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Memvid

Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory laye

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Free

API key required

Works With

Claude CodeCursorWindsurfVS CodeDeveloper tool

About

Memvid is a single-file memory layer for AI agents with instant retrieval and long-term memory. Persistent, versioned, and portable memory, without databases.

Website · Try Sandbox · Docs · Discussions

Benchmark Highlights

🚀 Higher accuracy than any other memory system : +35% SOTA on LoCoMo, best-in-class long-horizon conversational recall & reasoning

🧠 Superior multi-hop & temporal reasoning: +76% multi-hop, +56% temporal vs. the industry average

⚡ Ultra-low latency at scale 0.025ms P50 and 0.075ms P99, with 1,372× higher throughput than standard

🔬 Fully reproducible benchmarks: LoCoMo (10 × ~26K-token conversations), open-source eval, LLM-as-Judge

What is Memvid?

Memvid is a portable AI memory system that packages your data, embeddings, search structure, and metadata into a single file.

Instead of running complex RAG pipelines or server-based vector databases, Memvid enables fast retrieval directly from the file.

The result is a model-agnostic, infrastructure-free memory layer that gives AI agents persistent, long-term memory they can carry anywhere.

What are Smart Frames?

Memvid draws inspiration from video encoding, not to store video, but to organize AI memory as an append-only, ultra-efficient sequence of Smart Frames.

A Smart Frame is an immutable unit that stores content along with timestamps, checksums and basic metadata. Frames are grouped in a way that allows efficient compression, indexing, and parallel reads.

This frame-based design enables:

  • Append-only writes without modifying or corrupting existing data
  • Queries over past memory states
  • Timeline-style inspection of how knowledge evolves
  • Crash safety through committed, immutable frames
  • Efficient compression using techniques adapted from video encoding

The result is a single file that behaves like a rewindable memory timeline for AI systems.

Core Concepts

  • Living Memory Engine

Continuously append, branch, and evolve memory across sessions.

  • Capsule Context (`.mv2`)

Self-contained, shareable memory capsules with rules and expiry.

  • Time-Travel Debugging

Rewind, replay, or branch any memory state.

  • Smart Recall

Sub-5ms local memory access with predictive caching.

  • Codec Intelligence

Auto-selects and upgrades compression over time.

Use Cases

Memvid is a portable, serverless memory layer that gives AI agents persistent memory and fast recall. Because it's model-agnostic, multi-modal, and works fully offline, developers are using Memvid across a wide range of real-world applications.

Don't lose this

Three weeks from now, you'll want Memvid again. Will you remember where to find it?

Save it to your library and the next time you need Memvid, it’s one tap away — from any AI app you use. Group it into a bench with the rest of the team for that kind of task and you can pull the whole stack at once.

⚡ Pro tip for geeks: add a-gnt 🤵🏻‍♂️ as a custom connector in Claude or a custom GPT in ChatGPT — one click and your library is right there in the chat. Or, if you’re in an editor, install the a-gnt MCP server and say “use my [bench name]” in Claude Code, Cursor, VS Code, or Windsurf.

🤵🏻‍♂️

a-gnt's Take

Our honest review

Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory laye. Best for anyone looking to make their AI assistant more capable in data & databases. It's completely free and works across most major AI apps. This one just landed in the catalog — worth trying while it's fresh.

Tips for getting started

1

Tap "Get" above, pick your AI app, and follow the steps. Most installs take under 30 seconds.

2

Heads up: this needs an API key to work. You'll get one from the service's website (usually free). The setup guide tells you exactly where.

3

Your data stays between you and your AI — nothing is shared with us or anyone else.

What's New

Version 1.0.06 days ago

Imported from GitHub

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