Time Required: 45 minutes
Prerequisites: Advanced Configuration Tutorial
Difficulty: Advanced
Welcome to the Integration Tutorial! You've mastered production configuration, and now it's time to integrate GraphMemory-IDE with external systems. You'll learn to build custom integrations, automate workflows, and create seamless data pipelines.
- Build custom API integrations with external services
- Create automated workflows and data pipelines
- Implement webhook systems for real-time updates
- Develop custom plugins and extensions
- Set up CI/CD integration for automated deployments
- Configure monitoring and alerting integrations
- Production GraphMemory-IDE deployment
- Access to external services (GitHub, Slack, etc.)
- API keys and authentication credentials
- 45 minutes of focused time
- Understanding of REST APIs and webhooks
Let's start by understanding how to integrate GraphMemory-IDE with external systems.
graph TD
subgraph "External Services"
GITHUB[GitHub API]
SLACK[Slack API]
NOTION[Notion API]
ZAPIER[Zapier]
WEBHOOK[Webhook Sources]
end
subgraph "GraphMemory-IDE"
API[API Gateway]
PROCESSOR[Event Processor]
TRANSFORMER[Data Transformer]
STORAGE[(Memory Storage)]
end
subgraph "Integration Layer"
ADAPTERS[Service Adapters]
QUEUE[Message Queue]
SCHEDULER[Task Scheduler]
MONITOR[Integration Monitor]
end
GITHUB --> ADAPTERS
SLACK --> ADAPTERS
NOTION --> ADAPTERS
ZAPIER --> WEBHOOK
WEBHOOK --> API
ADAPTERS --> QUEUE
QUEUE --> PROCESSOR
PROCESSOR --> TRANSFORMER
TRANSFORMER --> STORAGE
SCHEDULER --> ADAPTERS
MONITOR --> ADAPTERS
Create a GitHub integration for automatic knowledge capture:
# integrations/github_integration.py
import asyncio
import aiohttp
from typing import Dict, List, Any
from datetime import datetime
class GitHubIntegration:
def __init__(self, token: str, repo: str):
self.token = token
self.repo = repo
self.base_url = "https://api.github.com"
async def sync_issues_to_memories(self) -> List[Dict[str, Any]]:
"""Convert GitHub issues to memories"""
issues = await self._fetch_issues()
memories = []
for issue in issues:
memory = {
"content": f"GitHub Issue: {issue['title']}\n\n{issue['body']}",
"type": "task" if "bug" in issue.get("labels", []) else "insight",
"tags": ["github", "issue"] + [label["name"] for label in issue.get("labels", [])],
"metadata": {
"source": "github",
"issue_number": issue["number"],
"url": issue["html_url"],
"state": issue["state"],
"created_at": issue["created_at"]
}
}
memories.append(memory)
return memories
async def _fetch_issues(self) -> List[Dict[str, Any]]:
"""Fetch issues from GitHub API"""
headers = {
"Authorization": f"token {self.token}",
"Accept": "application/vnd.github.v3+json"
}
async with aiohttp.ClientSession() as session:
async with session.get(
f"{self.base_url}/repos/{self.repo}/issues",
headers=headers
) as response:
return await response.json()Build a Slack bot for interactive memory management:
# integrations/slack_integration.py
from slack_bolt.async_app import AsyncApp
from slack_bolt.adapter.fastapi.async_handler import AsyncSlackRequestHandler
import re
class SlackBot:
def __init__(self, bot_token: str, signing_secret: str):
self.app = AsyncApp(
token=bot_token,
signing_secret=signing_secret
)
self._setup_handlers()
def _setup_handlers(self):
@self.app.message(re.compile(r"remember (.+)"))
async def handle_remember(message, say, context):
"""Handle 'remember' commands"""
content = context["matches"][0]
memory = await self._create_memory(
content=content,
user_id=message["user"],
channel=message["channel"]
)
await say(f"✅ Remembered: {content[:50]}...")
@self.app.command("/search-memory")
async def handle_search(ack, respond, command):
"""Handle memory search commands"""
await ack()
query = command["text"]
results = await self._search_memories(query)
blocks = self._format_search_results(results)
await respond(blocks=blocks)
async def _create_memory(self, content: str, user_id: str, channel: str) -> Dict[str, Any]:
"""Create memory from Slack message"""
return {
"content": content,
"type": "insight",
"tags": ["slack", "conversation"],
"metadata": {
"source": "slack",
"user_id": user_id,
"channel": channel,
"timestamp": datetime.utcnow().isoformat()
}
}Create automated workflows for continuous knowledge capture.
flowchart TD
TRIGGER[Workflow Trigger] --> CONDITION{Condition Check}
CONDITION -->|Pass| ACTION[Execute Action]
CONDITION -->|Fail| SKIP[Skip Workflow]
ACTION --> TRANSFORM[Transform Data]
TRANSFORM --> VALIDATE[Validate Data]
VALIDATE --> STORE[Store Memory]
STORE --> NOTIFY[Send Notification]
subgraph "Trigger Types"
SCHEDULE[Scheduled]
WEBHOOK[Webhook]
API[API Call]
FILE[File Change]
end
subgraph "Actions"
FETCH[Fetch Data]
PROCESS[Process Content]
ENRICH[Enrich Metadata]
RELATE[Create Relationships]
end
SCHEDULE --> TRIGGER
WEBHOOK --> TRIGGER
API --> TRIGGER
FILE --> TRIGGER
ACTION --> FETCH
ACTION --> PROCESS
ACTION --> ENRICH
ACTION --> RELATE
# workflows/engine.py
from typing import Dict, Any, Callable, List
import asyncio
from datetime import datetime, timedelta
import json
class WorkflowEngine:
def __init__(self):
self.workflows: Dict[str, Workflow] = {}
self.running = False
async def register_workflow(self, workflow: 'Workflow'):
"""Register a new workflow"""
self.workflows[workflow.id] = workflow
async def start(self):
"""Start the workflow engine"""
self.running = True
await asyncio.gather(*[
self._run_scheduled_workflows(),
self._process_webhook_queue()
])
async def _run_scheduled_workflows(self):
"""Execute scheduled workflows"""
while self.running:
for workflow in self.workflows.values():
if workflow.should_run():
await self._execute_workflow(workflow)
await asyncio.sleep(60) # Check every minute
async def _execute_workflow(self, workflow: 'Workflow'):
"""Execute a single workflow"""
try:
context = WorkflowContext()
# Check conditions
if not await workflow.check_conditions(context):
return
# Execute actions
for action in workflow.actions:
await action.execute(context)
# Update last run
workflow.last_run = datetime.utcnow()
except Exception as e:
await self._handle_workflow_error(workflow, e)
class Workflow:
def __init__(self, id: str, name: str, schedule: str = None):
self.id = id
self.name = name
self.schedule = schedule
self.conditions: List[Callable] = []
self.actions: List['WorkflowAction'] = []
self.last_run: datetime = None
def should_run(self) -> bool:
"""Check if workflow should run based on schedule"""
if not self.schedule:
return False
# Simple cron-like scheduling
if self.schedule == "hourly":
return not self.last_run or \
datetime.utcnow() - self.last_run > timedelta(hours=1)
elif self.schedule == "daily":
return not self.last_run or \
datetime.utcnow() - self.last_run > timedelta(days=1)
return False# workflows/examples.py
# Daily GitHub sync workflow
github_sync_workflow = Workflow(
id="github_sync",
name="Daily GitHub Sync",
schedule="daily"
)
github_sync_workflow.actions = [
FetchGitHubIssuesAction(repo="company/project"),
TransformToMemoriesAction(),
StoreMemoriesAction(),
NotifySlackAction(channel="#knowledge")
]
# Real-time documentation workflow
docs_workflow = Workflow(
id="docs_update",
name="Documentation Update",
schedule=None # Webhook triggered
)
docs_workflow.actions = [
ExtractDocumentationAction(),
AnalyzeChangesAction(),
CreateMemoryAction(type="documentation"),
UpdateRelationshipsAction()
]
# Learning progress workflow
learning_workflow = Workflow(
id="learning_progress",
name="Learning Progress Tracking",
schedule="weekly"
)
learning_workflow.actions = [
AnalyzeLearningProgressAction(),
GenerateInsightsAction(),
CreateProgressReportAction(),
ScheduleReviewAction()
]Implement a robust webhook system for real-time integrations.
sequenceDiagram
participant EXT as External Service
participant WH as Webhook Receiver
participant QUEUE as Message Queue
participant PROC as Event Processor
participant STORE as Memory Store
participant NOTIFY as Notification Service
EXT->>WH: POST /webhook/github
WH->>WH: Validate Signature
WH->>QUEUE: Queue Event
WH->>EXT: 200 OK
QUEUE->>PROC: Process Event
PROC->>PROC: Transform Data
PROC->>STORE: Store Memory
STORE->>NOTIFY: Trigger Notification
NOTIFY->>EXT: Update Status
# webhooks/receiver.py
from fastapi import APIRouter, Request, HTTPException, Depends
from typing import Dict, Any
import hmac
import hashlib
import json
router = APIRouter()
class WebhookReceiver:
def __init__(self):
self.handlers: Dict[str, Callable] = {}
def register_handler(self, source: str, handler: Callable):
"""Register a webhook handler for a specific source"""
self.handlers[source] = handler
async def process_webhook(self, source: str, payload: Dict[str, Any], headers: Dict[str, str]):
"""Process incoming webhook"""
if source not in self.handlers:
raise HTTPException(status_code=404, detail=f"No handler for {source}")
# Validate signature
if not await self._validate_signature(source, payload, headers):
raise HTTPException(status_code=401, detail="Invalid signature")
# Queue for processing
await self._queue_event(source, payload)
return {"status": "accepted"}
@router.post("/webhook/{source}")
async def receive_webhook(
source: str,
request: Request,
receiver: WebhookReceiver = Depends()
):
"""Receive webhook from external service"""
payload = await request.json()
headers = dict(request.headers)
return await receiver.process_webhook(source, payload, headers)
# GitHub webhook handler
async def handle_github_webhook(payload: Dict[str, Any]):
"""Handle GitHub webhook events"""
event_type = payload.get("action")
if event_type == "opened" and "issue" in payload:
# New issue created
issue = payload["issue"]
memory = {
"content": f"New GitHub Issue: {issue['title']}\n\n{issue['body']}",
"type": "task",
"tags": ["github", "issue", "new"],
"metadata": {
"source": "github_webhook",
"issue_number": issue["number"],
"url": issue["html_url"]
}
}
await create_memory(memory)
elif event_type == "closed" and "issue" in payload:
# Issue closed - update existing memory
issue = payload["issue"]
await update_memory_status(
source_id=f"github_issue_{issue['number']}",
status="completed"
)# webhooks/processor.py
import asyncio
from typing import Dict, Any, List
from dataclasses import dataclass
@dataclass
class WebhookEvent:
source: str
event_type: str
payload: Dict[str, Any]
timestamp: datetime
processed: bool = False
class EventProcessor:
def __init__(self):
self.queue: asyncio.Queue = asyncio.Queue()
self.processors: Dict[str, Callable] = {}
async def start_processing(self):
"""Start the event processing loop"""
while True:
event = await self.queue.get()
await self._process_event(event)
self.queue.task_done()
async def _process_event(self, event: WebhookEvent):
"""Process a single webhook event"""
try:
processor = self.processors.get(event.source)
if processor:
await processor(event)
event.processed = True
except Exception as e:
await self._handle_processing_error(event, e)
async def queue_event(self, source: str, payload: Dict[str, Any]):
"""Queue an event for processing"""
event = WebhookEvent(
source=source,
event_type=payload.get("action", "unknown"),
payload=payload,
timestamp=datetime.utcnow()
)
await self.queue.put(event)Create custom plugins to extend GraphMemory-IDE functionality.
graph TD
subgraph "Plugin System"
REGISTRY[Plugin Registry]
LOADER[Plugin Loader]
MANAGER[Plugin Manager]
HOOKS[Hook System]
end
subgraph "Plugin Types"
IMPORT[Import Plugins]
EXPORT[Export Plugins]
TRANSFORM[Transform Plugins]
NOTIFY[Notification Plugins]
end
subgraph "Plugin Lifecycle"
INSTALL[Install]
ACTIVATE[Activate]
CONFIGURE[Configure]
EXECUTE[Execute]
DEACTIVATE[Deactivate]
end
REGISTRY --> LOADER
LOADER --> MANAGER
MANAGER --> HOOKS
HOOKS --> IMPORT
HOOKS --> EXPORT
HOOKS --> TRANSFORM
HOOKS --> NOTIFY
INSTALL --> ACTIVATE
ACTIVATE --> CONFIGURE
CONFIGURE --> EXECUTE
EXECUTE --> DEACTIVATE
# plugins/framework.py
from abc import ABC, abstractmethod
from typing import Dict, Any, List, Optional
import importlib
import inspect
class Plugin(ABC):
"""Base plugin class"""
def __init__(self):
self.name: str = ""
self.version: str = "1.0.0"
self.description: str = ""
self.author: str = ""
self.enabled: bool = False
@abstractmethod
async def initialize(self, config: Dict[str, Any]):
"""Initialize the plugin"""
pass
@abstractmethod
async def execute(self, context: Dict[str, Any]) -> Dict[str, Any]:
"""Execute the plugin"""
pass
async def cleanup(self):
"""Cleanup plugin resources"""
pass
class PluginManager:
def __init__(self):
self.plugins: Dict[str, Plugin] = {}
self.hooks: Dict[str, List[Plugin]] = {}
async def load_plugin(self, plugin_path: str, config: Dict[str, Any] = None):
"""Load a plugin from file"""
module = importlib.import_module(plugin_path)
# Find plugin class
for name, obj in inspect.getmembers(module):
if inspect.isclass(obj) and issubclass(obj, Plugin) and obj != Plugin:
plugin = obj()
await plugin.initialize(config or {})
self.plugins[plugin.name] = plugin
break
async def register_hook(self, hook_name: str, plugin: Plugin):
"""Register a plugin for a specific hook"""
if hook_name not in self.hooks:
self.hooks[hook_name] = []
self.hooks[hook_name].append(plugin)
async def execute_hook(self, hook_name: str, context: Dict[str, Any]) -> List[Dict[str, Any]]:
"""Execute all plugins registered for a hook"""
results = []
if hook_name in self.hooks:
for plugin in self.hooks[hook_name]:
if plugin.enabled:
result = await plugin.execute(context)
results.append(result)
return results# plugins/notion_importer.py
class NotionImporterPlugin(Plugin):
def __init__(self):
super().__init__()
self.name = "notion_importer"
self.description = "Import pages from Notion"
async def initialize(self, config: Dict[str, Any]):
self.notion_token = config.get("notion_token")
self.database_id = config.get("database_id")
async def execute(self, context: Dict[str, Any]) -> Dict[str, Any]:
"""Import Notion pages as memories"""
pages = await self._fetch_notion_pages()
memories = []
for page in pages:
memory = {
"content": page["content"],
"type": "note",
"tags": ["notion", "imported"],
"metadata": {
"source": "notion",
"page_id": page["id"],
"url": page["url"]
}
}
memories.append(memory)
return {"memories": memories, "count": len(memories)}
# plugins/ai_summarizer.py
class AISummarizerPlugin(Plugin):
def __init__(self):
super().__init__()
self.name = "ai_summarizer"
self.description = "Generate AI summaries of memories"
async def execute(self, context: Dict[str, Any]) -> Dict[str, Any]:
"""Generate summary for memory content"""
content = context.get("content", "")
if len(content) > 500: # Only summarize long content
summary = await self._generate_summary(content)
return {
"summary": summary,
"original_length": len(content),
"summary_length": len(summary)
}
return {"summary": content}Integrate GraphMemory-IDE with your CI/CD pipeline for automated deployments.
flowchart LR
CODE[Code Changes] --> BUILD[Build & Test]
BUILD --> DEPLOY_STAGING[Deploy to Staging]
DEPLOY_STAGING --> TEST_STAGING[Integration Tests]
TEST_STAGING --> DEPLOY_PROD[Deploy to Production]
DEPLOY_PROD --> MONITOR[Monitor Deployment]
subgraph "Automated Checks"
LINT[Code Linting]
SECURITY[Security Scan]
PERFORMANCE[Performance Test]
end
BUILD --> LINT
BUILD --> SECURITY
BUILD --> PERFORMANCE
subgraph "Deployment Strategies"
BLUE_GREEN[Blue-Green]
CANARY[Canary]
ROLLING[Rolling Update]
end
DEPLOY_PROD --> BLUE_GREEN
DEPLOY_PROD --> CANARY
DEPLOY_PROD --> ROLLING
# .github/workflows/deploy.yml
name: Deploy GraphMemory-IDE
on:
push:
branches: [main]
pull_request:
branches: [main]
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: Install dependencies
run: |
pip install -r requirements.txt
pip install -r requirements-dev.txt
- name: Run tests
run: |
pytest tests/ --cov=graphmemory --cov-report=xml
- name: Security scan
run: |
bandit -r graphmemory/
safety check
- name: Upload coverage
uses: codecov/codecov-action@v3
deploy-staging:
needs: test
runs-on: ubuntu-latest
if: github.ref == 'refs/heads/main'
steps:
- uses: actions/checkout@v3
- name: Deploy to staging
run: |
# Deploy to staging environment
kubectl apply -f k8s/staging/
kubectl rollout status deployment/graphmemory-api -n staging
- name: Run integration tests
run: |
pytest tests/integration/ --env=staging
deploy-production:
needs: deploy-staging
runs-on: ubuntu-latest
environment: production
steps:
- uses: actions/checkout@v3
- name: Deploy to production
run: |
# Blue-green deployment
kubectl apply -f k8s/production/
kubectl rollout status deployment/graphmemory-api -n production
- name: Health check
run: |
curl -f https://api.graphmemory.com/health
- name: Notify team
uses: 8398a7/action-slack@v3
with:
status: ${{ job.status }}
channel: '#deployments'Set up comprehensive monitoring and alerting integrations.
graph TD
subgraph "Data Sources"
APP[Application Metrics]
INFRA[Infrastructure Metrics]
LOGS[Application Logs]
TRACES[Distributed Traces]
end
subgraph "Collection & Processing"
PROMETHEUS[Prometheus]
ELASTICSEARCH[Elasticsearch]
JAEGER[Jaeger]
end
subgraph "Visualization & Alerting"
GRAFANA[Grafana Dashboards]
KIBANA[Kibana Logs]
ALERTMANAGER[Alert Manager]
end
subgraph "Notification Channels"
SLACK[Slack]
EMAIL[Email]
PAGERDUTY[PagerDuty]
WEBHOOK[Custom Webhooks]
end
APP --> PROMETHEUS
INFRA --> PROMETHEUS
LOGS --> ELASTICSEARCH
TRACES --> JAEGER
PROMETHEUS --> GRAFANA
ELASTICSEARCH --> KIBANA
PROMETHEUS --> ALERTMANAGER
ALERTMANAGER --> SLACK
ALERTMANAGER --> EMAIL
ALERTMANAGER --> PAGERDUTY
ALERTMANAGER --> WEBHOOK
# monitoring/integrations.py
import asyncio
import aiohttp
from typing import Dict, Any, List
class MonitoringIntegration:
def __init__(self, config: Dict[str, Any]):
self.slack_webhook = config.get("slack_webhook")
self.pagerduty_key = config.get("pagerduty_key")
self.custom_webhooks = config.get("custom_webhooks", [])
async def send_alert(self, alert: Dict[str, Any]):
"""Send alert to all configured channels"""
tasks = []
if self.slack_webhook:
tasks.append(self._send_slack_alert(alert))
if self.pagerduty_key:
tasks.append(self._send_pagerduty_alert(alert))
for webhook in self.custom_webhooks:
tasks.append(self._send_webhook_alert(webhook, alert))
await asyncio.gather(*tasks, return_exceptions=True)
async def _send_slack_alert(self, alert: Dict[str, Any]):
"""Send alert to Slack"""
severity_colors = {
"critical": "#FF0000",
"warning": "#FFA500",
"info": "#00FF00"
}
payload = {
"attachments": [{
"color": severity_colors.get(alert["severity"], "#808080"),
"title": f"🚨 {alert['title']}",
"text": alert["description"],
"fields": [
{"title": "Severity", "value": alert["severity"], "short": True},
{"title": "Service", "value": alert.get("service", "GraphMemory-IDE"), "short": True},
{"title": "Time", "value": alert["timestamp"], "short": True}
]
}]
}
async with aiohttp.ClientSession() as session:
await session.post(self.slack_webhook, json=payload)Congratulations! You've mastered advanced GraphMemory-IDE integrations:
✅ API Integrations - GitHub, Slack, Notion, and custom services
✅ Automated Workflows - Scheduled tasks and event-driven processes
✅ Webhook System - Real-time event processing and notifications
✅ Custom Plugins - Extensible plugin architecture and development
✅ CI/CD Integration - Automated testing, deployment, and monitoring
✅ Monitoring Integration - Comprehensive alerting and notification systems
You now have a fully integrated knowledge management system:
graph TD
ECOSYSTEM[Knowledge Ecosystem] --> CAPTURE[Automated Capture]
ECOSYSTEM --> PROCESS[Intelligent Processing]
ECOSYSTEM --> INTEGRATE[Seamless Integration]
CAPTURE --> GITHUB[GitHub Issues]
CAPTURE --> SLACK[Slack Conversations]
CAPTURE --> DOCS[Documentation]
PROCESS --> WORKFLOWS[Automated Workflows]
PROCESS --> PLUGINS[Custom Plugins]
PROCESS --> AI[AI Enhancement]
INTEGRATE --> CICD[CI/CD Pipeline]
INTEGRATE --> MONITORING[Monitoring Stack]
INTEGRATE --> NOTIFICATIONS[Smart Notifications]
Your integration mastery opens new possibilities:
- Build domain-specific integrations - Create integrations for your specific tools and workflows
- Develop advanced plugins - Build sophisticated AI-powered plugins
- Create integration templates - Share reusable integration patterns
- Contribute to the community - Share your integrations with other users
Problem: Webhook timeouts
# Solution: Implement async processing
@router.post("/webhook/{source}")
async def receive_webhook(source: str, request: Request):
payload = await request.json()
# Queue for async processing
await event_queue.put({"source": source, "payload": payload})
# Return immediately
return {"status": "accepted"}Problem: API rate limiting
# Solution: Implement rate limiting and retry logic
import asyncio
from tenacity import retry, stop_after_attempt, wait_exponential
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
async def api_call_with_retry(url: str, headers: Dict[str, str]):
async with aiohttp.ClientSession() as session:
async with session.get(url, headers=headers) as response:
if response.status == 429: # Rate limited
await asyncio.sleep(int(response.headers.get("Retry-After", 60)))
raise Exception("Rate limited")
return await response.json()Problem: Plugin conflicts
# Solution: Implement plugin isolation
class IsolatedPluginRunner:
async def run_plugin(self, plugin: Plugin, context: Dict[str, Any]):
try:
# Run plugin in isolated context
isolated_context = context.copy()
result = await plugin.execute(isolated_context)
return result
except Exception as e:
# Log error but don't affect other plugins
logger.error(f"Plugin {plugin.name} failed: {e}")
return {"error": str(e)}For integration support:
- Check the API Guide for integration endpoints
- Review Plugin Development Guide for custom plugins
- Join GitHub Discussions for community support
- API Guide - Complete API reference for integrations
- Plugin Development Guide - Advanced plugin development
- Operations Guide - Production operations and monitoring
🎯 Congratulations! You've completed the comprehensive GraphMemory-IDE tutorial series. You now have the skills to build, deploy, and integrate a production-ready knowledge management system.
Exceptional work! You're now ready to create sophisticated integrations and automated workflows that will transform how you capture, process, and leverage knowledge.