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

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Note
Memvid v1 has been removed to avoid confusion. This repository represents Memvid v2, a revised and improved version of the project. Thanks to everyone who provided feedback on V1. The comments and issues helped shape the improvements included in V2.

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.

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.

• 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.

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.

• Long-Running AI Agents
• Enterprise Knowledge Bases
• Offline-First AI Systems
• Codebase Understanding
• Customer Support Agents
• Workflow Automation
• Sales and Marketing Copilots
• Personal Knowledge Assistants
• Medical, Legal, and Financial Agents
• Auditable and Debuggable AI Workflows
• Custom Applications

• Core architecture finalized
• APIs stabilized
• Docs and SDKs coming soon

Official v2 public release: January 5, 2026

Have questions or feedback? Email: [email protected]

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Apache License 2.0 — see the LICENSE file for details.