memvid/memvid

Memory layer for AI Agents. Replace complex RAG pipelines with a serverless, single-file memory layer. Give your agents instant retrieval and long-term memory.

What it solves

Memvid provides a portable, serverless memory layer for AI agents, eliminating the need for complex RAG pipelines or external vector databases. It allows agents to maintain persistent, long-term memory that is stored in a single, portable file, making memory infrastructure-free and model-agnostic.

How it works

Memvid organizes AI memory as an append-only sequence of "Smart Frames"—immutable units containing content, timestamps, and metadata. These frames are packaged into a single .mv2 file that includes the data segments, full-text indices (Tantivy), vector indices (HNSW), and chronological ordering. This design enables fast retrieval, crash safety, and the ability to rewind or branch memory states without needing a database server.

Who it’s for

Developers building long-running AI agents, offline-first AI systems, enterprise knowledge bases, and auditable AI workflows who need a lightweight, persistent memory solution that can be carried across different environments.

Highlights

  • Single-File Storage: All data, embeddings, and search structures are contained in one .mv2 file with no sidecar files.
  • High Performance: Offers ultra-low latency (0.025ms P50) and high throughput for memory recall.
  • Multi-Modal Support: Includes features for PDF text extraction, CLIP visual embeddings for image search, and Whisper audio transcription.
  • Time-Travel Debugging: Supports querying past memory states and replaying or branching the memory timeline.
  • Local & Cloud Embeddings: Supports local ONNX models (like BGE-small) and cloud APIs (OpenAI).

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