#1313th of 18 in Memory

memvid

Single-file memory store for AI agents, with search built in

Stars
16.6k
License
Apache-2.0
Last commit
Jul 2026
Last release
May 2026
Language
Rust

Missed the latest GitHub check; these facts may be older.

Overview

Memvid packs documents, embeddings, a Tantivy full-text index, an HNSW vector index and a time index into one append-only .mv2 file, so no database server is needed. It is a Rust library (memvid-core) with a CLI and Node.js and Python SDKs. Optional features cover local ONNX text embeddings, OpenAI embeddings, CLIP image search, Whisper transcription and encrypted capsules.

Who it is for: Developers adding embedded long-term memory to agents without a vector DB

Strengths

  • One .mv2 file holds data and indexes; no .wal, .lock or sidecar files
  • Append-only frames with embedded WAL give crash recovery and queries over past states
  • Runs offline with local ONNX embeddings (BGE, Nomic, GTE) and Whisper models
  • SDKs for Rust, Node.js and Python plus a CLI; Apache-2.0 licensed

Weaknesses

  • Local embedding ONNX models must be downloaded manually with curl before use
  • Benchmark claims (+35% on LoCoMo, 0.025ms P50) come from the project itself
  • README gives no multi-user or concurrent-writer details for the single file
  • Features like predictive caching and branching are not shown in README examples

What it needs

  • GPU optional
  • Needs Rust 1.85.0+, ONNX embedding model files (for vec), OpenAI API key (for api_embed only)
  • Models: bge-small-en-v1.5, bge-base-en-v1.5, nomic-embed-text-v1.5, gte-large, OpenAI text-embedding-3

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