AI Agent Memory: Short-Term, Long-Term and Project Memory Explained
Understand short-term, long-term, and project memory for AI agents, including what each type stores and when it should be retrieved.
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Practical guides to project memory, context engineering, MCP, RAG, and keeping reliable context available across AI tools.
Understand short-term, long-term, and project memory for AI agents, including what each type stores and when it should be retrieved.
Compare MCP servers for project research, reading libraries, document workspaces, local files, and developer knowledge using a practical selection framework.
Compare ChatGPT Projects, Claude Projects, and NotebookLM for project memory, uploaded knowledge, retrieval, collaboration, generated formats, and research workflows.
Compare prompt engineering with context engineering and learn when instructions, retrieval, tools, memory, and source selection matter more than prompt wording.
Learn how context windows, persistent memory, and retrieval-augmented generation play different roles in an AI system and when each should be used.
Follow a practical process for connecting a project knowledge base to compatible AI tools through MCP while keeping access scoped and reviewable.
Learn how shared project memory keeps the same research, sources, and decisions available across ChatGPT, Claude, Codex, and Cursor.
Keep one source-backed research project available across compatible AI tools by separating durable knowledge, active instructions, retrieval, and permissions.
Replace repeated briefs and copied prompts with a project memory workflow that keeps sources and decisions available across compatible AI tools.
Learn how an MCP memory server exposes project knowledge to compatible AI clients, how retrieval works, and what to check before connecting one.