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General Questions

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What is Memvid?

Memvid is a portable AI memory system that packages your data, embeddings, and search indices into a single .mv2 file. It’s designed for building RAG applications, AI agents, and knowledge bases without the complexity of traditional vector databases.

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Is Memvid open source?

Yes, the core library (memvid-core) is open source. The Python SDK and Node.js SDK are available as packages with comprehensive documentation.

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What makes Memvid different from other vector databases?

Memvid’s key differentiator is single-file portability. Unlike traditional vector databases that require servers and complex configurations, a .mv2 file contains everything, your data, embeddings, indices, and metadata, in one portable file.

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What platforms does Memvid support?

Memvid supports:
  • macOS (Intel and Apple Silicon)
  • Linux (x86_64 and ARM64)
  • Windows (x86_64)

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File Format

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Can I rely on a single .mv2 file in production?

Yes. Memvid is designed for production use. The .mv2 file is completely self-contained with no sidecar files, no external dependencies, and no hidden state. Copying the file transfers the entire memory, including the write-ahead log and all indices.

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How large can a .mv2 file be?

File size depends on your capacity tier:
TierCapacityWAL Size
Free1 GB4 MB
Developer25 GB16 MB
EnterpriseUnlimited64 MB
The embedded WAL automatically scales with file size for optimal performance.

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Can multiple processes access the same file?

Yes, with some rules:
  • Multiple readers: Allowed simultaneously
  • Single writer: Only one writer at a time
  • Read-only mode: Use read_only=True for concurrent read access
Writers use OS-level exclusive locks to prevent conflicts.

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Performance

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How fast is Memvid?

Memvid is built in Rust for maximum performance:
OperationPerformance
Search (1K docs)< 1ms
Search (100K docs)< 10ms
Single doc ingestion1-10 docs/sec
Batch ingestion (put_many)500-1000 docs/sec
WAL append< 0.1ms

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What search modes are available?

  • Lexical (lex): BM25 keyword search for exact matches
  • Semantic (sem): Vector search for conceptual similarity
  • Hybrid (auto): Combines both for best results (recommended)

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How do I optimize search performance?

  1. Build indices: Ensure lexical and vector indices are enabled
  2. Use batch ingestion: Use put_many() for 100-200x faster ingestion
  3. Enable parallel segments: Use --parallel-segments for large datasets
  4. Choose the right mode: Use lex for keywords, sem for concepts, auto for general queries

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SDKs and Integration

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Which programming languages are supported?

  • Python: pip install memvid-sdk
  • Node.js: npm install @memvid/sdk
  • Rust: Use memvid-core crate directly
  • CLI: cargo install memvid-cli

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Can I use Memvid with LangChain?

Yes! Both Python and Node.js SDKs support framework adapters: Python: 
from memvid_sdk import use
mem = use('langchain', 'knowledge.mv2')
tools = mem.tools  # LangChain StructuredTool objects
Node.js: 
import { use } from '@memvid/sdk';
const mv = await use('langchain', 'knowledge.mv2');

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What AI frameworks are supported?

Python SDK:
  • LangChain
  • LlamaIndex
  • CrewAI
  • AutoGen
  • Haystack
Node.js SDK:
  • Vercel AI SDK
  • OpenAI Functions
  • LangChain.js
  • Semantic Kernel

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Capacity and Storage

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What happens when I exceed capacity?

You’ll receive a CapacityExceeded error (MV001). Solutions:
  1. Delete unused frames: memvid delete knowledge.mv2 --frame-id <id>
  2. Vacuum to reclaim space: memvid doctor knowledge.mv2 --vacuum
  3. Create a larger memory file with a higher tier

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How do I check my storage usage?

memvid stats knowledge.mv2
This shows document count, size, capacity, and utilization percentage.

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Can I reduce storage size?

Yes, use vector compression: 
memvid put knowledge.mv2 --input docs/ --vector-compression
Vector compression provides 16x smaller vectors with minimal quality loss.

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Troubleshooting

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Why is my file locked?

Another process is using the file. Check for:
  • Other terminals running memvid commands
  • Running applications with open handles
  • Stale processes (use lsof your-file.mv2 to find them)
Use memvid who your-file.mv2 to see who holds the lock.

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Why are my searches returning no results?

  1. Check indices: Run memvid stats your-file.mv2 to verify indices exist
  2. Try different modes: Use --mode lex for keywords or --mode sem for concepts
  3. Rebuild indices: Run memvid doctor your-file.mv2 --rebuild-lex-index

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How do I recover from corruption?

Use the doctor command: 
# Preview repairs
memvid doctor your-file.mv2 --plan-only

# Apply repairs
memvid doctor your-file.mv2 --rebuild-time-index --rebuild-lex-index

# Verify
memvid verify your-file.mv2 --deep
The embedded WAL ensures your data survives unexpected shutdowns.

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Why is ingestion slow?

Use batch ingestion for better performance: 
# Instead of individual puts (1-10 docs/sec)
for doc in docs:
    mem.put(text=doc['text'], title=doc['title'])

# Use put_many (500-1000 docs/sec)
mem.put_many(docs)

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Getting Help

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Where can I report bugs?

Report issues on GitHub: github.com/memvid/memvid/issues

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Is there a community?

Yes! Join us on: