You open a new session and your first job is to tell it what you told the last one. Anthropic's own cookbook for its memory tool names the problem in plain words: similar tasks across conversations "require re-explaining context every time."1

The big three assistants now each offer a fix. The question for anyone who works in more than one of them is whether the memory one builds about you travels to the others. We read OpenAI's, Anthropic's and Google's own pages, plus the published benchmark papers on long-term memory, over the week spanning 24 September to 1 October 2026, to find out. As far as we could find, no vendor describes a memory that follows you into another vendor's tools. What does exist is a connector that could carry it.

The cost is real. Nobody we found has priced it.

Developers describe the friction first-hand. A Hacker News commenter in April 2026 wrote: "I try to explain up-front to the agent how aggressively they can modify the existing code and which parts, but I've had mixed success."2 That's someone briefing an agent, not a count of sessions. An undated roundup of Hacker News discussion of coding agents has a line about tools that "reduce the need to restate house rules every session."3

We found no one who measured it. The cost is well attested and unquantified.

What the benchmarks measured

The LongMemEval paper, from researchers rather than a vendor and dated March 2025 on arXiv, reported "a 30% accuracy drop on memorizing information across sustained interactions" for commercial chat assistants and long-context models.4 That's the number we'd keep. Forgetting across a long stretch of use is measurable, and commercial assistants showed it.

An earlier benchmark, LoCoMo (February 2024), found that long-context models and retrieval still "substantially lag behind human performance."5 Systems built to fix this differ a great deal from one another. A May 2026 paper on coding agents, on its own test and not on either benchmark, reported 72.5% average accuracy for its approach. The strongest retrieval baseline scored 48.5%, and an off-the-shelf coding agent scored 69.3%.6 None of this crowns a winner.

You'll also see bigger numbers. Mem0's paper reports saving more than 90% of token cost against re-sending full context, measured by Mem0 on LoCoMo.7 Anthropic reports 84% fewer tokens from context editing in a 100-turn web search test. That result is about keeping one long session alive, not about memory that survives into the next session or the next tool.8 Neither tells you what portable memory would save you, and we found no figure that does.

What each vendor says about its own memory

OpenAI describes ChatGPT's Memory as remembering "relevant preferences and details from your chats and other available sources."9 Its documentation also says: "ChatGPT web uses ChatGPT memory, while local Codex clients use a separate local memory store and controls."10 So OpenAI's own two products don't share. The same documentation says OpenAI "keeps chats that used external context such as MCP tool calls, web search, or tool search out of memory generation."11 Codex's command reference lists /import as "Import Claude Code or Cursor setup, projects, and chats,"12 a one-time copy into Codex rather than a memory that stays in sync.

Anthropic goes the other way on portability. Its memory announcement tells you how to bring memory over from another AI tool, or export it from Claude "for backup or migration."13 As of August 2026, what Claude remembers from your chats is also there when Cowork runs a task in the cloud, "and vice versa."14 So Claude's memory moves by hand and is shared inside Anthropic's own surfaces. We found nothing from Anthropic saying it flows live into another vendor's tool.

Google says Gemini's memory setting is on by default: it "remembers key details and preferences you've shared," and you can switch it off at any time.15 Google also added an import option in March 2026 for bringing memory in from other AI apps.16 Its help page states where that memory actually works: "Only available in the Gemini mobile app, the Gemini web app at gemini.google.com, Gemini in Chrome (in countries where Gemini in Chrome is available), and Gemini on your smartwatch."17 Gemini CLI isn't on that list.

Here's our reading, and it's ours. The memory that exists moves between vendors by transfer, and only at the edges: you export from one product, you import into another. A memory that tools from different vendors read and write as you work isn't something we found any of the three describing.

The bridge already exists

MCP, the Model Context Protocol, is a standard for connecting these tools to outside data and services. Anthropic said in December 2025 that it had been adopted by ChatGPT, Cursor, Gemini, Microsoft Copilot and Visual Studio Code.18 That's Anthropic's account of a protocol it started, so we looked for the others in their own words.

OpenAI's documentation describes MCP access through a plugin in ChatGPT and Codex, through deep research, and through the API.19 It says a remote MCP server can be any public server that implements the protocol.20 Google's Gemini CLI documentation is a guide to configuring MCP servers, set up in a settings file in your home directory.2122 The CLI is the only Gemini product where we found it. We found nothing on MCP in the Gemini API, and nothing saying whether the CLI's support is official or experimental.

The sources show that these tools speak MCP. They don't show developers choosing it as the answer to memory portability, and we found no one saying that.

There's a wrinkle in OpenAI's line about MCP tool calls. As we read it, a memory connected over MCP sits beside ChatGPT's own memory and doesn't feed it. That's our inference from one sentence in the documentation, not something OpenAI says. It means the bridge is a second memory you own. It isn't a way to edit the first.

What we couldn't settle

What we went looking for and didn't find: buyers who require data residency for AI memory, an independent measurement of open-weight local models doing memory work as well as cloud models, and small teams pricing memory by cost per session. If you're deciding on those grounds, the evidence we found doesn't help you yet.

What we make

You've followed this far, so you'd ask. We make Anamnesis, a memory for AI tools. It's live in alpha, which means it runs, people use it, and it's still changing. It's free during alpha, and local self-hosting is free.

One account, one memory. A single MCP connector reaches it across seven clients: Claude Code, Claude Desktop, claude.ai, Cowork, ChatGPT, Codex CLI and Gemini CLI. Fully automatic capture runs in Claude Code today. In the others, the memory is read into every chat, and new memories are saved when you ask or when the assistant judges something worth keeping. Retrieval took 13 to 42 milliseconds in our scale benchmark, flat through 1,000 records.23 Each account's memory is encrypted with a key derived from its own credentials, with no master key. It isn't end-to-end encrypted today. Export is plain JSON, anytime. You can read more at anamnesis.smtry.ai.

Each tool is building its own memory of you. Whether the next tool gets it is a decision you can make yourself.

Sources

  1. Anthropic, https://platform.claude.com/cookbook/tool-use-memory-cookbook, published 2025-08-18, retrieved 2026-09-24. Quote: "Repeated patterns: Similar tasks across conversations require re-explaining context every time" ↩
  2. Hacker News (Y Combinator), https://news.ycombinator.com/item?id=47867253, published 2026-04, retrieved 2026-09-24. Quote: "I try to explain up-front to the agent how aggressively they can modify the existing code and which parts, but I've had mixed success" ↩
  3. Developers Digest, https://www.developersdigest.tech/blog/what-hacker-news-gets-right-about-ai-coding-agents-2026, published unknown, retrieved 2026-09-24. Quote: "They reduce the need to restate house rules every session." ↩
  4. arXiv (Cornell University), https://arxiv.org/abs/2410.10813, published 2025-03-04, retrieved 2026-09-24. Quote: "commercial chat assistants and long-context LLMs showing a 30% accuracy drop on memorizing information across sustained interactions" ↩
  5. arXiv, https://arxiv.org/abs/2402.17753, published 2024-02-27, retrieved 2026-09-24. Quote: "Employing strategies like long-context LLMs or RAG can offer improvements but these models still substantially lag behind human performance." ↩
  6. arXiv, https://arxiv.org/html/2605.12493v1, published 2026-05-12, retrieved 2026-09-24. Quote: "Experiments show that AgentRunbook-C achieves the best performance with 72.5% average accuracy, outperforming the strongest RAG baseline (48.5%) and the off-the-shelf coding agent baseline (69.3%)." ↩
  7. Cornell University (arXiv.org), https://arxiv.org/abs/2504.19413, published 2025-04-28, retrieved 2026-09-24. Quote: "Mem0 attains a 91% lower p95 latency and saves more than 90% token cost, offering a compelling balance between advanced reasoning capabilities and practical deployment constraints." ↩
  8. Anthropic, https://claude.com/blog/context-management, published 2025-09-29, retrieved 2026-09-24. Quote: "In a 100-turn web search evaluation, context editing enabled agents to complete workflows that would otherwise fail due to context exhaustion—while reducing token consumption by 84%." ↩
  9. OpenAI, https://help.openai.com/en/articles/8590148-memory-faq, published unknown, retrieved 2026-09-24. Quote: "When Memory is enabled, ChatGPT can remember relevant preferences and details from your chats and other available sources." ↩
  10. OpenAI, https://learn.chatgpt.com/docs/customization/memories, published unknown, retrieved 2026-09-24. Quote: "ChatGPT web uses ChatGPT memory, while local Codex clients use a separate local memory store and controls." ↩
  11. OpenAI, https://learn.chatgpt.com/docs/customization/memories.md, published unknown, retrieved 2026-09-24. Quote: "keeps chats that used external context such as MCP tool calls, web search, or tool search out of memory generation" ↩
  12. OpenAI, https://learn.chatgpt.com/docs/developer-commands.md?surface=cli, published unknown, retrieved 2026-10-01. Quote: "Import Claude Code or Cursor setup, projects, and chats." ↩
  13. Anthropic, https://claude.com/blog/memory, published 2025-09-11, retrieved 2026-09-24. Quote: "If you would like to bring your memory details over from a different AI tool or export your memory from Claude for backup or migration, you can follow these instructions." ↩
  14. Anthropic, https://claude.com/blog/claudes-memory-works-everywhere-and-you-decide-whats-in-it, published 2026-08-25, retrieved 2026-09-24. Quote: "When Cowork runs a task in the cloud, what Claude remembers from your chats is there, and vice versa." ↩
  15. Google, https://blog.google/products-and-platforms/products/gemini/temporary-chats-privacy-controls/, published 2025-08-13, retrieved 2026-09-24. Quote: "When this setting is on, Gemini remembers key details and preferences you've shared, leading to more natural and relevant conversations, as if you're collaborating with a partner who's already up to speed." ↩
  16. Google, https://blog.google/innovation-and-ai/products/gemini-app/switch-to-gemini-app/, published 2026-03-26, retrieved 2026-09-24. Quote: "our new memory import feature can easily bring an understanding of your key preferences, relationships, and personal context directly into Gemini" ↩
  17. Google, https://support.google.com/gemini/answer/16598469, published unknown, retrieved 2026-10-01. Quote: "Only available in the Gemini mobile app, the Gemini web app at gemini.google.com, Gemini in Chrome (in countries where Gemini in Chrome is available), and Gemini on your smartwatch." ↩
  18. Anthropic, https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation, published 2025-12-09, retrieved 2026-09-24. Quote: "MCP has been adopted by ChatGPT, Cursor, Gemini, Microsoft Copilot, Visual Studio Code, and other popular AI products" ↩
  19. OpenAI, https://developers.openai.com/api/docs/mcp, published unknown, retrieved 2026-09-24. Quote: "makes it available through a plugin in ChatGPT and Codex, through ChatGPT deep research and company knowledge, and through the API" ↩
  20. OpenAI, https://developers.openai.com/api/docs/guides/tools-connectors-mcp, published unknown, retrieved 2026-09-24. Quote: "Remote MCP servers can be any server on the public Internet that implements a remote Model Context Protocol (MCP) server." ↩
  21. Google (Gemini CLI team), https://raw.githubusercontent.com/google-gemini/gemini-cli/main/docs/tools/mcp-server.md, published unknown, retrieved 2026-09-24. Quote: "This document provides a guide to configuring and using Model Context Protocol (MCP) servers with Gemini CLI." ↩
  22. Google (google-gemini organization), https://github.com/google-gemini/gemini-cli, published unknown, retrieved 2026-09-24. Quote: "Configure MCP servers in ~/.gemini/settings.json to extend Gemini CLI with custom tools:" ↩
  23. SMTRY, Anamnesis product page, https://smtry.ai/anamnesis, retrieved 2026-10-02. Quote: "13 to 42 milliseconds across the scale benchmark, flat through 1,000 records." ↩