How to Give AI Agent a Memory That Survives the Session
The article explains how to give AI agents a persistent memory that survives individual chat sessions by using a shared SQLite database. It describes five Vektor tools—store, recall, graph, delta, and status—that allow agents to save, retrieve, and manage information across sessions. The guide also outlines installation and configuration steps for various MCP clients and direct SDK integration.
- ▪AI chat sessions typically lose context once the session ends, requiring a persistent storage solution.
- ▪Vektor provides five memory tools—store, recall, graph, delta, and status—that operate on a shared SQLite database.
- ▪The setup works across multiple MCP clients such as Claude Desktop, Cursor, and Windsurf by installing and activating the Vektor package.
- ▪When using the OpenAI Agents SDK, developers can call the memory functions directly without an MCP client.
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| Original publisher | Medium |
| Canonical URL | https://medium.com/@vektormemory/how-to-give-ai-agent-a-memory-that-survives-the-session-116f69c23eaf |
| Publication time | Thu, 06 Aug 2026 00:43:35 +0000 |
| Retrieval time | 2026-08-06T00:55:45.634Z |
| Last seen | 2026-08-06T00:55:45.634Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | S84kjS7_OobK · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
AIAI AgentVector DatabaseSshHow to Give AI Agent a Memory That Survives the SessionVektor Memory10 min read·Just now--ListenSharePress enter or click to view image in full sizeCustom generative code imageThis is a walkthrough of how you can set this up using our tools or borrow our ideas and use them in your own setup. We are using five custom SSH tools and our graph memory tools that cover the whole job between them, plus we also advise on what to do when your agent writes something to a real file and gets it wrong and how you can fix it.This article is written in natural language, so it is easy for everyone to understand; deeper info is on the website if needed.When you open a new chat with an AI coding assistant, the first ten minutes usually go the same way. You explain your stack.
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