Haive Dataflow Documentation¶
Welcome to Haive Dataflow¶
The revolutionary AI component orchestration and real-time data flow platform - Haive Dataflow provides automatic discovery, intelligent registry management, streaming data pipelines, and MCP integration for building sophisticated AI systems that adapt, scale, and evolve in real-time.
🌊 Beyond Static Architectures: This isn’t just a component registry - it’s a living, breathing AI ecosystem featuring dynamic discovery, real-time streaming, intelligent persistence, and Model Context Protocol support that transforms how AI agents interact with data, tools, and each other!
Revolutionary Dataflow Platform¶
Enter the Future of AI System Architecture:
🔍 Automatic Component Discovery - Intelligent scanning, registration, and dependency management
📊 Real-Time Data Streaming - WebSocket-based streaming, event-driven architectures, live data flows
🗄️ Intelligent Registry System - Component metadata, configuration management, version tracking
🤖 MCP Integration - Model Context Protocol servers, tools, resources, and prompts
💾 Advanced Persistence Layer - Conversation history, state management, Supabase integration
🔌 Universal API Gateway - Multi-provider LLM support, unified interfaces, dynamic routing
📡 Event-Driven Architecture - Reactive patterns, observable streams, intelligent pipelines
🔧 Extensible Framework - Plugin architecture, custom providers, flexible serialization
Revolutionary Example: Build an AI system that automatically discovers new agents, registers their capabilities, streams data between components in real-time, persists conversations across sessions, and adapts its architecture dynamically - all with type-safe, observable data flows!
Core Platform Capabilities¶
Intelligent Component Management
Automatic discovery, registration, dependency tracking, and lifecycle management for the entire AI ecosystem.
Real-Time Data Flow Architecture
WebSocket streaming, event-driven pipelines, reactive patterns, and live data transformation capabilities.
Model Context Protocol Support
Native MCP server/client support, tool registration, resource management, and prompt orchestration.
Intelligent Data Persistence
Conversation history, state management, session handling, and Supabase integration for scalable storage.
Universal LLM Gateway
Multi-provider support, dynamic routing, unified interfaces, and intelligent load balancing.
Advanced Architectural Patterns
Event-driven design, reactive patterns, pipeline architectures, and scalable system design.
Revolutionary Intelligence Layer¶
Intelligent component discovery that automatically finds, registers, and manages agents, tools, engines, and games across your entire system.
Real-time data flow pipelines with WebSocket support, event routing, and intelligent transformation for live AI interactions.
Event-driven reactive patterns that enable observable data flows, intelligent routing, and adaptive system behavior.
Model Context Protocol integration providing standardized interfaces for tools, resources, and cross-system communication.
Quick Start: Intelligent Discovery¶
Experience automatic component discovery and registration:
from haive.dataflow import (
registry_system, discover_all, EntityType,
discover_agents, discover_tools
)
# Automatic discovery of all components
discovered = discover_all()
print(f"Discovered {len(discovered)} components:")
print(f"- {len(discovered['agents'])} agents")
print(f"- {len(discovered['tools'])} tools")
print(f"- {len(discovered['engines'])} engines")
# Register custom component
entity_id = registry_system.register_entity(
name="StreamProcessor",
type=EntityType.AGENT,
description="Real-time stream processing agent",
module_path="my_module.agents",
class_name="StreamProcessor",
metadata={
"capabilities": ["streaming", "transformation", "aggregation"],
"version": "1.0.0"
}
)
# Query components by capability
streaming_agents = registry_system.query_entities(
type=EntityType.AGENT,
metadata_filter={"capabilities": "streaming"}
)
Real-Time Streaming Architecture¶
Deploy sophisticated streaming data flows:
from haive.dataflow.streaming import (
StreamingPipeline, WebSocketManager,
EventRouter, TransformationNode
)
# Create streaming pipeline
pipeline = StreamingPipeline("ai-data-flow")
# Add transformation nodes
pipeline.add_node(
TransformationNode(
name="sentiment_analyzer",
transform_fn=analyze_sentiment,
output_schema=SentimentResult
)
)
pipeline.add_node(
TransformationNode(
name="entity_extractor",
transform_fn=extract_entities,
output_schema=EntityList
)
)
# Connect nodes in pipeline
pipeline.connect("input", "sentiment_analyzer")
pipeline.connect("sentiment_analyzer", "entity_extractor")
pipeline.connect("entity_extractor", "output")
# Stream data through pipeline
async with pipeline.stream() as stream:
async for result in stream.process(data_source):
print(f"Processed: {result}")
MCP Server Integration¶
Native Model Context Protocol support:
from haive.dataflow.mcp import (
MCPServer, MCPToolRegistry,
MCPResource, MCPPrompt
)
# Create MCP server
mcp_server = MCPServer(
name="haive-mcp-server",
version="1.0.0",
transport="stdio"
)
# Register tools
@mcp_server.tool("analyze_data")
async def analyze_data(data: dict) -> dict:
"""Analyze data using AI models."""
# Tool implementation
return {"analysis": "results"}
# Register resources
@mcp_server.resource("knowledge_base")
async def get_knowledge() -> dict:
"""Access knowledge base."""
return {"knowledge": "data"}
# Register prompts
@mcp_server.prompt("code_review")
def code_review_prompt(code: str) -> str:
"""Generate code review prompt."""
return f"Review this code for quality: {code}"
# Start MCP server
await mcp_server.start()
Advanced Persistence Layer¶
Intelligent data persistence and state management:
from haive.dataflow.persistence import (
ConversationManager, StateStore,
SupabaseAdapter, SessionHandler
)
# Initialize persistence layer
persistence = SupabaseAdapter(
url=os.getenv("SUPABASE_URL"),
key=os.getenv("SUPABASE_KEY")
)
# Conversation management
conversation_mgr = ConversationManager(persistence)
# Store conversation
conversation_id = await conversation_mgr.create_conversation(
agent_id="assistant-001",
user_id="user-123",
metadata={"context": "customer_support"}
)
# Add messages
await conversation_mgr.add_message(
conversation_id=conversation_id,
role="user",
content="How can I integrate MCP?"
)
# Retrieve conversation history
history = await conversation_mgr.get_conversation(conversation_id)
# State persistence
state_store = StateStore(persistence)
await state_store.save_state(
entity_id="agent-001",
state_data={"memory": "important_context"}
)
Platform Architecture Innovation¶
Revolutionary Framework Design:
Reactive architecture with event sourcing, CQRS patterns, and observable data streams for scalable AI systems.
WebSocket-based streaming with backpressure handling, intelligent routing, and real-time transformations.
Performance & Integration Metrics¶
Discovery Speed: <100ms for component scanning and registration
Streaming Latency: <5ms for data pipeline processing
Registry Capacity: 10,000+ components with instant lookup
MCP Compatibility: Full Model Context Protocol 1.0 support
Persistence Scale: Billions of messages with Supabase integration
API Gateway: 20+ LLM providers with unified interface
Platform Capabilities Deep Dive¶
- 🔍 Registry & Discovery System
Automatic component discovery with dependency resolution, metadata management, and lifecycle tracking.
- 🌊 Streaming Data Architecture
Real-time WebSocket streaming with event routing, transformation pipelines, and backpressure handling.
- 🤖 MCP Protocol Support
Native Model Context Protocol integration for standardized tool, resource, and prompt management.
- 💾 Persistence & State Management
Scalable conversation history, state persistence, and session management with cloud storage.
- 🔌 Universal API Gateway
Multi-provider LLM support with dynamic routing, rate limiting, and intelligent load balancing.
- 🏗️ Architectural Patterns
Event-driven design, reactive patterns, and microservice architectures for scalable AI systems.
Next Steps¶
Registry & Discovery System - Master component discovery and registration
Streaming Intelligence - Build real-time data pipelines
MCP Integration - Integrate Model Context Protocol
Research Applications¶
- Academic Research
Dynamic AI system architectures
Real-time data flow optimization
Component discovery algorithms
Event-driven AI patterns
- Commercial Applications
Enterprise AI orchestration
Real-time analytics pipelines
Scalable conversation systems
Multi-tenant AI platforms
- Innovation & Development
Adaptive AI architectures
Self-organizing systems
Intelligent routing algorithms
Distributed AI coordination
Getting Help¶
Documentation: Comprehensive guides and API references
GitHub Issues: https://github.com/haive-ai/haive-dataflow/issues
Community Forum: Join our dataflow architecture discussions
Enterprise Support: Professional consulting for large-scale deployments
—
The Future of AI System Architecture Starts Here - Deploy intelligent component discovery, real-time data streaming, and adaptive architectures that transform static AI systems into living, evolving intelligence platforms! 🚀