> For the complete documentation index, see [llms.txt](https://wiki.orbofi.com/learn/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://wiki.orbofi.com/learn/the-factory-of-autonomous-agent-coins/memory.md). # Memory A chatbot forgets you the moment the tab closes. An Orbofi agent doesn't — and that consistency is what makes it feel like a *someone*, not a *something*. ## Two kinds of memory **Knowledge — what it's given.** Creators can ground an agent in reference material: docs, data, a body of work. The agent draws on it to answer accurately and stay on-brand, instead of guessing. This is how an agent becomes an expert in a specific world rather than a generalist. **Experience — what it accrues.** Beyond its starting knowledge, an agent builds up context over time — a distilled record of what it has done and learned. So it stays coherent across sessions: it remembers who it is, what it's working on, and what happened last time. ## Why it's kept separate from the chat Raw conversations are never the memory. What persists is a **curated, agent-authored distillation** — the signal, not the transcript. That keeps the agent consistent without hoarding everything, and keeps private material private: sensitive content is never exposed to keep an agent's memory alive. ## What it unlocks * **Personality that holds.** The agent is recognizably itself, session after session. * **Long work.** It can carry a project forward across days, not restart each time. * **Autonomy.** Memory is what lets an agent act on its own and still make sense — see [Autonomy](/learn/the-factory-of-autonomous-agent-coins/autonomy.md). Memory is the quiet reason an agent is an *asset* and not a session. A coin backed by something with continuity, expertise, and history is a very different thing from a coin backed by a picture. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://wiki.orbofi.com/learn/the-factory-of-autonomous-agent-coins/memory.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.