Architecture

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System Overview

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Core Components

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Ingress Layer

• FastAPI Server (src/ingress/api.py) - Main HTTP/WebSocket server
• Platform Adapters - Telegram, Discord, Slack, WhatsApp adapters
• Message Validation - Input sanitization and validation
• Queue Publishing - Pushes messages to processing queue

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Message Queue

• Asynchronous message processing
• Load balancing across workers
• Retry mechanisms for failed tasks
• Priority-based task scheduling

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Worker Pool

• Agent Workers - Run LangGraph agent loops
• Scheduled Task Workers - Execute recurring tasks
• Ingestion Workers - Process files and documents
• Auto-scaling based on queue depth

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LangGraph Agent

# Simplified agent flow
State -> Tool Selection -> Tool Execution -> State Update -> Response

• Multi-step reasoning with LangGraph
• Conversational memory and context
• Dynamic tool selection
• Interrupt handling for long operations

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Tool Factory

• Integration Tools - Google, Microsoft, Notion, etc.
• Communication Tools - Send messages, intermediate updates
• Database Tools - User preferences, context storage
• Web Tools - Browsing, search, content extraction
• Utility Tools - File processing, scheduling

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Data Flow

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Message Processing Flow

1. Receive Message
   • User sends message via platform (Telegram, Discord, etc.)
   • Platform webhook hits ingress endpoint
   • Message validated and normalized
2. Queue & Route
   • Message published to Azure Service Bus
   • Worker picks up message from queue
   • Worker loads user context from database
3. Agent Processing
   • LangGraph agent initialized with tools
   • Agent reasons about task
   • Agent executes tools as needed
   • Intermediate updates sent to user
4. Response Delivery
   • Final response formatted for platform
   • Egress layer sends message
   • State saved to database

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Tool Execution Pattern

# Example tool execution
async def send_email(to: str, subject: str, body: str):
    # 1. Validate inputs
    # 2. Load user's email credentials
    # 3. Send via Gmail/Outlook API
    # 4. Return execution status
    return ToolExecutionResponse(status="success", result="Email sent")

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Technology Stack

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Backend

• Python 3.11+ - Core runtime
• FastAPI - Web framework
• LangGraph - Agent orchestration
• Pydantic - Data validation
• Motor - Async MongoDB driver

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AI & LLM

• Portkey - LLM gateway for routing
• OpenAI - Primary LLM provider
• Google Gemini - Alternative LLM provider
• LangChain - LLM abstractions and utilities

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Infrastructure

• Docker - Containerization
• Kubernetes - Orchestration
• Azure Service Bus - Message queue
• Azure Cosmos DB - Document database
• Azure Key Vault - Secrets management

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Web Automation

• Playwright - Browser automation
• browser-use - AI-powered browsing library
• BeautifulSoup - HTML parsing

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Design Principles

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Modularity

src/
  integrations/
    telegram/
      adapter.py      # Platform-specific logic
      handler.py      # Message handling
      sender.py       # Message sending

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Extensibility

1. Create new integration module
2. Implement base adapter interface
3. Register with tool factory
4. Configure credentials

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Scalability

• Stateless workers for horizontal scaling
• Queue-based architecture for load distribution
• Async operations throughout
• Efficient caching strategies

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Reliability

• Automatic retry logic for transient failures
• Dead letter queues for failed messages
• Health checks and monitoring
• Graceful degradation

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Configuration Management

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Environment-Based Config

# src/config/settings.py
class Settings:
    environment: str  # development, staging, production
    mongodb_uri: str
    azure_keyvault_url: str
    # ... more settings

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Secrets Management

• API tokens
• Database credentials
• Integration credentials

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Feature Flags

• Beta feature access
• Tool availability
• Rate limiting

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Monitoring & Observability

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Logging

logger.info("Agent processing message", extra={
    "user_id": user_id,
    "platform": "telegram",
    "message_id": msg_id
})

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Metrics

• Message processing time
• Tool execution success rate
• Queue depth
• Error rates by type

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Tracing

• End-to-end message journey
• Tool execution times
• External API calls

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Security

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Authentication

• Platform-specific auth (bot tokens, OAuth)
• User identity verification
• Session management

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Authorization

• User-level permissions
• Integration access control
• Tool usage policies

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Data Protection

• Encryption at rest (Cosmos DB)
• Encryption in transit (TLS)
• Credential isolation per user
• PII handling compliance

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Performance Considerations

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Caching Strategy

• User context cached in-memory (TTL: 5 min)
• Integration credentials cached (TTL: 1 hour)
• Common responses cached

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Resource Management

• Connection pooling for databases
• Rate limiting on external APIs
• Memory limits per worker
• Timeout handling for long operations

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Optimization

• Lazy loading of integrations
• Async/await throughout
• Bulk operations where possible
• Efficient serialization

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Future Enhancements

• Multi-tenant Support - Isolated environments per organization
• Plugin System - User-installable tools and integrations
• Event Streaming - Real-time analytics with Kafka/Event Hub
• Edge Deployment - Regional workers for lower latency
• GraphQL API - Flexible data querying