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.
fromvectorvaultimportVault# Create a local vaultvault=Vault(
vault='my_knowledge_base',
openai_key='YOUR_OPENAI_KEY',
local=True# Everything stays on your machine
)
# Add your datavault.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 keywordsresults=vault.get_similar("How do AI systems learn?")
# → Returns: "Neural networks are inspired by biological brains"# Or chat with your dataresponse=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 conversationvault.add(f"User asked about {topic}. Agent responded with {response}")
vault.get_vectors()
vault.save()
# Later, retrieve relevant contextcontext=vault.get_similar(new_user_message)Turn any document collection into a question-answering system.
# Load documentsfordocindocuments:
vault.add(doc.text, meta={'source': doc.filename})
vault.get_vectors()
vault.save()
# Answer questionsanswer=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 modevault=Vault(
user='you@company.com',
api_key='YOUR_VECTORVAULT_KEY',
openai_key='YOUR_OPENAI_KEY',
vault='production_kb'
)
# Same API, production infrastructureresponse=vault.get_chat("Customer question here", get_context=True)
# Or run visual workflowsresult=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 callvault.add_n_save("Your text here")
# Stream responsesforchunkinvault.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 testscd VectorVault-Testing
python run_tests.py # Cloud tests
python test_local_mode.py # Local testsMIT License
Start free. Scale infinitely.vectorvault.io