Architecture Guide

This guide explains the module structure and design philosophy of the agenticstar-platform SDK.

Design Philosophy

The agenticstar-platform SDK provides only platform infrastructure (DB, events, memory, storage, RAG, security). Developers have complete freedom to design agent logic (prompt design, tool selection, execution flow).

SDK Layer Architecture

The SDK is composed of a 3-layer architecture.

Layer	Role	Responsibility
Your AI Agent	Agent logic (prompts, tools, flow)	Freely designed by developers
SDK	Access to platform infrastructure	Provided by agenticstar-platform
Infrastructure	Databases, vector DBs, storage, etc.	Managed by the platform

Module List

Each module can be installed and used independently. There are no implicit dependencies between modules.

Module	pip extras	External Deps	Description
events	(core)	None	SSE / Webhook / DB event delivery
db	[db]	asyncpg, azure-identity	PostgreSQL + Azure AD auth
rag	[rag]	qdrant-client, openai	Vector search + Embedding
storage	[storage]	azure-storage-blob, boto3, google-cloud-storage	Multi-cloud object storage
auth	(core)	httpx	AGENTIC STAR Auth API client
memory	[memory]	mem0ai	Semantic memory (Mem0 + Qdrant)
security	[security]	httpx, google-cloud-dlp	Content moderation + PII detection
common	(core)	None	Validation + secret masking

Data Flow

1. User Request — A request from the user reaches the agent
2. Your AI Agent — Processes while streaming progress via the SDK's EventEmitter
3. SDK — Calls modules such as DB, RAG, Storage, and Memory
4. Infrastructure — Accesses PostgreSQL, Qdrant, cloud storage, etc.
5. Response — Delivers the final response via SSE with EventType.COMPLETION_SUCCESS

Configuration Management

All SDK modules read configuration from config.toml. Settings can be loaded uniformly via the from_toml() method.

config.tomlToml

[database]
host = "your-db.postgres.database.azure.com"
port = 5432
database = "agenticstar"
username = "agent_user"
password = "your_password"
pool_min_size = 2
pool_max_size = 10

[database.azure_ad]
enabled = true
tenant_id = "your-tenant-id"

[rag.qdrant]
url = "http://qdrant.internal:6333"
collection_name = "knowledge_base"
vector_size = 1536

[rag.embedding]
api_base = "https://your-endpoint.openai.azure.com/"
api_key = "your-api-key"
deployment_name = "text-embedding-3-large"

[storage.azure]
bucket_name = "agent-files"
connection_string = "DefaultEndpointsProtocol=https;..."

[memory.llm]
model = "azure_openai/gpt-4.1"
api_key = "your-api-key"
base_url = "https://your-endpoint.openai.azure.com/"

[security]
provider = "azure"
content_safety_endpoint = "https://your-cs.cognitiveservices.azure.com/"
content_safety_api_key = "your-key"

Loading from config.tomlPython

from agenticstar_platform.db import PostgreSQLConfig
from agenticstar_platform.rag import QdrantConfig, EmbeddingConfig

db_config = PostgreSQLConfig.from_toml("config.toml")
qdrant_config = QdrantConfig.from_toml("config.toml")
embed_config = EmbeddingConfig.from_toml("config.toml")

Example Project Structure

my-agent/
├── config.toml              # SDK settings
├── main.py                  # Agent entry point
├── agent.py                 # Your agent logic
├── tools/                   # Custom tools
│   ├── search.py
│   └── file_ops.py
├── Dockerfile
└── requirements.txt

Next Steps

Deploy Guide

Docker build and platform registration steps

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Events / Streaming Guide

Implementation details for SSE event delivery

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