# Transitioning from Demo to Production Library: KOI-MCP Integration
Based on the architectural overview, I see that the key value of this system isn't about "personalities" specifically, but rather the powerful pattern it demonstrates for distributed knowledge representation, dissemination, and discovery. Let me outline what a production-ready, extensible library version of this integration would look like.
## Core Architecture for a Production Library
### 1. Modular, Domain-Agnostic Core
The production library should abstract away the "personality" specific elements and provide a generic framework:
```python
# Example core module structure
koi_mcp/
├── core/
│ ├── rid/
│ │ ├── base.py # Base RID extension framework
│ │ └── registry.py # RID type registry
│ ├── schema/
│ │ ├── base.py # Base schema models
│ │ └── registry.py # Schema registry
│ ├── adapter/
│ │ ├── base.py # Abstract adapter interface
│ │ └── registry.py # Adapter registry
│ ├── handlers/
│ │ ├── base.py # Handler interfaces
│ │ ├── rid.py # Generic RID handlers
│ │ ├── bundle.py # Generic bundle handlers
│ │ └── network.py # Generic network handlers
│ └── nodes/
│ ├── base.py # Base node classes
│ ├── coordinator.py # Coordinator node base
│ └── producer.py # Producer node base
├── adapters/ # Concrete adapter implementations
├── extensions/ # Domain-specific extensions
├── server/ # MCP server implementations
├── utils/ # Utility functions
└── config.py # Configuration management
```
### 2. Domain Extension Framework
The library should provide a clear extension mechanism for new domain types:
```python
class DomainExtension:
"""Base class for domain-specific KOI-MCP extensions."""
@property
def rid_types(self):
"""Return RID types for this domain."""
return []
@property
def schemas(self):
"""Return schemas for this domain."""
return []
@property
def adapters(self):
"""Return MCP adapters for this domain."""
return []
def register_handlers(self, processor):
"""Register domain-specific handlers."""
pass
```
### 3. Plugin Architecture
Implement a plugin system that allows easy registration of domain extensions:
```python
class KoiMcpRegistry:
"""Central registry for KOI-MCP extensions."""
def __init__(self):
self.extensions = {}
self.rid_types = {}
self.schemas = {}
self.adapters = {}
def register_extension(self, name, extension):
"""Register a domain extension."""
self.extensions[name] = extension
# Register RID types
for rid_type in extension.rid_types:
self.rid_types[rid_type.namespace] = rid_type
# Register schemas
for schema in extension.schemas:
self.schemas[schema.__name__] = schema
# Register adapters
for adapter in extension.adapters:
self.adapters[adapter.__name__] = adapter
def configure_node(self, node, domain_names=None):
"""Configure a node with registered extensions."""
domains = domain_names or self.extensions.keys()
for name in domains:
if name in self.extensions:
extension = self.extensions[name]
extension.register_handlers(node.processor)
```
### 4. Domain-Agnostic Coordinator and Producer Base Classes
Create base classes that can be customized for specific domains but handle the core networking and adapter coordination:
```python
class CoordinatorNode:
"""Base coordinator node that can host multiple domain adapters."""
def __init__(self, name, base_url, registry_port, domains=None):
self.registry = KoiMcpRegistry()
# Initialize with domain extensions
if domains:
for domain in domains:
self.registry.register_extension(domain.name, domain)
# Initialize node with all registered RID types
self.node = self._create_node(name, base_url)
# Initialize adapters
self.adapters = self._create_adapters()
# Initialize server
self.server = self._create_server(registry_port)
# Configure node with extensions
self.registry.configure_node(self.node)
def _create_node(self, name, base_url):
# Create node with all registered RID types
pass
def _create_adapters(self):
# Create adapters from registry
pass
def _create_server(self, port):
# Create server with all adapters
pass
```
### 5. Extensible Schema System
Provide a robust base for schema definition and transformation:
```python
class SchemaRegistry:
"""Registry for domain schemas with validation and transformation."""
def __init__(self):
self.schemas = {}
self.transformers = {}
def register_schema(self, schema_cls):
"""Register a schema class."""
self.schemas[schema_cls.__name__] = schema_cls
def register_transformer(self, source_schema, target_schema, transformer):
"""Register a transformer between schemas."""
key = (source_schema.__name__, target_schema.__name__)
self.transformers[key] = transformer
def transform(self, source_obj, target_schema_name):
"""Transform an object from one schema to another."""
source_name = source_obj.__class__.__name__
key = (source_name, target_schema_name)
if key in self.transformers:
return self.transformers[key](source_obj)
raise ValueError(f"No transformer found for {source_name} to {target_schema_name}")
```
## Key Production Features
### 1. Robust Error Handling and Recovery
```python
class RobustKnowledgeHandler:
"""Base class for handlers with retry and error recovery."""
def __init__(self, max_retries=3, backoff_factor=1.5):
self.max_retries = max_retries
self.backoff_factor = backoff_factor
async def handle_with_retry(self, processor, kobj):
"""Handle a knowledge object with retry logic."""
retries = 0
delay = 1.0
while retries < self.max_retries:
try:
return await self.handle(processor, kobj)
except TransientError as e:
logger.warning(f"Transient error handling {kobj.rid}: {e}")
retries += 1
if retries >= self.max_retries:
logger.error(f"Failed to handle {kobj.rid} after {retries} retries")
return self.handle_failure(processor, kobj, e)
await asyncio.sleep(delay)
delay *= self.backoff_factor
except PermanentError as e:
logger.error(f"Permanent error handling {kobj.rid}: {e}")
return self.handle_failure(processor, kobj, e)
async def handle(self, processor, kobj):
"""Handle a knowledge object."""
raise NotImplementedError
def handle_failure(self, processor, kobj, error):
"""Handle a failure to process a knowledge object."""
return STOP_CHAIN
```
### 2. Monitoring and Observability
```python
class ObservableAdapter:
"""Adapter with built-in monitoring."""
def __init__(self, metrics_registry=None):
self.metrics = metrics_registry or PrometheusMetrics()
self.register_counter = self.metrics.create_counter(
"adapter_registrations_total",
"Total number of registrations processed by adapter"
)
self.query_counter = self.metrics.create_counter(
"adapter_queries_total",
"Total number of queries processed by adapter"
)
self.error_counter = self.metrics.create_counter(
"adapter_errors_total",
"Total number of errors encountered by adapter"
)
def register_with_metrics(self, entity):
"""Register an entity with metrics collection."""
try:
self.register(entity)
self.register_counter.inc({"entity_type": entity.__class__.__name__})
except Exception as e:
self.error_counter.inc({"operation": "register", "error": str(e)})
raise
```
### 3. Authentication and Authorization
```python
class SecureCoordinatorNode(CoordinatorNode):
"""Coordinator with authentication and authorization."""
def __init__(self, name, base_url, registry_port, auth_config, **kwargs):
super().__init__(name, base_url, registry_port, **kwargs)
self.auth_manager = AuthManager(auth_config)
def _create_server(self, port):
server = super()._create_server(port)
# Add authentication middleware
server.app.middleware("http")(self.auth_middleware)
# Add authorization handlers
for route in server.app.routes:
route.endpoint = self.authorize(route.endpoint)
return server
async def auth_middleware(self, request, call_next):
"""Authenticate incoming requests."""
token = request.headers.get("Authorization")
if not token:
return JSONResponse(status_code=401, content={"error": "Unauthorized"})
try:
request.state.user = self.auth_manager.validate_token(token)
return await call_next(request)
except AuthError as e:
return JSONResponse(status_code=401, content={"error": str(e)})
def authorize(self, endpoint):
"""Wrap endpoint with authorization check."""
async def authorized_endpoint(*args, **kwargs):
request = kwargs.get("request")
if not request or not hasattr(request.state, "user"):
return JSONResponse(status_code=401, content={"error": "Unauthorized"})
# Check permissions
required_permissions = getattr(endpoint, "required_permissions", [])
if required_permissions:
user_permissions = request.state.user.permissions
if not all(perm in user_permissions for perm in required_permissions):
return JSONResponse(status_code=403, content={"error": "Forbidden"})
return await endpoint(*args, **kwargs)
return authorized_endpoint
```
### 4. Persistence and Recovery
```python
class PersistentCoordinatorNode(CoordinatorNode):
"""Coordinator with persistence and recovery capabilities."""
def __init__(self, name, base_url, registry_port, storage_config, **kwargs):
self.storage = StorageProvider.create(storage_config)
super().__init__(name, base_url, registry_port, **kwargs)
async def start(self):
"""Start the node with recovery."""
await super().start()
# Recover state from persistent storage
await self.recover_state()
async def recover_state(self):
"""Recover state from persistent storage."""
# Load registered entities
entities = await self.storage.load_all()
# Re-register entities with adapters
for entity in entities:
for adapter in self.adapters.values():
if adapter.can_handle(entity):
await adapter.register(entity)
async def handle_bundle(self, processor, kobj):
"""Handle a bundle and persist it."""
result = await super().handle_bundle(processor, kobj)
# Persist the bundle if processing succeeded
if result != STOP_CHAIN:
await self.storage.store(kobj.rid, kobj.contents)
return result
```
## Domain-Specific Extensions
Instead of hard-coding "personalities," the library would provide example extensions:
```python
# Example: Data Catalog Extension
class DataCatalogExtension(DomainExtension):
"""Extension for distributed data catalog functionality."""
@property
def name(self):
return "data_catalog"
@property
def rid_types(self):
return [DatasetRID, DataSchemaRID]
@property
def schemas(self):
return [DatasetMetadata, DataSchemaDefinition]
@property
def adapters(self):
return [DatasetAdapter]
def register_handlers(self, processor):
# Register dataset handlers
@processor.register_handler(HandlerType.RID, rid_types=[DatasetRID])
def dataset_rid_handler(proc, kobj):
# Implementation
return kobj
# More handlers...
# Example: Service Registry Extension
class ServiceRegistryExtension(DomainExtension):
"""Extension for distributed service registry."""
@property
def name(self):
return "service_registry"
@property
def rid_types(self):
return [ServiceRID, EndpointRID]
# More implementation...
```
## API Design
The public API would be clean and focused on the extension patterns:
```python
# Example usage
from koi_mcp import CoordinatorNode, ProducerNode, DomainExtension
from koi_mcp.extensions.data_catalog import DataCatalogExtension
from koi_mcp.extensions.service_registry import ServiceRegistryExtension
# Create a coordinator with multiple domains
coordinator = CoordinatorNode(
name="multi-domain-coordinator",
base_url="http://localhost:9000/koi-net",
registry_port=9000,
domains=[
DataCatalogExtension(),
ServiceRegistryExtension()
]
)
# Create a data producer
data_producer = ProducerNode(
name="dataset-publisher",
base_url="http://localhost:8100/koi-net",
domains=[DataCatalogExtension()],
first_contact="http://localhost:9000/koi-net"
)
# Publish a dataset
dataset_metadata = DatasetMetadata(
name="customer_transactions",
description="Daily customer transaction records",
schema="transactions_schema_v1",
format="parquet",
location="s3://data-lake/transactions/",
update_frequency="daily"
)
data_producer.publish(dataset_metadata)
```
## Configuration Management
Use a hierarchical, extensible configuration system:
```python
from pydantic import BaseSettings, Field
from typing import Dict, List, Optional, Any
class NetworkSettings(BaseSettings):
first_contact: Optional[str] = None
connect_timeout: float = 10.0
request_timeout: float = 30.0
max_retries: int = 3
backoff_factor: float = 1.5
class PersistenceSettings(BaseSettings):
storage_type: str = "file" # or "redis", "postgres", etc.
storage_path: str = ".koi/storage"
ttl: Optional[int] = None
class SecuritySettings(BaseSettings):
enable_auth: bool = False
auth_provider: str = "none" # or "jwt", "oauth", etc.
auth_config: Dict[str, Any] = Field(default_factory=dict)
class DomainSettings(BaseSettings):
enabled: bool = True
config: Dict[str, Any] = Field(default_factory=dict)
class KoiMcpSettings(BaseSettings):
network: NetworkSettings = Field(default_factory=NetworkSettings)
persistence: PersistenceSettings = Field(default_factory=PersistenceSettings)
security: SecuritySettings = Field(default_factory=SecuritySettings)
domains: Dict[str, DomainSettings] = Field(default_factory=dict)
class Config:
env_prefix = "KOI_MCP_"
env_nested_delimiter = "__"
```
## Deployment Considerations
The library should support various deployment scenarios:
1. **Embedded** - Library used within an existing application
2. **Standalone** - Run as a dedicated service
3. **Containerized** - Packaged with Docker for orchestration
4. **Serverless** - Adaptable to serverless environments
```python
# Example Docker-ready entrypoint
def entrypoint():
"""Container entrypoint for KOI-MCP services."""
parser = argparse.ArgumentParser()
parser.add_argument("--role", choices=["coordinator", "producer", "consumer"])
parser.add_argument("--config", default="/etc/koi-mcp/config.json")
args = parser.parse_args()
# Load config
config = KoiMcpSettings.parse_file(args.config)
# Set up logging
setup_logging(config)
# Initialize appropriate node type
if args.role == "coordinator":
node = create_coordinator_from_config(config)
elif args.role == "producer":
node = create_producer_from_config(config)
elif args.role == "consumer":
node = create_consumer_from_config(config)
else:
raise ValueError(f"Unknown role: {args.role}")
# Start node
node.start()
# Set up signal handlers
setup_signal_handlers(node)
# Run until terminated
try:
# Keep main thread alive
while True:
time.sleep(1)
except KeyboardInterrupt:
node.shutdown()
```
## Summary: From Demo to Production Library
The transition from the demo to a production library requires:
1. **Abstraction**: Replace the personality-specific code with a domain-agnostic core
2. **Extension**: Create a robust extension framework for domain-specific behaviors
3. **Resilience**: Add error handling, retry logic, and proper recovery mechanisms
4. **Observability**: Incorporate logging, metrics, and monitoring
5. **Security**: Add authentication, authorization, and secure communication
6. **Configurability**: Develop a comprehensive configuration system
7. **Deployment**: Support various deployment scenarios
The core architectural pattern remains the same:
- Define domain-specific RID types and schemas
- Distribute knowledge through the KOI network
- Transform to MCP-compatible resources via adapters
- Expose through standardized API endpoints
But the production library would be much more flexible, robust, and security-focused, with clear extension points for adding new domains beyond the personality example used in the demo.
