Abstract Network Background

One Platform. Every Agent.

Build agents on Prajvis or bring your own. Every agent gets identity, storage, tools, sandbox, and observability.

The Reality Today

Building Agents Is Easy. Running Them Is Not.

You can spin up an agent in minutes. But making it production-ready? That's where months of work begins.

Who Did What?

Your agent acts on behalf of users, but you can't track which user triggered which action.

Scattered Context

Knowledge bases in one place, files in another, chat history somewhere else. Your agent has no unified memory.

Tool Sprawl

Every new capability means building another integration. MCP tools scattered across services with no central management.

Blind to Behavior

No visibility into what your agents are doing. No data to fine-tune. No insights to improve.

The Prajvis Way

Every Agent Gets a Complete Runtime

Build agents directly on Prajvis or bring your own. Every agent gets identity, storage, tools, an on-demand sandbox with terminal and browser, and complete observability. All agents become first-class citizens.

  • Dual Identity: Agents and Users get auth. Track who did what, for whom.
  • Unified Storage: Knowledge bases, files, notes, instructions all in one place.
  • On-Demand Sandbox: Terminal and browser access. Let agents execute code, browse the web, and interact with systems.
  • Complete Data Capture: Every conversation, every action, ready for insights and fine-tuning.
Your Agent
Custom built or external
Identity
Storage
Tools
Sandbox
Observe
Production-Ready Agent
With full observability
The Platform

Everything Your Agent Needs

Identity, storage, tools, sandbox, and observability. Build agents here or bring your own, all become production-ready.

Identity for Everyone

Agents get auth. Users get auth. You get visibility.

Every agent has its own identity with permissions and tool access. Every user gets social login built-in. Now you can track exactly what each agent did, for which user, at what time.

Agent-level permissions & tool access
Full action attribution
Dual Identity System
Agent Identity
agent_id:sales_agent_01
permissions:read, write, execute
tools:github, slack, db
User Identity
user_id:john@acme.com
login:Google OAuth
sessions:47 conversations
Track: Agent X did Action Y for User Z

Context That Sticks

Knowledge bases, files, notes, all in one place.

Upload documents for your agent to reference. Add notes and instructions. Store files that agents can work with. Everything your agent needs to be effective, centralized and always available.

Upload knowledge bases per agent
Custom instructions & notes
Vector-indexed for fast retrieval

Agent Knowledge Base

Product Documentation.pdf
2.4 MB • Indexed
Ready
FAQ Database.csv
847 entries • Indexed
Ready
Agent Instructions.md
Custom prompts • Active
Ready
Vector embeddings12,847 chunks

Any Provider, Any Tool

Bring your own models. Host your own MCP servers.

Connect OpenAI, Anthropic, or any provider. Switch models per agent. Host your MCP tools directly on Prajvis or connect remote servers. Mix and match for each task.

Dynamic provider switching
Host or connect MCP servers
Built-in tool catalog

Model Providers

OpenAI
GPT-4o, GPT-4o-mini
Active
Anthropic
Claude 3.5 Sonnet
Active
Groq
Llama 3.1, Mixtral
Configure
Switch providers per agent • Use different models for different tasks

On-Demand Sandbox

Terminal and browser. Your agent's own execution environment.

Every agent gets access to its own hosted sandbox on demand. Execute code in a terminal, browse the web, interact with systems, all in an isolated, secure environment that spins up when needed.

Full terminal with package installation
Headless browser for web tasks
Isolated & secure per task
Adjust isolation levels
Terminal Access
agent-sandbox
agent@prajvis:~/workspace$pip install pandas
Successfully installed pandas-2.1.0
agent@prajvis:~/workspace$python analyze.py
Processing 1,247 records...
✓ Analysis complete. Results saved.
agent@prajvis:~/workspace$_
Agents can install packages, run scripts, and process data

See Everything

Track behavior. Capture data. Improve continuously.

Every conversation logged. Every tool call recorded. Every user interaction captured. Use this data to understand behavior patterns, identify issues, and fine-tune your models.

Complete interaction history
User behavior insights
Export data for fine-tuning
User Activity
Last 7 days
1,247
Conversations
89
Active Users
4.2k
Tool Calls
Recent Activity
john@acme.com2m ago
sales_agent5m ago
sarah@corp.io12m ago
Plugin Architecture

Bring Anything. Host Everything.

Your agents, your MCP servers, your integrations - all running on Prajvis with access to shared context, storage, and providers.

Bring Your Agents

External A2A agents? Custom-built agents? They all become first-class citizens on Prajvis with full data capture and observability.

Upload knowledge bases per agent
Full conversation logging
Same controls as internal agents
Host Your MCP Tools

Build MCP servers and host them directly on Prajvis. Attach them to any agent. Use remote MCP servers alongside built-in tools.

Self-hosted or remote MCP
Attach tools to any agent
Built-in tool catalog
Request Any Storage

Plugins can request object storage, files and Postgres with vector DB. Use existing Prajvis data in your integrations and agents.

Object storage on demand
Postgres + Vector DB
Access shared Prajvis context
The Governance

Know What Every Agent Does

Full visibility into agent behavior. Complete data capture for insights and improvement. Enterprise-ready security and compliance.

Dual Identity System

Every agent gets its own identity with permissions. Every user gets authentication. Track exactly who did what and on whose behalf.

Agent identity
User auth
Action attribution
Complete Data Capture

Every conversation, every tool call, every user interaction is captured and stored. Use this data for insights, debugging, and model fine-tuning.

Conversation logs
Tool call history
Export for training
User Behavior Insights

Understand how users interact with your agents. Identify patterns, track engagement, and continuously improve based on real usage data.

Usage analytics
Behavior patterns
Engagement metrics
Multi-Tenant Isolation

Complete separation between workspaces. Each tenant's agents, users, and data are isolated. No cross-contamination possible.

Workspace isolation
Data separation
Secure by default
Role-Based Access Control

Define who can access which agents and tools. Set permissions at workspace, agent, or tool level. Full control over your deployment.

Granular permissions
Agent access control
Tool restrictions
Audit-Ready Logging

Complete audit trails for compliance. Every action logged with timestamps, user context, and outcomes. Export for regulatory review.

Full event logging
Compliance exports
Timestamp tracking
What You Can Build

From Prototype to Production

With auth, storage, tools, and observability handled,you can focus entirely on what your agents actually do.

  • Customer-Facing AI Products
  • Multi-Agent OrchestrationCoordinate multiple agents with shared context. Each agent has its own identity, but they can collaborate through Prajvis.
  • Data-Driven ImprovementAll chat data captured and ready for analysis. Understand user behavior, identify patterns, fine-tune your models.

Works With Any Stack

LangChainCrewAIAutoGenCustom AgentsAny A2A Agent
For Every Team

The Platform That Scales With You

Whether you're building your first agent or orchestrating dozens, Prajvis provides the runtime you need.

For Builders

Ship real AI products, not demos. Get auth, storage, and tools out of the box. Focus on your agent's unique capabilities.

For Teams

Share tools and context across agents. Unified observability for your whole fleet. Data capture for continuous improvement.

For Enterprises

Complete audit trails, multi-tenant isolation, and governance controls. Connect your own infrastructure. Know exactly what every agent does, for every user.

Get Started

Your Agents Deserve a Real Runtime

Stop duct-taping infrastructure. Give your agents identity, storage, tools, a sandbox, and observability in minutes, not months.