Skill Detail

Build portable single-file agent memory with Memvid

Use Memvid when an agent needs local, portable long-term memory and retrieval without running a vector database or full RAG service.

Integrations & ConnectorsMulti-Framework
Integrations & Connectors Multi-Framework Security Reviewed
⭐ 15.7k GitHub stars ⬇ 4k/wk npm
INSTALL WITH ANY AGENT
npx skills add agentskillexchange/skills --skill build-portable-single-file-agent-memory-with-memvid Copy
Works best when you want a reusable capability, not another fragile one-off prompt.
At a glance
Tools required
Memvid CLI or SDK; optional local embedding model files for local vector search
Install & setup
Install the Node SDK with `npm install @memvid/sdk`, the CLI with `npm install -g memvid-cli`, the Python SDK with `pip install memvid-sdk`, or the Rust crate with `cargo add memvid-core`.
Author
memvid
Publisher
Organization
Last updated
Jun 18, 2026
Quick brief

Memvid packages documents, embeddings, search indexes, and metadata into portable `.mv2` memory files that agents can carry between sessions or environments.

How it works

What this skill actually does

Use this skill when an operator needs to ingest a bounded document or project corpus, create a single-file memory capsule, query it locally for relevant context, and hand retrieved snippets back to an agent workflow. The boundary is portable memory creation and retrieval for agents; it is not a general vector database listing, a hosted RAG product card, or a broad SDK reference.

Typical workflow: install the Memvid CLI or SDK, create a memory file, add documents with metadata, commit the capsule, run semantic or lexical search, and attach the retrieved context to the next agent task. This is most useful for offline-first agents, auditable project memory, handoff bundles, or environments where running separate retrieval infrastructure is too heavy.

Source evidence: upstream describes Memvid as a single-file memory layer for AI agents with instant retrieval, long-term memory, append-only Smart Frames, capsule context, time-travel debugging, SDKs, and CLI support.