Backend Implementation Strategy

This document outlines the backend implementation strategy for the Meta Agent Platform, including architecture, technical dependencies, and development approach.

Overview

The Meta Agent Platform backend is designed as a modular, scalable system that supports various agent execution models, workflow orchestration, and other core features. This reference guide provides a comprehensive overview of the backend architecture, technical dependencies, and implementation strategy.

Backend Architecture

Core Components

API Layer: RESTful endpoints for frontend communication
Workflow Orchestration Engine: Manages workflow execution and state
Agent Execution Service: Handles different agent execution types
Authentication & Authorization: User and service authentication
Data Storage: Persistence for workflows, agents, runs, etc.
Observability: Logging, metrics, and tracing

Architecture Diagram

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │
│  Frontend UI    │────▶│  API Layer      │────▶│  Data Storage   │
│  (SvelteKit)    │     │  (FastAPI)      │     │  (PostgreSQL)   │
│                 │     │                 │     │                 │
└─────────────────┘     └────────┬────────┘     └─────────────────┘
                                 │
                                 ▼
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│                 │     │                 │     │                 │
│  Agent Registry │◀───▶│  Workflow       │────▶│  Observability  │
│  & Execution    │     │  Orchestration  │     │  (OpenTelemetry)│
│                 │     │  (Temporal)     │     │                 │
└─────────────────┘     └─────────────────┘     └─────────────────┘
        │
        ▼
┌─────────────┐ ┌─────────────┐ ┌─────────────┐
│             │ │             │ │             │
│  Docker     │ │  API-Based  │ │  Framework  │
│  Agents     │ │  Agents     │ │  Agents     │
│             │ │             │ │             │
└─────────────┘ └─────────────┘ └─────────────┘

Technical Dependencies

Core Backend Framework

FastAPI (v0.104+): For building high-performance, type-safe APIs
Pydantic (v2.5+): For data validation and settings management
SQLAlchemy (v2.0+): For ORM and database interactions
Alembic: For database migrations
httpx (v0.24+): For making HTTP requests to external services

Database

PostgreSQL (v15+): Primary relational database
pgvector extension: For vector similarity search (AI embeddings)
pgsodium extension: For advanced encryption features
PostgREST: For automatic API generation (optional)

Workflow Orchestration

Temporal.io: For reliable workflow orchestration
Temporal Python SDK: For integrating with Temporal
Temporal Server: For workflow execution and state management

Agent Execution

Docker SDK for Python: For managing Docker containers
A2A Protocol Client: For interacting with A2A-compatible agents
API Client Libraries: For API-based agent execution

Authentication & Authorization

Keycloak (v23+): For authentication and identity management
Keycloak Python SDK: For backend integration

Caching & Messaging

Redis (v7+): For caching and pub/sub messaging
Celery (v5.3+): For background task processing (optional)

Observability

OpenTelemetry (v1.19+): For distributed tracing
Prometheus Client: For metrics collection
Langfuse: For LLM observability
Structlog: For structured logging

Implementation Strategy

Phase 1: Foundation (4-6 weeks)

Project Setup
Initialize FastAPI project structure
Configure PostgreSQL with required extensions
Set up authentication with Keycloak
Implement basic API endpoints
Core Data Models
Define SQLAlchemy models for:
- Users and workspaces
- Agents and agent configurations
- Workflows and workflow definitions
- Workflow runs and execution logs
Basic API Layer
Implement CRUD operations for agents
Implement CRUD operations for workflows
Add basic authentication and authorization

Phase 2: Workflow Orchestration (4-6 weeks)

Temporal Integration
Set up Temporal server
Implement workflow definitions in Temporal
Create workflow execution service
Agent Execution Framework
Implement Docker container execution adapter
Implement API-based agent adapter
Create basic A2A protocol adapter
Workflow Management
Implement workflow versioning
Add workflow validation
Create workflow execution history

Phase 3: Advanced Features (6-8 weeks)

HITL Integration
Implement task queue for human review
Add notification system
Create approval workflows
Enhanced Agent Support
Implement full A2A protocol support
Add multi-modal agent execution
Create agent marketplace integration
Observability
Implement comprehensive logging
Add metrics collection
Set up distributed tracing
Integrate LLM observability

Application Structure

backend/
├── app/
│   ├── api/
│   │   ├── v1/
│   │   │   ├── agents.py
│   │   │   ├── workflows.py
│   │   │   ├── runs.py
│   │   │   ├── hitl.py
│   │   │   └── users.py
│   │   └── dependencies.py
│   ├── core/
│   │   ├── config.py
│   │   ├── security.py
│   │   └── logging.py
│   ├── db/
│   │   ├── base.py
│   │   ├── session.py
│   │   └── init_db.py
│   ├── models/
│   │   ├── agent.py
│   │   ├── workflow.py
│   │   ├── run.py
│   │   └── user.py
│   ├── schemas/
│   │   ├── agent.py
│   │   ├── workflow.py
│   │   ├── run.py
│   │   └── user.py
│   ├── services/
│   │   ├── agent_execution/
│   │   │   ├── docker_adapter.py
│   │   │   ├── api_adapter.py
│   │   │   └── a2a_adapter.py
│   │   ├── workflow/
│   │   │   ├── temporal_client.py
│   │   │   ├── workflow_manager.py
│   │   │   └── workflow_validator.py
│   │   └── hitl/
│   │       ├── task_queue.py
│   │       └── notification.py
│   └── main.py
├── alembic/
│   ├── versions/
│   └── alembic.ini
├── tests/
│   ├── api/
│   ├── services/
│   └── conftest.py
├── docker-compose.yml
├── Dockerfile
├── pyproject.toml
└── README.md

Development and Deployment

Local Development Setup

Use Docker Compose for local development:

version: '3.8'

services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: postgres
      POSTGRES_DB: meta_agent_platform
    ports:
      - "5432:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data

  redis:
    image: redis:7
    ports:
      - "6379:6379"

  temporal:
    image: temporalio/auto-setup:1.20.0
    environment:
      - CASSANDRA_SEEDS=temporal-cassandra
      - DYNAMIC_CONFIG_FILE_PATH=config/dynamicconfig/development.yaml
    ports:
      - "7233:7233"
    depends_on:
      - temporal-cassandra

  temporal-cassandra:
    image: cassandra:3.11
    ports:
      - "9042:9042"
    volumes:
      - temporal_cassandra_data:/var/lib/cassandra

  temporal-ui:
    image: temporalio/ui:2.10.3
    environment:
      - TEMPORAL_ADDRESS=temporal:7233
    ports:
      - "8088:8080"
    depends_on:
      - temporal

  api:
    build:
      context: .
      dockerfile: Dockerfile
    command: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
    volumes:
      - .:/app
    ports:
      - "8000:8000"
    environment:
      - DATABASE_URL=postgresql://postgres:postgres@postgres:5432/meta_agent_platform
      - REDIS_URL=redis://redis:6379/0
      - TEMPORAL_SERVER_URL=temporal:7233
    depends_on:
      - postgres
      - redis
      - temporal

volumes:
  postgres_data:
  temporal_cassandra_data:

Deployment Options

Based on the project documentation, several deployment options are available:

Docker Compose: For simple self-hosted deployments
Kubernetes: For scalable, production deployments
Cloudflare Workers/Pages: For lightweight components
SST.dev: For AWS-based fragmented deployment

For MVP, start with a simple Docker Compose deployment and then evolve to a more sophisticated approach as needed.

Technical Challenges and Considerations

Docker Execution Security: Ensure proper isolation and resource limits for Docker-based agent execution.
A2A Protocol Evolution: The A2A/Open Agent Protocol is still evolving, so design your adapter with flexibility to accommodate changes.
Database Schema Evolution: Use Alembic for database migrations to manage schema changes over time.
Authentication Integration: Properly integrate Keycloak for secure authentication and identity management.
Temporal Learning Curve: Temporal has a learning curve, so allocate time for the team to become familiar with its concepts.
Observability Setup: Implement comprehensive logging, metrics, and tracing from the start to make debugging easier.

MVP Approach

For the MVP, focus on:

Core Functionality: Implement the essential features needed for basic agent orchestration.
Simplified Architecture: Start with a monolithic approach before moving to microservices.
Mock Data: Use mock data for initial development and testing.
Minimal Dependencies: Start with the essential dependencies and add more as needed.
Iterative Development: Build and test in small increments, focusing on one component at a time.

Conclusion

The backend implementation strategy outlined in this document provides a comprehensive roadmap for developing the Meta Agent Platform backend. By following this approach, you can build a robust, scalable, and maintainable backend that supports the platform's core features and can evolve as the platform grows.

Last updated: 2024-05-20