How can I use Memvid
Chat with Your Memory
Upload your docs, connect your data sources, and chat with your entire knowledge base, then keep growing and refining it over time.
Misa Customer Support
Built for eCommerce startups. Misa handles returns, FAQs, and order questions with the intelligence of your entire support history, so your team never repeats themselves.
Developer Tool
Integrate Memvid directly into your app or workflow using our SDKs or REST API to add persistent memory to any AI experience.
Custom Memory
Need something built exactly for your use case? Tell us what you need and we'll design a memory solution around your product, team, or workflow.
Give your agents context
in less than 5 minutes
Import Your Context
Drop in documents, notes, conversations, or any text. Memvid automatically chunks, embeds, and indexes everything.
Connect Your File to Agent
Connect any AI model or agent through MCP, SDK, or direct API. Get lightning-fast hybrid search combining BM25 lexical matching with semantic vector search.
Deploy Anywhere
Store your memory file locally, on-prem, in a private cloud, or public cloud, same file, same performance. No vendor lock-in.
Memvid works out of the box.
Here's how.
Single-File Architecture
Everything in one portable .mv2 file. Data, embeddings, indices, and WAL. No databases, no servers, no complexity.
Sub-5ms Search
Lightning-fast hybrid search combining BM25 lexical matching with semantic vector embeddings.
Crash-Safe & Deterministic
Embedded WAL ensures data integrity. Automatic recovery after crashes. Identical inputs produce identical outputs.
Multi-Language SDKs
Native bindings for Python, Node.js, and Rust. Plus CLI and MCP server for any AI framework.
Time-Based Queries
Built-in timeline index for temporal queries. Perfect for conversation history and time-sensitive retrieval.
Works Everywhere
Local-first, offline-capable. Share files via USB, cloud, or Git. No vendor lock-in.
Why thousands of devs choose Memvid
See how Memvid compares to traditional vector databases
| Feature | Memvid | Pinecone | Chroma | Weaviate | Qdrant |
|---|---|---|---|---|---|
Single Self-Contained File No databases, zero configuration setup | |||||
Zero Pre-Processing Use raw data as-is. No cleanup or format conversion required. | |||||
All-in-one RAG pipeline Embedding, chunking, retrieval, reasoning, all-in-one | |||||
Memory Layer + RAG deeper context-aware retrieval intelligence | |||||
Hybrid search (BM25 + vector) Best of lexical and semantic search | |||||
Embedded WAL (crash-safe) Built-in write-ahead logging | |||||
Built-in timeline index Query by time range out of the box |
Loved by Developers
See what developers are saying about Memvid on X.
"At Scaylor we've tested every major RAG provider and have seen the same issues with top K, embedding and re-ranking that we haven't seen with MemVid."
Shazor Khan
@shazorkhan01
"We took an objective look at all the major models and vector databases, and Memvid beat all of them on accuracy, cost, and inference speed. None were able to do what Memvid did."
JJ Gubbay
@JJGgubbay
"they just turned vector DBs into museum relics, 90% cheaper, zero latency, all in one tiny video file. the RAG empire has fallen, long live the revolution."
Anant Dadhich
@AnantDadhich6
"This is what we've been waiting for: https://github.com/memvid/claude-brain @memvidAI"
maikunari
@maikunari
"insane, I remember when it just came out and was like wtffff how is it possible"
Tadas Gedgaudas
@tadasgedgaudas
"honestly this is crazy cool man. zero infra and 5 min setup is insane. been following memvid since the start and this v2 looks even more insane"
geekylax
@geekylax
"I think this is an interesting concept: Instead of running complex RAG pipelines or server-based vector databases, Memvid enables fast retrieval directly from the file."
Darek Makowski
@makowskid
"Memvid's single file agent memory ditching RAG pipelines for portable, instant recall. Huge for reliable long running workflows. Congrats on back to back #1."
3quanax
@3quanax
"At Scaylor we've tested every major RAG provider and have seen the same issues with top K, embedding and re-ranking that we haven't seen with MemVid."
Shazor Khan
@shazorkhan01
"We took an objective look at all the major models and vector databases, and Memvid beat all of them on accuracy, cost, and inference speed. None were able to do what Memvid did."
JJ Gubbay
@JJGgubbay
"they just turned vector DBs into museum relics, 90% cheaper, zero latency, all in one tiny video file. the RAG empire has fallen, long live the revolution."
Anant Dadhich
@AnantDadhich6
"This is what we've been waiting for: https://github.com/memvid/claude-brain @memvidAI"
maikunari
@maikunari
"insane, I remember when it just came out and was like wtffff how is it possible"
Tadas Gedgaudas
@tadasgedgaudas
"honestly this is crazy cool man. zero infra and 5 min setup is insane. been following memvid since the start and this v2 looks even more insane"
geekylax
@geekylax
"I think this is an interesting concept: Instead of running complex RAG pipelines or server-based vector databases, Memvid enables fast retrieval directly from the file."
Darek Makowski
@makowskid
"Memvid's single file agent memory ditching RAG pipelines for portable, instant recall. Huge for reliable long running workflows. Congrats on back to back #1."
3quanax
@3quanax