🧱 Enterprise Python Agent Template (Production-Ready)

🧱 Enterprise Python Agent Template (Production-Ready)

This is not a toy LangChain script.

This is a microservice-grade structure.

1️⃣ Repository Structure

enterprise-agent/

│

├── app/

│   ├── main.py                 # FastAPI entrypoint

│   ├── config.py               # Settings (Pydantic)

│   │

│   ├── api/

│   │   ├── routes.py           # HTTP endpoints

│   │   └── schemas.py          # Request/Response models

│   │

│   ├── agent/

│   │   ├── orchestrator.py     # LangGraph state machine

│   │   ├── state.py            # Typed agent state

│   │   ├── tools.py            # Tool adapters

│   │   └── prompts.py          # Prompt templates

│   │

│   ├── memory/

│   │   ├── redis_memory.py

│   │   └── vector_store.py     # LlamaIndex integration

│   │

│   ├── models/

│   │   └── llm_gateway.py      # Model abstraction layer

│   │

│   ├── observability/

│   │   ├── logging.py

│   │   └── tracing.py

│   │

│   └── core/

│       ├── exceptions.py

│       └── constants.py

│

├── tests/

│

├── Dockerfile

├── docker-compose.yml

├── pyproject.toml

├── Makefile

└── README.md

That structure alone eliminates 70% of chaos.

2️⃣ Architectural Layers

🔹 API Layer (FastAPI)

• Accept request

• Validate schema

• Pass to orchestrator

• Return structured result

No business logic here.

🔹 Orchestrator Layer (LangGraph)

State machine like:

class AgentState(TypedDict):

    input: str

    context: dict

    plan: list[str]

    result: str

    error: str | None

Graph:

Receive → Plan → Execute Tools → Evaluate → Respond

Explicit transitions.

Deterministic branches.

Retry nodes if needed.

This is your “canonical core.”

🔹 Tool Adapter Layer

Never call tools directly in prompts.

Instead:

class SalesforceTool:

    def execute(self, payload: dict) -> dict:

        ...

Clean, injectable, mockable.

🔹 LLM Gateway (Critical)

Never call OpenAI or Claude directly inside orchestrator.

Instead:

class LLMGateway:

    def init(self, provider: str):

        ...

    def generate(self, messages: list[dict]) -> str:

        ...

That keeps you:

• Model-agnostic

• Swap-ready

• Future-proof

🔹 Memory Layer

Redis for:

• Short-term state

• Conversation persistence

• Workflow context

LlamaIndex for:

• RAG

• Document querying

• Enterprise knowledge ingestion

🔹 Observability Layer

Every request:

• Correlation ID

• Structured logging

• Latency metrics

• Tool invocation tracing

Without observability, agents become black boxes.

You don’t allow black boxes.

3️⃣ Deployment Hardening

Dockerfile

• Multi-stage build

• Non-root user

• Healthcheck endpoint

Health Endpoint

GET /health

Checks:

• Redis connection

• LLM provider reachable

• Vector store reachable

4️⃣ Optional Event-Driven Version

If event-driven:

Add:

app/messaging/

    kafka_consumer.py

    kafka_producer.py

Then:

API becomes optional.

Service becomes event listener.

5️⃣ Why This Beats “Generate Agent”

Because once this template exists:

Next agent =

• Copy repo

• Define new state

• Define new tools

• Adjust graph

Everything else stays hardened.

That’s how you go from 70% → 90%.

6️⃣ What NOT to Do

❌ Don’t let LangChain logic leak into API layer

❌ Don’t let prompts call external APIs directly

❌ Don’t tie memory to provider

❌ Don’t skip tracing