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 agent frameworks
What you can build with Memvid
From simple chatbots to complex multi-agent systems, Memvid is powering the next generation of AI 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.
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 |
Migrate from your current solution in minutes
Read migration guide→Loved by developers worldwide
Hear how teams are building intelligent applications with Memvid
"Building AI agents with persistent memory used to require complex vector databases and infrastructure. With Memvid, everything is in one portable file. Our agents can now remember conversations and context across sessions effortlessly."
Sarah Chen
AI Engineer
"From Python to Node.js to Rust, the SDK consistency across languages meant our entire team could adopt Memvid immediately. The portable .mv2 format works everywhere - local dev, CI/CD, production. No vendor lock-in."
Alex Martinez
Principal Engineer
"The MCP integration made it incredibly easy to connect Claude to our knowledge bases. Setup took minutes, not days."
Marcus Johnson
Staff Engineer
"Parallel ingestion is a game changer. We indexed 50,000 research papers in under an hour with perfect accuracy."
Dr. Emily Rodriguez
Research Lead
"The hybrid search with BM25 and vector indices gives us the best of both worlds. Lexical precision plus semantic understanding."
James Park
ML Engineer
"We share .mv2 files across our team via Dropbox. No database setup, no servers. It just works."
Lisa Anderson
Product Lead
"Sub-millisecond search across millions of documents. The performance is incredible for such a simple file format."
David Kim
Tech Lead
