An honest roundup of the ways people give AI long-term memory in 2026, from an Obsidian wiki to ChatGPT Projects to a vector database, and which one fits your work.
Short answer: there is no single best agentic memory setup. Developers building an agent usually start with Mem0, Zep or Letta. One person with one topic does fine with an Obsidian wiki, NotebookLM, or a ChatGPT or Claude Project. If you carry several clients, you need memory that keeps each client separate and fills itself from your calls: that is Armbrain.
Updated October 15, 2026
Below: a table of nine setups, who each one fits, and answers to the questions people ask most. Competitor facts come from each tool's own docs, checked October 9, 2026. Each setup links to a fair side-by-side comparison and, where it makes sense, a step-by-step guide to doing it yourself.
New to the term? Start with What is agentic memory? and Agentic memory vs RAG.
Which setup fits you
| Setup | Best for | Where it falls short | Compare | Do it yourself |
|---|---|---|---|---|
| Karpathy's LLM Wiki (Obsidian + a coding agent) | Deep research on one topic, if you like running an agent | One vault per client, and you file every source by hand | Compare | How-to |
| Obsidian + AI plugins (Smart Connections) | People who already write good notes and want them local | You still write and file every note | Compare | How-to |
| NotebookLM (now Gemini Notebook) | Cited answers from a fixed set of documents | Knows only the sources you add; no promise tracking | Compare | How-to |
| ChatGPT Projects | A few clients, mostly working in ChatGPT | You can't see a list of what project memory holds | Compare | How-to |
| Claude Projects | A few clients, mostly working in Claude | Project knowledge changes only when you change it | Compare | Armbrain in Claude Projects |
| Custom GPTs | Stable reference files shared with a team | OpenAI plans to retire them | Compare | How-to |
| Microsoft Copilot Notebooks | Clients who live in Microsoft 365 | Answers only from references you added | Compare | How-to |
| DIY vector database (Pinecone + LangChain) | Teams building an AI product with engineers | You build and maintain the pipeline | Compare | How-to |
| Armbrain | Fractional leaders, agencies, execs and sales: many clients or accounts | Not a research notebook or a notes app | All comparisons | Start free |
Armbrain is $90 per person/month, or five seats for $400/month. Unlimited minds and businesses. 30 days free, no card to start.
Agent memory frameworks compared: Mem0, Zep, Letta and more
The setups above are for people. If you are building an AI agent and need a memory layer for it, these are the main options. Facts come from each project's own GitHub page or docs, checked October 15, 2026.
| Setup | What it is | Open source | Where it runs | Best for |
|---|---|---|---|---|
| Mem0 | A memory layer for AI agents that you call from your code | Yes, Apache 2.0 | Python or JS library, your own server, or the managed Mem0 Platform | Adding user memory to an app you already have |
| Zep | A managed context service built on Graphiti, a temporal knowledge graph where facts can be marked out of date | Graphiti is Apache 2.0 | Zep fully managed or in your cloud; Graphiti self-hosted | Facts that change over time and need point-in-time answers |
| Letta | A platform for stateful agents whose memory is part of the agent | Yes, Apache 2.0 | Self-hosted server or Letta Cloud | Long-running agents built on Letta's runtime |
| DIY vector database (Pinecone + LangChain) | Building blocks: load, split, embed and search your documents | LangChain is open source; Pinecone is a hosted service | Your code plus Pinecone's service | Teams with engineers who want full control |
| ChatGPT memory | Memory of your own preferences, plus Projects for files and chats per topic | No | Inside ChatGPT | One person working mainly in ChatGPT |
| Armbrain | A separate mind per client, filled from your meetings and email, used inside the AI assistant you already use | No | Hosted by Armbrain | Fractional leaders, agencies, execs and sales teams with many clients |
Mem0, Zep and Letta are tools for engineers: you write the code that decides what to save and when. Looking to replace Mem0 specifically? See Mem0 alternatives compared. Armbrain is a finished product for people who do client work. See how to build your own with Pinecone and LangChain and Armbrain vs ChatGPT memory.
How to choose in two questions
1. How many separate clients or accounts does your AI need to keep apart? If the answer is one, most built-in tools are enough. Use a single Project, notebook or vault. If it is three or more, separation becomes the main job, and setups that rely on you filing things correctly start to leak.
2. Where does the knowledge come from? If it comes from documents you already have, a notebook or Project over those files works well. If it comes from calls, email and decisions that change every week, you need memory that fills itself and keeps the newest decision on top.
Questions people ask
What is the best AI memory tool?
It depends on the job. For research over documents, NotebookLM (now Gemini Notebook) is hard to beat. For a knowledge base you own, an Obsidian setup is a strong free option. Developers building agents usually pick Mem0, Zep or Letta. For client work across many accounts, Armbrain keeps a separate mind per client.
Mem0 vs Zep vs Letta: which should I use?
They make different bets. Mem0 is a memory layer you add to an existing app. Zep is a managed service built on Graphiti, a knowledge graph where facts can be marked out of date. Letta is a platform where memory is part of the agent itself. All three are for engineers, and each has open-source code under Apache 2.0.
How do I give ChatGPT long-term memory?
ChatGPT has built-in memory for your own preferences, and Projects for keeping related chats, files and instructions together. For client work, make one Project per client and choose project-only memory so chats stay inside that project. Our ChatGPT Projects guide has the steps.
How do I give Claude long-term memory?
Claude's help center says memory is on by default for Free, Pro and Max plans, and each project has its own memory, kept separate from other projects. Files you add to project knowledge are used across every chat in that project. For a team setup with Armbrain inside Claude Projects, see Claude Projects for Teams.
What is the best AI memory for agencies, fractional CMOs and sales?
The test is whether one client's facts can turn up in another client's draft. Armbrain gives each client its own mind, files meeting transcripts under the right client, and keeps commitments on a list until they are done. For sales, that means the brief for your next call shows what the buyer was promised.
Can I build my own AI memory?
Yes. Pinecone and LangChain give you the building blocks: load, split, embed and store documents, with one namespace per client. Our step-by-step guide covers it. Budget for the upkeep: re-indexing, keeping clients separate in every query, and the code for decisions and corrections.
All comparisons and guides
- Comparisons: Karpathy's LLM Wiki, Obsidian with AI plugins, NotebookLM, ChatGPT Projects, ChatGPT memory, Claude Projects, custom GPTs, Copilot Notebooks, Notion AI, DIY vector database, RAG, Viktor, and the compare hub.
- How-to guides: LLM wiki with Obsidian, Obsidian AI plugins, NotebookLM, ChatGPT Projects, custom GPTs, Copilot Notebooks, Pinecone and LangChain, Claude Projects for Teams.
- Background: What is agentic memory? and Agentic memory vs RAG.
Sources
Checked October 9, 2026; frameworks checked October 15, 2026: Mem0 on GitHub, Graphiti on GitHub, Zep pricing and deployment, Letta on GitHub, Letta self-hosting docs. Each compare page lists its sources. Also: Claude Help Center, How can I create and manage projects? and Use Claude's chat search and memory.