# Memvid — Serverless Memory Layer for AI Agents

> An open-source memory system that replaces complex RAG pipelines with a single-file, serverless memory layer providing instant retrieval and long-term storage for AI agents.

## Install

Save as a script file and run:

# Memvid — Serverless Memory Layer for AI Agents

## Quick Use
```bash
pip install memvid
```
```python
from memvid import MemvidEncoder, MemvidRetriever

encoder = MemvidEncoder()
encoder.add_text("Your document or conversation text here")
encoder.build("memory.mv2")

retriever = MemvidRetriever("memory.mv2")
results = retriever.search("what was discussed about deployment?")
```

## Introduction
Memvid provides a drop-in memory layer for AI agents that stores and retrieves information from a single portable file. Instead of managing vector databases, embedding services, and chunking pipelines, you encode documents into a compact .mv2 file and query it with semantic search. The entire system runs locally with no external dependencies.

## What Memvid Does
- Encodes text, documents, and conversations into a single .mv2 memory file
- Provides semantic search over stored memories with sub-second latency
- Runs entirely offline without vector database infrastructure
- Supports incremental memory updates without full re-encoding
- Integrates with Python and Rust ecosystems for agent development

## Architecture Overview
Memvid encodes text chunks into compact vector representations stored in a custom binary format (.mv2). The format uses video-codec-inspired compression to achieve high density. At query time, a FAISS-based index enables fast approximate nearest-neighbor search over the encoded vectors. The single-file design means memories are portable, versioned, and require no running services.

## Self-Hosting & Configuration
- Install via pip (Python) or cargo (Rust core)
- No external services, databases, or API keys required
- Configure embedding model, chunk size, and overlap in code
- Memory files are portable across machines
- Supports both CPU and GPU-accelerated encoding

## Key Features
- Single-file memory format eliminates infrastructure overhead
- Semantic search without running a vector database
- Portable .mv2 files can be shared or version-controlled
- Sub-second retrieval even on large memory stores
- Works offline with local embedding models

## Comparison with Similar Tools
- **ChromaDB** — requires a running server; Memvid is a single file
- **FAISS** — low-level index library; Memvid adds encoding, storage, and retrieval
- **LanceDB** — embedded vector DB; Memvid focuses on agent memory semantics
- **Pinecone** — managed cloud service; Memvid is fully local and free
- **txtai** — broader NLP toolkit; Memvid is purpose-built for agent memory

## FAQ
**Q: How large can a memory file get?**
A: A .mv2 file can store millions of text chunks. Practical limits depend on available RAM during search.

**Q: Can I update a memory file without re-encoding everything?**
A: Yes. Memvid supports incremental appends to existing memory files.

**Q: What embedding models does it use?**
A: By default it uses sentence-transformers models. You can configure any model that produces fixed-size vectors.

**Q: Is there a Rust API?**
A: Yes. The core is written in Rust with Python bindings. Both interfaces are supported.

## Sources
- https://github.com/memvid/memvid


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Source: https://tokrepo.com/en/workflows/asset-c6f6a833
Author: Script Depot