The Pythonic Multi-Agent AI Framework

Transform complex AI applications into simple, composable agent systems using decorator-based APIs, secure messaging, and distributed deployment.

Simple Agents → Powerful Results Build complex AI systems from specialized agents that self-organize
Get Started See Examples
v0.7.16 MIT License Python 3.9+

The Coral Reef Philosophy

Coral polyps are absurdly simple. But thousands of them, working together, create Earth's most complex ecosystems. Praval applies this insight to AI: complexity from collaboration, not from complexity.

🎯

Single Responsibility

Each agent excels at one thing—like coral polyps with specialized functions. A researcher researches. An analyst analyzes. No "god agents" trying to do everything. Complexity emerges from collaboration, not from individual complexity.

🌊

Message-Driven Architecture

Spores carry structured knowledge through the reef like chemical signals in water. Agents broadcast findings and respond to what matters to them—natural information flow without tight coupling.

🏛️

Choreography over Orchestration

No central controller. Agents self-organize based on message types, like polyps responding to chemical signals. System-level intelligence emerges from agent-level interactions—coordination without orchestration.

🌿

Composable by Design

Add new specialists without touching existing agents. Each addition enriches the ecosystem organically—that's how reefs grow, and how your AI systems should scale.

Featured Project: Praval Deep Research

Local-first AI research assistant demonstrating production-grade agent architecture

🤖 6 Specialized Agents
💾 4 Storage Systems
📝 16,000+ Lines
🔒 100% Local-First

Open Source Innovation

A complete research assistant for ArXiv papers showcasing Praval's capabilities in a real-world application. Built with identity-driven agents that learn from experience and self-organize through message passing.

  • Agent Memory & Learning: Agents remember past searches and continuously improve query optimization
  • Proactive Insights: AI-generated research areas with clickable topics for instant discovery
  • Privacy-First: All data stays local—papers, embeddings, and conversations never leave your machine
  • Production-Ready: Full Docker Compose stack with 7 services, health checks, and monitoring

Technology Stack

Frontend

React 19 + TypeScript
Real-time updates (SSE)

Vector Search

Qdrant database
Semantic paper discovery

Storage

PostgreSQL + Redis
MinIO object storage

Messaging

RabbitMQ
Agent coordination via spores

View on GitHub Read Documentation

Why Praval?

🎯

Pythonic Simplicity

Decorator-based @agent() API transforms functions into intelligent agents. Zero boilerplate, maximum clarity.

🌊

Self-Organizing Systems

Agents coordinate without central control through the Reef communication protocol. True emergent intelligence.

🧠

Multi-Layered Memory

4-tier memory architecture: short-term working memory, long-term vector storage, episodic conversation tracking, and semantic knowledge base with automatic PDF indexing.

🔒

Enterprise Ready

End-to-end encryption (Secure Spores), multi-protocol messaging (AMQP, MQTT, STOMP), and production deployment.

🎭

Multi-LLM Support

Seamlessly work with OpenAI, Anthropic, Cohere, and more. Switch providers without code changes.

🔧

Tool Integration

Declarative @tool decorator enables external API integrations, web searches, and custom capabilities. Share tools across agents or keep them specialized.

🐳

Docker Deployment

Full-stack containerization with RabbitMQ, Mosquitto, Qdrant, and Redis. Production-ready infrastructure.

📡

Reef Communication

Knowledge-first messaging through Spores. Agents broadcast and subscribe to structured semantic data.

💾

Unified Storage

Single API across FileSystem, PostgreSQL, Redis, S3, and Qdrant. Multi-provider strategies with automatic fallback.

🔐

Security First

End-to-end encryption with Secure Spores, multi-protocol messaging (AMQP, MQTT, STOMP), and message authentication.

📈

Horizontal Scaling

External reef with Redis/RabbitMQ, shared storage backends, Qdrant clustering, and configurable concurrency limits.

💪

Resilience

Agent failures don't crash the system. Automatic retries, circuit breakers, and comprehensive error handling.

Get Started in Minutes

1

Install Praval

pip install praval
2

Set Up Environment

# Add to .env file
OPENAI_API_KEY=your_openai_key
3

Run Your First Agent

# Test your installation
python -c "from praval import agent; print('Ready!')"

Or Deploy with Docker

# Full stack with RabbitMQ, Qdrant, Redis
docker-compose -f docker/docker-compose.secure.yml up -d

Learn More

📦

PyPI Package

Install via pip and view releases
⭐

GitHub Repository

Source code, examples, and issues
📖

Full Documentation

Comprehensive guides and API reference

About the Creator

Rajesh Sampathkumar

Praval was created by Rajesh Sampathkumar, a Data and AI leader with over 22 years of experience, including a decade spanning machine learning and AI. Rajesh has built AI and ML products and solutions in diverse domains such as automotive, aerospace, energy, telecommunications, and BFSI sectors.

His work focuses on architecting enterprise-scale generative AI applications, including agentic systems, RAG architectures, and compound AI systems. Praval emerged from the confluence of Rajesh's passion for self-organizing systems and complex adaptive systems on the one hand, and deep exploration of multi-agent frameworks on the other hand. Praval's vision is to make agentic AI development more Pythonic, composable, scalable and accessible.

@aiexplorations LinkedIn aiexplorations.in