--- title: "AI Memory Glossary: Key Terms Explained | Hebby Memories" description: "AI memory glossary: context window, token, CLAUDE.md, MCP, RAG, embedding, reranker, graph memory and Hebbian learning explained briefly and clearly." canonical: "https://www.hebby-memories.com/en/guides/glossary/" language: en last_updated: 2026-11-03 image: "https://www.hebby-memories.com/assets/og-bild.jpg" markdown_versions: ["https://www.hebby-memories.com/wissen/glossar.md", "https://www.hebby-memories.com/en/guides/glossary.md"] publisher: "Hebby Memories, https://www.hebby-memories.com (llms.txt: https://www.hebby-memories.com/llms.txt)" --- Guide ยท Glossary # Glossary: AI memory in terms. The technical terms around AI memory, agents and context, each explained in a few sentences. ## AI agent An **AI agent** is a language model that does not just answer but carries out steps on its own: reading files, running commands, changing code, calling tools. Examples are Claude Code, Codex or Hermes. For a single request an agent often performs dozens of steps, and each one costs tokens. ## AI memory An **AI memory** stores knowledge across sessions: decisions, project facts, settings, past mistakes. A language model on its own forgets everything after each session that is not in its training data. More in the guide [AI memory](https://www.hebby-memories.com/en/guides/ai-memory/). ## Second brain A **second brain** is an external memory for notes, ideas and knowledge. With AI it becomes a system that does not just file content but links it by itself and brings it back at the right moment. More in [Second brain AI](https://www.hebby-memories.com/en/guides/second-brain-ai/). ## Context window The **context window** is the amount of text a language model sees at once for a request: system prompt, context files, conversation history, tool results. Whatever is not in it, the model does not know at that moment. The fuller it is, the more expensive and often less precise the answer. More in the guide [Context window](https://www.hebby-memories.com/en/guides/context-window/). ## Token A **token** is the unit language models compute in, usually part of a word. Billing is based on the tokens that go into and come out of the model. Everything in the context window counts on every call, including content that contributes nothing to the current question. What Hebby costs and saves in tokens: [Token efficiency](https://www.hebby-memories.com/en/token-efficiency/). ## Context file (CLAUDE.md, AGENTS.md) A **context file** is a text file an agent reads automatically in every session, such as CLAUDE.md for Claude Code, AGENTS.md for Codex, GEMINI.md or MEMORY.md. It is the simplest form of memory, but it is sent in full with every call and grows over time. Why lean context files are better: [Lean context](https://www.hebby-memories.com/en/guides/lean-context/). ## Prompt caching With **prompt caching** the model provider remembers the unchanged beginning of a request and charges less for it on the next call. That lowers the cost of repeated content such as context files, but does not make it free. ## MCP (Model Context Protocol) The **Model Context Protocol** is an open standard that lets AI applications connect external tools and data sources. A service that speaks MCP works in many clients, such as Claude Code, Codex or other agents, without a separate adaptation for each client. ## MCP memory server An **MCP memory server** gives an agent a memory over MCP: the agent can store and query knowledge. Hebby is connected this way and additionally brings matching memories in advance. More in [MCP memory server](https://www.hebby-memories.com/en/guides/mcp-memory-server/). ## Hook A **hook** is a small script an agent runs automatically at fixed points, for example before a request goes to the model. Through hooks a memory can put memories into the context before the agent asks for them. ## RAG (retrieval-augmented generation) **RAG** means that before answering, a system searches a collection for matching passages and passes them to the language model. This lets the model answer with knowledge that is not in its training data. The quality depends on how well the search finds the right passages. ## Embedding and vector search An **embedding** turns a text into a series of numbers (a vector) that represents its meaning. Texts with similar meaning end up close to each other as vectors. **Vector search** uses this to find content with matching meaning quickly in large collections, even without the same words. ## Reranker A **reranker** sorts the results of a first, fast search a second time, thoroughly, by scoring question and result together. That improves the results but costs time and compute on every request. More in the guide [Reranker](https://www.hebby-memories.com/en/guides/reranker/). ## Graph memory A **graph memory** stores knowledge as a network of nodes and connections. That way the memory knows what belongs together, for example which decision belongs to which project. Hebby uses a graph memory of its own design. More in [Graph memory](https://www.hebby-memories.com/en/guides/graph-memory/). ## Hebbian learning **Hebbian learning** goes back to the psychologist Donald Hebb: what is active together connects more strongly. In Hebby, connections between memories grow stronger when they are used together. More in [Hebbian learning](https://www.hebby-memories.com/en/hebbian-learning/). ## Consolidation In memory research, **consolidation** means that the brain sorts and strengthens new content afterwards and links it with what is already there, above all during sleep. An AI memory can mirror this: it merges duplicates, reorganizes and retires what is outdated. ## Anticipatory recall **Anticipatory recall** means the memory brings matching memories into the context before the agent searches for them. Research steps and follow-up questions drop away, and the agent knows known pitfalls before the first attempt. ## AI knowledge management **AI knowledge management** means collecting and organizing knowledge in teams or companies so that AI systems can use it, with permissions per person and per agent. More in the guide [AI knowledge management](https://www.hebby-memories.com/en/guides/ai-knowledge-management/). **Hebby Memories:** the second brain for Claude Code, Codex & co. Learns as you work, thinks ahead, never forgets what matters. [Get Early Access โ†’](https://www.hebby-memories.com/en/early-access/) ## Read more [AI memoryAI Memory: How to Give AI a Long-Term MemoryWhy ChatGPT, Claude & co. forget, what AI memory does, and how to spot a good one.Read more โ†’](https://www.hebby-memories.com/en/guides/ai-memory/) [Context windowContext window explained: why ChatGPT & Claude forgetWhat an AI's context window is, why ChatGPT and Claude forget in long chats, and what context engineering has to do with it.Read more โ†’](https://www.hebby-memories.com/en/guides/context-window/)