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.
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What is Memvid?
Memvid is the knowledge layer for AI that gives AI agents photographic memory in a single portable .mv2 file. It replaces complex RAG pipelines and vector databases with a serverless, offline-capable memory format designed for fast retrieval and long-term persistence. Users drop in documents, notes, conversations, or any text, and Memvid automatically chunks, embeds, and indexes everything into one self-contained file.
Key features include sub-5ms hybrid search combining BM25 lexical matching with semantic vector embeddings, crash-safe persistence with embedded write-ahead logging (WAL), entity extraction with O(1) lookups, time-based queries with a built-in timeline index, and support for PDF ingestion with tables. The platform plugs into existing stacks through MCP, SDK (Node.js and Python), or direct API, and works with any AI model including GPT, Claude, Gemini, and Llama.
Memvid is designed for developers building AI agents, teams needing enterprise search and workflow automation, and organizations requiring privacy-sensitive local-first AI systems. It's ideal for long-term memory for AI agents, RAG applications without infrastructure overhead, knowledge bases and document search, and air-gapped or on-prem deployments where data cannot leave your walls.
Memvid pricing
Pricing model: Freemium
Memvid offers a free self-hosted option under Apache 2.0 license for individual developers and offline/local agents with no costs. Cloud plans include: Starter plan for small teams and power users with 25 GB total memory, 250k queries/month, and up to 5 memory files; Pro plan for fast-moving teams with 125 GB total memory, 20M queries/month, and up to 10 memory files; Enterprise plan for mission-critical systems with unlimited storage, queries, and memory files plus advanced features. Specific dollar amounts for paid plans are not publicly listed and require contacting sales for custom enterprise solutions.
Memvid pros
- Single self-contained .mv2 file - no databases required
- Sub-5ms search latency on consumer hardware
- Hybrid search combining BM25 lexical and vector semantic search
- Apache 2.0 open-source self-hosted version available
- Zero preprocessing - use raw data as-is
- Crash-safe with embedded write-ahead logging (WAL)
- Works fully offline with no cloud dependencies
- 93% cost savings on infrastructure compared to vector DBs
- 157 docs/sec ingestion speed
- Built-in timeline index for time-based queries
- Entity extraction with O(1) lookup for structured facts
- PDF ingestion with table extraction support
- 90% compression ratio - smaller storage footprint
- No vendor lock-in - deploy anywhere on-prem or cloud
- Saves developers 20+ hours per week
- CLI, Python SDK, and Node.js SDK support
- Deterministic - identical inputs produce identical outputs
- Plugs into LangChain, LlamaIndex, Vercel AI frameworks
Memvid cons
- Requires OPENAI_API_KEY for LLM Q&A synthesis feature
- Relatively new product with limited enterprise adoption history
- Cloud plans pricing not explicitly disclosed on website
- Entity extraction requires separate enrich command
- V1 used video/MP4 format, v2 uses .mv2 - potential migration needed
- Limited to 5 memory files on small team plan
- Custom enterprise solutions require contacting sales
- No built-in GUI - primarily CLI and API-driven
Frequently asked questions about Memvid
What is Memvid and what does it do?
Memvid is a memory layer for AI agents that replaces complex RAG pipelines and vector databases with a single portable .mv2 file. It gives AI systems photographic memory by storing content, embeddings, indices, metadata, and relationships in one self-contained file that enables fast semantic search, perfect keyword recall, and timeline/context memory without any databases or servers.
How does Memvid compare to traditional vector databases like Pinecone or Chroma?
Memvid outperforms traditional vector databases with sub-5ms search latency vs their higher latency, +35% higher accuracy, 93% cost savings on infrastructure, single self-contained file vs multiple database components, zero preprocessing required, all-in-one RAG pipeline with embedding/chunking/retrieval/reasoning, embedded WAL for crash-safety, and built-in timeline index. It stores 50,000 docs in just ~200 MB with 15x compression.
How do I install and get started with Memvid?
Install the CLI with 'npm install -g memvid-cli', then create a new memory with 'memvid create knowledge.mv2'. Add documents using 'memvid put knowledge.mv2 --title "Doc Title"'. Search immediately with 'memvid find knowledge.mv2 --query "your query"'. For LLM Q&A, set OPENAI_API_KEY and use 'memvid ask knowledge.mv2 --question "your question" --use-model openai'. Full setup takes under 5 minutes.
What is the single-file architecture and how does it work?
Memvid stores everything in one portable .mv2 file containing data, embeddings, indices, and write-ahead logging (WAL). The core technology uses Smart Frames - self-contained semantic objects holding raw content, embeddings, tags, timestamps, and relationships. Since structure is precomputed at write time, queries don't run pipelines; Memvid activates matching frames by meaning, keyword, time, or relational context and reconstructs answers instantly.
Does Memvid work offline and is my data private?
Yes, Memvid works fully offline with zero cloud dependencies. It's local-first and offline-capable, deployable on-prem, in private cloud, or even air-gapped environments. Your data never leaves your walls with no vendor lock-in. This makes it ideal for privacy-sensitive AI systems and organizations with sensitive data like healthcare that need on-prem, portable, private, and offline memory.
What AI models and frameworks does Memvid support?
Memvid works with every major AI model including GPT, Claude, Gemini, and Llama. It integrates with major frameworks like LangChain, LlamaIndex, and Vercel AI. You can connect any AI model or agent through MCP (Model Context Protocol), SDK (Node.js and Python), or direct API. The memory file is model-agnostic, so any AI agent that can read a file or call a function can use Memvid.
What is hybrid search and how does it improve retrieval?
Hybrid search combines BM25 lexical matching with semantic vector embeddings, giving you the best of both keyword and meaning-based search. BM25 provides perfect keyword recall while vector embeddings enable semantic understanding. This combination delivers sub-5ms search latency and +60% better retrieval accuracy vs traditional RAG pipelines, with search working immediately without pre-processing.
Can I extract structured facts and entities from my documents?
Yes, Memvid includes entity extraction with O(1) lookup speed. Use 'memvid enrich knowledge.mv2 --engine rules' to extract structured facts, then query entity state with 'memvid state knowledge.mv2 "Entity Name"'. This returns structured data like employer, role, location in O(1) time, perfect for knowledge bases with people, companies, projects, and other entities.
Is Memvid open source and what license does it use?
The core Memvid is open source under Apache 2.0 license for self-hosted use. The self-hosted version is ideal for individual developers and offline/local agents. Memvid is also available on GitHub with 10k+ stars and thousands of developers building on it. The open-source core uses the same technology that powers their enterprise cloud platform.
How do time-based queries work in Memvid?
Memvid has a built-in timeline index that enables temporal queries out of the box. Every Smart Frame contains timestamps, allowing you to query by time range for conversation history and time-sensitive retrieval. This is perfect for tracking how information changes over time, retrieving context from specific periods, and understanding the evolution of conversations or documents.