内容摘录
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<strong>Memvid is a single-file memory layer for AI agents with instant retrieval and long-term memory.</strong><br/>
Persistent, versioned, and portable memory, without databases.
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<a href="https://www.memvid.com">Website</a>
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<a href="https://sandbox.memvid.com">Try Sandbox</a>
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<a href="https://docs.memvid.com">Docs</a>
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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.
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
SDKs & CLI
Use Memvid in your preferred language:
| Package | Install | Links |
| --------------- | --------------------------- | ------------------------------------------------------------------------------------------------------------------- |
| **CLI** | npm install -g memvid-cli | npm |
| **Node.js SDK** | npm install @memvid/sdk | npm |
| **Python SDK** | pip install memvid-sdk | PyPI |
| **Rust** | cargo add memvid-core | Crates.io |
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Installation (Rust)
Requirements
**Rust 1.85.0+** — Install from rustup.rs
Add to Your Project
Feature Flags
| Feature | Description |
| ------------------- | ---------------------------------------------------------------- |
| lex | Full-text search with BM25 ranking (Tantivy) |
| pdf_extract | Pure Rust PDF text extraction |
| vec | Vector similarity search (HNSW + local text embeddings via ONNX) |
| clip | CLIP visual embeddings for image search |
| whisper | Audio transcription with Whisper |
| api_embed | Cloud API embeddings (OpenAI) |
| temporal_track | Natural language date parsing ("last Tuesday") |
| parallel_segments | Multi-threaded ingestion |
| encryption | Password-based encryption capsules (.mv…