Build AI agents that think, remember, and act. Start free on your machine. Scale to production with zero infrastructure.
Vector Vault gives you two ways to build:
🏠 Local Mode (Free) — Run entirely on your machine. No account needed. No limits. Perfect for learning, prototyping, and projects where your data stays local.
☁️ Cloud Platform — When you're ready for production, deploy to our Persistent Agentic Runtime (PAR). Sub-second responses, 99.9% uptime, visual workflow builder, and agents that can pause for days and resume instantly.
pip install vector-vaultNo signup. No API keys (except OpenAI for embeddings). Just code.
from vectorvault import Vault
# Create a local vault
vault = Vault(
vault='my_knowledge_base',
openai_key='YOUR_OPENAI_KEY',
local=True # Everything stays on your machine
)
# Add your data
vault.add("The mitochondria is the powerhouse of the cell")
vault.add("Neural networks are inspired by biological brains")
vault.add("Vector databases enable semantic search")
vault.get_vectors()
vault.save()
# Search by meaning, not keywords
results = vault.get_similar("How do AI systems learn?")
# → Returns: "Neural networks are inspired by biological brains"
# Or chat with your data
response = vault.get_chat(
"What powers the cell?",
get_context=True # Automatically retrieves relevant context
)Give any LLM access to your knowledge base with automatic context retrieval.
response = vault.get_chat(
"How do I configure authentication?",
get_context=True,
n_context=5
)Find content by meaning. Search "budget issues" and find documents about "financial constraints."
results = vault.get_similar("budget issues", n=10)Give your agents persistent memory across conversations.
# Store conversation
vault.add(f"User asked about {topic}. Agent responded with {response}")
vault.get_vectors()
vault.save()
# Later, retrieve relevant context
context = vault.get_similar(new_user_message)Turn any document collection into a question-answering system.
# Load documents
for doc in documents:
vault.add(doc.text, meta={'source': doc.filename})
vault.get_vectors()
vault.save()
# Answer questions
answer = vault.get_chat("What's the refund policy?", get_context=True)When you're ready to scale, Vector Vault Cloud provides:
Agents that pause for days, branch into parallel tasks, and resume instantly — without you managing servers.
Design agent workflows visually with drag-and-drop. Branching logic, approvals, integrations, all in the browser.
- Sub-second streaming responses
- 99.9% uptime SLA
- Auto-scaling to thousands of concurrent conversations
- SOC 2 compliant infrastructure
- Team collaboration
- Usage-based pricing
# Switch to cloud mode
vault = Vault(
user='you@company.com',
api_key='YOUR_VECTORVAULT_KEY',
openai_key='YOUR_OPENAI_KEY',
vault='production_kb'
)
# Same API, production infrastructure
response = vault.get_chat("Customer question here", get_context=True)
# Or run visual workflows
result = vault.run_flow('customer_support_agent', user_message="...")Get started at vectorvault.io →
# Local mode (free, no account)
vault = Vault(
vault='vault_name',
openai_key='sk-...',
local=True
)
# Cloud mode (production)
vault = Vault(
user='email',
api_key='vv_...',
openai_key='sk-...',
vault='vault_name'
)| Method | Description |
|---|---|
add(text, meta=None) |
Add text to the vault |
get_vectors() |
Generate embeddings |
save() |
Persist to storage |
get_similar(text, n=4) |
Semantic search |
get_chat(text, get_context=True) |
RAG chat |
get_items(ids) |
Retrieve by ID |
edit_item(id, text) |
Update item |
delete_items(ids) |
Remove items |
# Add + embed + save in one call
vault.add_n_save("Your text here")
# Stream responses
for chunk in vault.get_chat_stream("Your question"):
print(chunk, end='')Vector Vault uses FAISS (Facebook AI Similarity Search) for fast, accurate vector operations:
- Add your text data
- Embed using OpenAI's embedding models
- Search by semantic similarity
- Chat with automatic context retrieval
Local mode stores everything in ~/.vectorvault/. Cloud mode syncs to our managed infrastructure.
- Python 3.8+
- OpenAI API key (for embeddings)
- Website: vectorvault.io
- Vector Flow: app.vectorvault.io/vector-flow
- Full API Docs: Documentation
- Discord: Join the community
- JavaScript SDK: VectorVault-js
git clone https://github.com/John-Rood/VectorVault.git
cd VectorVault
pip install -e .
# Run tests
cd VectorVault-Testing
python run_tests.py # Cloud tests
python test_local_mode.py # Local testsMIT License
Start free. Scale infinitely. vectorvault.io