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.
Works with your favorite frameworks
What you can build with Memvid
Memvid is the first portable, serverless memory layer that gives every AI agent instant recall and persistent memory. Here are some interesting ways developers are using Memvid across hundreds of real-world applications.
AI Agents
Give your agents persistent memory across sessions. Build autonomous systems that learn and remember.
RAG Applications
Build retrieval-augmented generation systems with sub-5ms search latency. Perfect for chatbots and Q&A.
Knowledge Bases
Create searchable company wikis, documentation systems, and internal knowledge repositories.
Chatbot Memory
Add long-term memory to your chatbots. Remember user preferences, past conversations, and context.
Document Processing
Ingest PDFs, docs, and text at scale. Automatic chunking, embedding, and indexing.
Multi-Agent Systems
Share memory between agents. Build collaborative AI systems with shared context.
Built with Memvid
Discover real agents and projects built with Memvid. Created by our team and the community, and shared for anyone to explore, fork, and use.
commitreel
Time travel for checkpoints
Record a single MV2 tape, scrub a timeline, and replay any moment on demand. Perfect for debugging AI agent sessions.
claude-brain
Claude Code finally remembers
Give Claude Code photographic memory in ONE portable file. No database, no SQLite, no ChromaDB. Just a single .mv2 file.
adrflow
Capture architectural decisions
An MCP server that records decisions like 'let's use Postgres' while you code. Search 'why?' and get the full context back.
canvas
AI UI Kit powered by Memvid
Build AI-powered apps in minutes. A complete UI kit with React components, server utilities, and built-in memory management.
maw
Crawl any site. Search it forever.
Crawl any website into a single searchable file. Query it forever, offline. No more bookmarking docs you'll forget about. No more 47 browser tabs.
screenshot-memory
Search screenshots by content
Search your screenshots by content, not filename.
commitreel
Time travel for checkpoints
Record a single MV2 tape, scrub a timeline, and replay any moment on demand. Perfect for debugging AI agent sessions.
claude-brain
Claude Code finally remembers
Give Claude Code photographic memory in ONE portable file. No database, no SQLite, no ChromaDB. Just a single .mv2 file.
adrflow
Capture architectural decisions
An MCP server that records decisions like 'let's use Postgres' while you code. Search 'why?' and get the full context back.
canvas
AI UI Kit powered by Memvid
Build AI-powered apps in minutes. A complete UI kit with React components, server utilities, and built-in memory management.
maw
Crawl any site. Search it forever.
Crawl any website into a single searchable file. Query it forever, offline. No more bookmarking docs you'll forget about. No more 47 browser tabs.
screenshot-memory
Search screenshots by content
Search your screenshots by content, not filename.
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