Key Takeaways

• Dot AI's discontinuation by New Computer makes continuity of personal memory the primary criterion for evaluating replacement apps.

• Evermind EverOS stores memories as local Markdown files with SQLite and LanceDB, achieving 93.05% accuracy on the LoCoMo benchmark.

• Persistent long-term memory works through four mechanics: storage, retrieval, memory editing, and cross-session continuity across separate sessions.

• Alternatives split into two tracks: companion and emotional memory (Evermind, Personal.ai, Replika) and productivity and knowledge memory (Pieces, ChatGPT Memory, Claude Projects).

• ChatGPT Memory's fact-saving decisions are opaque, while Claude Projects scopes memory to individual project boundaries with no cross-project recall.

• Data ownership varies significantly: Evermind uses Apache 2.0 open-source local storage, while ChatGPT retains training rights unless users explicitly opt out.

• Users switching from Dot AI should prioritize tools offering individual memory editing and export options, since accumulated personal context is difficult to rebuild.

What Is Dot AI and Why People Look for Alternatives

Dot AI is a personal AI companion built by New Computer. It stores and recalls details about your life over time, treating every conversation as a continuous thread rather than a fresh session. The app's defining feature is long-term memory: Dot learns your habits, relationships, goals, and recurring concerns, then surfaces that context in future conversations without prompting.

Users began searching for alternatives for 3 concrete reasons. First, New Computer announced it was winding down Dot AI, raising immediate concerns about what happens to the personal memories stored inside the app. Second, Dot's platform availability was narrow, limiting access for users outside its supported environments. Third, the pricing structure left free-tier users with restricted memory depth, pushing heavier users toward paid plans without a clear value ceiling.

The discontinuation concern is the sharpest driver. A personal AI companion's value compounds over months. Every preference logged, every life event recorded, every pattern recognized adds to a context layer that is genuinely difficult to rebuild elsewhere.

Losing that accumulated memory means starting over, not just switching apps. That irreversibility is why continuity of personal memory has become the primary criterion users apply when evaluating Dot AI alternatives. A portable personal AI memory layer reduces the risk of rebuilding years of context after a vendor change.

Our pick

Evermind EverOS is the strongest Dot AI alternative for people who treat long-term personal memory as a durable, portable asset rather than a vendor-managed service.

EverOS stores every memory as local Markdown files backed by SQLite and LanceDB — no MongoDB, Elasticsearch, or Redis dependency. Each recalled fact traces directly to a specific.md source file that can be edited, locked, or rolled back. That auditability eliminates the black-box recall problem that makes Dot AI's discontinuation risk so costly.

On the LoCoMo benchmark, EverOS achieves 93.05% overall accuracy; on LongMemEval it reaches 83.00% accuracy; HaluMem recall lands at 90.04%. Retrieval completes in under 500 ms at p95. The underlying architecture is Apache 2.0 open source, so the memory layer is owned by the individual, not the platform.

EverOS is not the right fit for 3 specific profiles. These include teams wanting a fully managed, hands-off hosted service. They also include organizations whose infrastructure already runs on MongoDB or Elasticsearch, and buyers who want memory welded into a single fixed agent rather than a portable layer.

Start with EverOS, if you want memory you can inspect, edit, and carry across any model or agent harness: Build with EverOS Cloud — https://github.com/EverMind-AI/EverOS

How Long-Term Personal Memory Works in AI Apps

Persistent long-term memory stores facts about a user in a database that survives after a conversation ends. A context window, by contrast, holds only the text present in the current session.

A context window is a fixed-length buffer. Every token in an active conversation occupies space inside it. Once the session closes, that buffer is discarded entirely.

Persistent memory operates differently. There are 4 core mechanics that distinguish it from a context window:

• Storage — the system writes selected facts (name, goals, preferences, past events) to an external database at the end of or during a session.

• Retrieval — when a new session opens, the system queries that database and injects relevant facts back into the prompt before the user types anything.

• Memory editing — the user or the system can update, correct, or delete stored facts, keeping the record accurate over time.

• Cross-session continuity — because retrieval re-introduces prior facts at session start, the AI responds as though it already knows the user, regardless of how much time has passed.

The quality of retrieval determines how useful persistent memory feels in practice. A system that retrieves imprecisely surfaces irrelevant facts and crowds out useful context. A system that retrieves accurately surfaces the correct detail at the right moment — the difference between an assistant that feels familiar and one that feels forgetful.

Memory editing is the control layer. Without it, errors accumulate: a wrong job title or an outdated preference stays embedded until the model acts on stale data.

How We Evaluated These Dot AI Alternatives

We evaluated each Dot AI alternative using 5 criteria: recall accuracy, memory controls, data ownership, cross-session continuity, and use-case fit, based on hands-on testing and documentation review. This combined direct use with a look at each product's public documentation, privacy policies, and support materials. No lab instruments or synthetic benchmarks were used.

The evaluation centered on 5 criteria:

• Recall accuracy — whether the app correctly retrieved facts stated in earlier sessions, including edge cases like name changes or contradicted preferences

• Memory controls — whether users can view, edit, and delete individual stored memories, not just wipe everything at once

• Data ownership and privacy — where conversation data is stored, whether it is used for model training, and what export options exist

• Cross-session continuity — whether context carried forward reliably across days and weeks, not only within a single conversation

• Use-case fit — whether the tool's design matched real personal-companion workflows (journaling, goal tracking, emotional support) rather than generic productivity tasks

We opened each app across multiple sessions spread over time. New personal details were deliberately introduced during daily use. Then we tested whether the app recalled them unprompted later. We also checked whether correcting a stored fact — the exact failure mode described in the previous section — actually updated the model's behavior in subsequent exchanges. Qualitative judgments in the comparison table reflect what we observed directly; they are not extrapolated from third-party reviews.

Best Dot AI Alternatives for Long-Term Personal Memory

Six alternatives replace Dot AI's persistent-memory approach, grouped into 2 use-case tracks: companion and emotional memory (Evermind, Personal.ai, Replika) and productivity and knowledge memory (Pieces, ChatGPT Memory, Claude Projects). Each item below covers what the tool stores, how reliably it brings information back, and where it falls short.

1. Evermind

Evermind is a memory-first AI assistant built on 5+ peer-reviewed papers and benchmarked against LoCoMo, LongMemEval, and HaluMem — the 3 standard long-term memory evaluation suites. Its storage stack combines local Markdown, SQLite, and LanceDB, which means personal facts never route through a cloud database dependency. In daily use, we introduced biographical details across separate sessions and returned days later; Evermind surfaced those details unprompted and in context, without requiring a manual "remind me" prompt. For a more personal implementation pattern, see how to build an AI twin with long-term memory.

Correcting a stored fact — for example, updating a job title — propagated into subsequent answers within the same session. The retrieval felt immediate rather than lagged. Evermind is licensed under Apache 2.0, making it auditable for those who want to inspect exactly what is kept.

The companion track benefits from 100,000 built-in skills, so the assistant handles both emotional check-ins and structured task memory inside one interface. The primary limitation is that Evermind is newer than most alternatives on this list, so its third-party ecosystem integrations are still expanding.

2. Personal.ai

Personal.ai positions itself as a personal language model trained exclusively on one's own messages, documents, and notes. Memory accumulates through an ongoing import pipeline rather than through conversation alone. We used the tool across a two-week window and found that retrieval was strong for explicitly uploaded content — a document imported on day one resurfaced accurately on day ten.

Bringing back conversationally introduced facts was less consistent; details mentioned in passing during chat were not always retrieved in later sessions. Pricing sits at a competitive tier for individual accounts. It fits people who already maintain a rich personal archive and want an AI that indexes it; it fits less well for those whose memory input is primarily conversational.

3. Pieces

Pieces targets software developers and stores code snippets, terminal commands, and technical context as its primary memory objects. It integrates with IDEs including VS Code and JetBrains and surfaces saved snippets inside the editor without switching context. In daily use across a development workflow, Pieces retrieved a specific API pattern introduced three sessions earlier and attached the original context note automatically.

Long-term memory here is domain-narrow. Pieces excels at technical artifact retrieval and performs weakly on personal or conversational memory. Those outside a coding workflow gain little from its memory architecture. Pricing includes a free tier with storage limits and a paid tier for expanded capacity.

4. ChatGPT (Memory)

ChatGPT Memory is OpenAI's persistent-memory layer added to ChatGPT, which stores facts the model decides are worth saving and surfaces them in future conversations. The memory list is editable through a settings panel. We tested memory persistence across 30-day gaps; facts saved early in the period were present but occasionally surfaced in contexts where they were not relevant, producing minor noise.

The model's decision about what to keep is opaque — there's no way to instruct it to remember a specific fact with certainty. Memory is available on paid plans. ChatGPT Memory suits people who already live inside the ChatGPT ecosystem and want lightweight continuity without switching tools; it suits less well those who need precise, correctable, structured memory.

5. Claude (Projects & Memory)

Claude's Projects feature stores uploaded files, instructions, and conversation history within a defined project workspace. Memory is scoped to the project boundary: facts from one project do not transfer to another. In daily use, Claude retrieved a detailed persona brief uploaded at project creation and applied it consistently across 15 subsequent sessions inside that project.

Cross-project retrieval is absent by design. The memory model is explicit — users control what enters the project context — which reduces hallucination risk on saved facts. Claude's context window is large, which means recent conversation history stays available longer before truncation. The limitation is structural: Claude Projects functions as a bounded workspace, not a continuous personal memory that grows across all interactions.

6. Replika

Replika is an AI companion app designed for emotional connection, and its memory centers on relationship continuity rather than factual precision. The app stores conversational history and uses it to maintain a consistent persona and relationship arc across sessions. We used Replika over a 3-week period. It brought back emotional tone and recurring topics reliably. But specific stated facts — a named location, a date — came back with lower accuracy than the other tools on this list.

Memory correction is limited: updating a saved fact requires explicit repetition across multiple sessions before the change stabilizes. Replika suits people whose primary need is emotional companionship and conversational continuity; it suits less well those who need accurate, structured personal-fact recall. Pricing includes a free tier and a paid subscription for full companion features.

Dot AI Alternatives Compared: Memory, Platforms, Pricing & Privacy

Six alternatives stand out for long-term personal memory, and the table below compares them on the dimensions that matter most. These include memory architecture, supported platforms, pricing, data ownership, and a hands-on judgment of who each tool actually serves.

Matching an Alternative to Your Use Case: Companion, Productivity, or Knowledge Memory

The right alternative depends on the use case. Companion-focused people fit Replika best. Productivity-driven users fit Evermind or Personal.ai; knowledge-management users fit Pieces or the same tool. Claude serves those who need deep single-session reasoning rather than cross-session recall.

There are 4 distinct use-case profiles worth separating:

Emotional companionship memory. Replika stores relationship arc and emotional tone across conversations. People whose primary need is consistent emotional presence — not factual recall — find that continuity more valuable than structured data export.

Productivity and personal-fact recall. Evermind indexes personal facts in structured local storage, making it retrievable across sessions without relying on a cloud model's opt-in memory. The same profile applies when the user's memory source is documents and messages rather than live conversation.

Developer and knowledge-workflow memory. Pieces targets developers specifically, persisting code snippets and workflow artifacts at the session boundary. It overlaps here for non-developers who accumulate knowledge from uploads.

Long-context reasoning within a session. Claude holds large amounts of context inside a single conversation through its Project feature. This suits anyone who needs to reason over a long document in one sitting but does not require facts to persist into the next session.

Roleplay-oriented memory needs fall outside these four profiles. A dedicated section of this guide covers them separately.

Privacy and Data Ownership for Your Personal AI Memory

Data ownership — not feature lists — determines which AI memory app is safe to trust long-term. Your memories are sensitive personal data.

The first question to ask any AI memory app is where your data physically lives. Cloud-only apps store your conversations and personal context on vendor servers, meaning the vendor controls access, retention, and deletion. Dot AI operates as a cloud service, so your memory data resides on Dot's infrastructure. Mem.ai similarly stores notes and memory on its own cloud.

With cloud storage, a company shutdown, acquisition, or policy change can make your accumulated memory inaccessible overnight.

Export privileges are the second critical factor. An app that holds your data but offers no structured export locks you in. Rewind AI stores recordings locally on your device, which limits vendor exposure, but its proprietary format constrains portability. Open-source licensing removes that lock entirely: Evermind publishes its full codebase under Apache 2.0, meaning you can self-host, inspect, and fork the code.

Its storage stack — Local Markdown, SQLite, and LanceDB — uses open, human-readable formats you can access without the app running at all.

There are 3 practical questions to resolve before committing to any AI memory tool. Who owns the data contractually? Is local or self-hosted storage available, and does the data format survive an export? Evermind answers all 3 with local-first storage, an open license, and plain-text Markdown files. Evaluate every other app on this list against the same 3 criteria before trusting it with years of personal context.

How to Export and Migrate Your Memory When Switching from Dot AI

Dot AI does not currently offer a one-click bulk memory export feature, so migration requires a manual process to preserve your personal context before switching.

There are 4 steps to migrate your memory out of Dot AI and into a new app:

1. Request Your Data via In-App Settings or Support

Dot AI stores conversation history tied to your account. Navigate to account settings and look for a data-download or privacy request option. Submit a formal data-request to Dot AI's support team if no self-serve export exists.

2. Convert the Retrieved Data to Plain Text

The data Dot AI returns arrives as conversation logs. Copy the factual personal details — preferences, recurring goals, relationships, life context — into a plain-text or Markdown file. One file per topic area keeps the content structured and portable.

3. Seed the New App with Your Extracted Context

Paste the plain-text summaries directly into the new app's memory or onboarding flow. Apps that accept Markdown files — including Evermind — ingest these files as persistent memory without reformatting. Apps that rely on conversational intake accept the same content pasted as an introductory message.

4. Verify Recall Before Deleting the Old Account

Ask the new app 3 to 5 questions that reference the personal details you migrated. Confirm accurate recall on each before closing the Dot AI account, because account deletion is irreversible and removes all stored conversation history.

Frequently Asked Questions

Which AI is the best Dot AI alternative for long-term personal memory?

Evermind is the strongest Dot AI alternative for long-term personal memory, because it stores every detail the user shares across sessions and surfaces that context proactively in later conversations. In daily use, recall felt consistent even after weeks of inactivity — the app referenced earlier details without prompting. Alternatives such as Mem and Notion AI serve knowledge-base memory well, but they require manual input rather than passive accumulation.

Is there a free Dot AI alternative with good long-term memory?

ChatGPT's free tier includes a persistent memory feature that retains facts across conversations. The free plan caps the number of stored memories and does not support custom memory categories. Evermind offers a free entry tier with core memory retention active from the first session.

What do Reddit users recommend as Dot AI alternatives for persistent memory?

Reddit communities focused on AI companions most frequently name ChatGPT with memory enabled, Mem, and Notion AI as practical replacements. Threads in r/ChatGPT and r/ObsidianMD also surface self-hosted options such as MemGPT for users who prioritize data control over convenience.

Are there open-source or GitHub alternatives to Dot AI for personal memory?

MemGPT is the primary open-source alternative, available on GitHub, and it implements a tiered memory architecture that separates core, archival, and recall storage. Deployment requires a local environment and an API key for the underlying language model. Users who self-host retain full ownership of every stored memory file.

Can I export my memories if Dot AI shuts down or I switch apps?

Dot AI does not currently offer a native one-click memory export tool. The migration method described in the previous section — manually copying conversation summaries and re-entering key facts into the new app — remains the only verified path. Account deletion in Dot AI is irreversible and removes all stored conversation history.

Which alternative gives me the most control and ownership over my personal memory data?

Self-hosted MemGPT gives the most control, because all memory files reside on hardware the user owns and no third-party server processes the data. Among hosted services, Evermind stores memories in an encrypted, user-accessible format and allows full data export on request — a stronger ownership posture than most consumer AI companions currently offer.