Vector Vault is a foundational platform for building, deploying, and operating autonomous AI agents. While most tools focus on creating chatbots, we're engineering the production-grade infrastructure for persistent, stateful agents that execute complex tasks over time—with or without human supervision.

This isn't just a better vector database. It's the execution layer for the agentic future.

The AI industry is rapidly moving from simple request-response systems to autonomous digital workers. Vector Vault provides the core infrastructure to build agents that:

• Persist and adapt across multiple, asynchronous interactions.
• Execute complex, multi-step workflows independently.
• Maintain state while scaling in a serverless environment.
• Learn and evolve from every interaction.
• Operate autonomously for hours, days, or indefinitely.

Build sophisticated AI agents visually at app.vectorvault.io/vector-flow.

• Drag-and-drop agent design with advanced reasoning patterns.
• Multi-platform AI integration (OpenAI, Claude, Grok, Groq, Gemini).
• Python execution in secure, sandboxed containers (run code inside your flows).
• API integrations and external tool access.
• Real-time deployment with instant production updates.

Vector Flow runs on a Persistent Agentic Runtime. Compute remains serverless & stateless, but each agent’s state is stored durably in the cloud.
• Continuous state – agents pick up exactly where they left off, no context rebuilding.
• Temporal autonomy – agents respond to events over minutes, hours, or days.
• Scalable execution – state lives in PAR while stateless workers spin up on-demand to process steps.

This architecture is what lets Vector Vault move beyond chatbots and power long-running, auditable AI systems.

pip install vector-vault

from vectorvault import Vault

# Initialize with multi-platform AI support
vault = Vault(
    user='YOUR_EMAIL',
    api_key='YOUR_VECTOR_VAULT_API_KEY', 
    openai_key='YOUR_OPENAI_API_KEY',
    anthropic_key='YOUR_ANTHROPIC_KEY',  # optional
    vault='MY_AGENT_VAULT'
)

# Build your agent's knowledge base
vault.add('Your domain expertise, technical docs, and procedures...')
vault.get_vectors()
vault.save()

# Deploy autonomous workflows
agent_response = vault.run_flow(
    'intelligent_assistant',
    'Process this new customer inquiry',
    customer_data={"tier": "premium", "history": [...]},
    escalation_rules={"urgent": True}
)

# Get context-aware responses with smart history
response = vault.get_chat(
    "What about that issue we discussed earlier?",
    history=conversation_history,
    get_context=True,
    smart_history_search=True,  # AI generates contextual search queries
    model="claude-sonnet-4-0"   # Switch models seamlessly
)

Vector Vault supports all leading AI platforms under one interface. Switch between OpenAI, Claude, Grok and Gemini mid-conversation without changing your code:

# Start with OpenAI for analysis
response = vault.get_chat("Analyze this data", model="gpt-4o")

# Switch to Claude for reasoning  
response = vault.get_chat("What's your recommendation based on that?", model="claude-sonnet-4-0")

# Use Grok for creative tasks
response = vault.get_chat("Now, generate some innovative solutions", model="grok-4")

Traditional RAG fails when users say "Tell me more about that" or "How do I fix that?" Our Smart History Search solves this by using AI to generate a contextual search query based on the conversation history.

# User: "I'm getting database timeout errors in PostgreSQL"
# AI: "Here are some common causes..."
# User: "How do I fix that?"

# WITHOUT smart search: Searches "how do I fix that" → returns random, generic results
# WITH smart search: Searches "PostgreSQL database timeout errors fix" → returns specific solutions

response = vault.get_chat(
    "How do I fix that?",  # Vague, contextual query
    history=conversation_history,
    get_context=True,
    smart_history_search=True
)

Build agents that can see and understand images and documents.

response = vault.get_chat(
    "Analyze the key takeaways from this financial report",
    image_path="/path/to/report.pdf", 
    get_context=True
)

Create interactive and responsive agent experiences.

# Console applications
response = vault.print_stream(
    vault.get_chat_stream("Research the latest AI trends", get_context=True)
)

# Web applications (Server-Sent Events)
@app.route('/agent-stream')
def agent_chat():
    return Response(
        vault.cloud_stream(vault.get_chat_stream(user_message, get_context=True)),
        mimetype='text/event-stream'
    )

• Our Persistent Agentic Runtime is built for stateful, long-running tasks.
• Use our visual flow builder to design complex reasoning patterns.
• Achieve real-time deployment and enable continuous agent learning.

• Serverless scaling from prototype to enterprise-grade applications.
• Multi-platform AI support with automatic provider detection.
• Comprehensive logging and observability for every agent action.

• Execute complex AI workflows with one-line operations.
• Go from idea to deployed agent in minutes with the visual agent builder.
• Instant deployment with zero infrastructure management.

• Unlimited isolated databases for multi-tenant agent systems.
• Advanced RAG with smart contextual search that actually works.
• Continuous innovation in agentic capabilities to keep you ahead.

Vector Vault isn't just keeping up with the AI revolution—we're defining it. While others build better chatbots, we're creating the infrastructure for digital workers that think, persist, and execute autonomously.

1. 30-day free trial: VectorVault.io
2. Visual agent builder: app.vectorvault.io/vector-flow
3. Install the platform: pip install vector-vault

• Full Documentation: API Reference
• Vector Flow Guide: Agent Building Documentation
• Chat Functions: RAG & Streaming Guide
• Community: Discord
• JavaScript SDK: VectorVault-js

The age of autonomous AI agents is here. Build yours with Vector Vault.