trying, worth watching, but not recommended to bet your entire fortune on.
User Reviews
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GraceCook_7—免费额度 $25 够我折腾好几天了，先跑通流程再看看要不要付费。 -
Robert_Gonzalez_X79—试了一下，把 Notion 和 Gmail 连上之后，Claude 确实记住了我上周的项目进度，不用再重新粘贴一遍背景信息了。而且我发现把日历也接上去之后，它能直接知道我今天有哪些会议安排，这个体验确实值得点赞。不过第一次配置花了点时间理解 MCP 的概念。 -
0qcek8iu—最大的担忧还是数据安全。把 Gmail、Slack、GitHub 的读权限都给一家波兰初创公司，心里还是有点不踏实。 -
Sara.Phillips_88—老哥，免费层只支持 100 个项目，稍微用一下就超了，升级到 Pro 要 $13/月，性价比自己掂量。 -
Adam_Morgan_88—权限控制确实比我想象的细，可以单独给 Claude 和 ChatGPT 不同的访问范围，这个很实用。 -
3dpon7—终于有人做这个了！之前每次在 Claude 和 ChatGPT 之间切来切去都要重新喂一遍 context，包括我是做什么的、项目背景、当前进度，有时候光贴这些就得弄好几分钟，太痛苦了。Unabyss 这个思路是对的，MCP 协议的方向也看好。 -
KellyKim_2024—有个疑问，如果我在 Claude 里写了一条 Notes，GPT 那边也写了一版不同的，到底以哪个为准？团队回复说正在做版本化机制，但目前还没上线。 -
EdwardMurray—MCP 还是太新了，目前只支持 Claude Desktop、Cursor 这几个主流工具，ChatGPT 的支持还不太稳定，我试了几次连接都断开了。如果主力用的是 ChatGPT 或者 Gemini 生态的话，这个工具的价值会打不少折扣，建议先观望。 -
Jack.Martinez5—现在 $13/月 定价还行，就怕后面用户多了涨价。基础设施类产品一涨价就很被动，因为数据和上下文都绑在上面了。 -
RobertWerner—设置了大概 20 分钟，把 GitHub、Notion 和 Slack 接上去了。第一次提取时间确实不到两分钟，persona.md 生成出来还挺像那么回事。不过有个小问题是 LinkedIn 的导入不太稳定，试了两次才成功，可能是刚上线的小 bug。 -
唐海龙—对于同时用 Cursor 和 Claude Code 的开发者来说真的挺香的，两个工具的上下文终于统一了，不用再各说各话。 -
FAcru_lab—能不能加个功能让我看看每个 AI 工具到底读取了哪些上下文？现在有点黑盒，不知道它到底把我的哪些信息传过去了。 -
SamuelMorales5205—感觉更适合独立开发者和小团队用，公司级别的话没有审计日志和 SSO 过不了合规那一关。 -
realMineaSalmi—昨天接上 Fathom 会议记录之后，今天早上问 Claude 昨天跟客户开会说了什么，它直接给出了要点。这感觉确实不一样。 -
DonnaWatson_2021—说实话，自己手动写个 MCP server 也不是不行，但 Unabyss 胜在开箱即用，20 多个连接器都配好了，省了不少功夫。 -
Shirley.Cox—已经订了 Pro 版，$13/月 不算贵，以前手动维护 prompt 模板的时间和精力成本远不止这个数。 -
Lisa.Hall_2021—PH 上看到就试了，连接过程很顺，一条命令就搞定了 MCP token。关键是后续不用再维护，省心。 -
Donna_Sanchez_7—很想知道如果一个连了 6 个月的 Notion 突然断开了，之前已经生成的记忆会不会自动清除？产品文档里好像没提这个场景。如果不清理的话那些 stale context 一直在，反而会误导 agent 给出过时的回答，希望团队能给个明确的机制说明。 -
HEvans—有个疑问，当多个 AI 对同一个事实产生矛盾的时候，Unabyss 怎么决定保留哪个版本？团队说有方案但还没实现，这就尴尬了。 -
Pamela_BaileySr—链接了 LinkedIn 之后自动生成了个人简介，直接拿来当 Cursor 的系统提示用了，效果还行。