ai-control-plane-sdk

AI Control Plane SDK — Python

Easy integration for autonomous runtime control in your FastAPI microservices.

The SDK tracks API performance and receives intelligent AI-driven configuration from the Control Plane — automatically deciding caching, circuit breaking, rate limiting, and load shedding for your routes.

Installation

pip install ai-control-plane-sdk

Requirements: Python ≥ 3.9, FastAPI/Starlette ≥ 0.27, httpx ≥ 0.27

Links

• GitHub: https://github.com/Ayush-soni-12/AI_CONTROL_PLANE
• npm SDK: @ayushsoni12/ai-control-plane

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What Does This SDK Do?

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Quick Start

0. Get Your API Key

1. Open the Control Plane dashboard (http://localhost:3000)
2. Go to API Keys → Generate New Key
3. Copy the key and add it to your .env

1. Generate a Tenant ID

python -c "import uuid; print(uuid.uuid4().hex)"
# e.g.  bfc3aed7948e46fafacac26faf8b3159

Store it in your .env. It uniquely identifies your service/user — don't change it.

2. Initialize the SDK

# main.py
import os
from ai_control_plane import ControlPlaneSDK

sdk = ControlPlaneSDK(
    api_key=os.getenv("CONTROL_PLANE_API_KEY"),       # ⚠️ REQUIRED
    tenant_id=os.getenv("TENANT_ID"),                 # ⚠️ REQUIRED — generate above
    service_name="my-service",
    control_plane_url=os.getenv("CONTROL_PLANE_URL", "http://localhost:8000"),
)

.env file:

CONTROL_PLANE_API_KEY=your-api-key-here
CONTROL_PLANE_URL=http://localhost:8000
TENANT_ID=bfc3aed7948e46fafacac26faf8b3159

---

Two Integration Patterns

Pattern ① — Global Middleware (all routes)

Applies Control Plane tracking to every route with one line. Config is available as request.state.control_plane in any route.

from fastapi import FastAPI, Request
from ai_control_plane import ControlPlaneSDK
from ai_control_plane.middleware import FastAPIMiddleware

app = FastAPI()
sdk = ControlPlaneSDK(
    api_key=os.getenv("CONTROL_PLANE_API_KEY"),
    tenant_id=os.getenv("TENANT_ID"),
    service_name="product-service",
)

# ✅ One line — covers all routes
app.add_middleware(FastAPIMiddleware, sdk=sdk, priority="medium")

@app.get("/products")
async def get_products(request: Request):
    cp = request.state.control_plane   # ← injected by middleware

    if cp["should_skip"]:
        # Circuit breaker: upstream is degraded — return fallback
        return {"products": [], "note": cp["reason"]}

    if cp["is_load_shedding"]:
        # System is overloaded — reject this request gracefully
        from fastapi.responses import JSONResponse
        return JSONResponse(status_code=503, content={"error": "Service busy, try again shortly"})

    if cp["is_rate_limited_customer"]:
        # This specific end-user has exceeded their rate limit
        from fastapi.responses import JSONResponse
        return JSONResponse(
            status_code=429,
            headers={"Retry-After": str(cp["retry_after"])},
            content={"error": "Too many requests"},
        )

    # --- your normal business logic ---
    products = await db.get_products()

    # AI detected repeated identical requests → cache the response
    if cp["should_cache"]:
        await redis.set("products", products, ex=60)

    return {"products": products}

@app.get("/orders")
async def get_orders(request: Request):
    cp = request.state.control_plane   # same dict available here too
    # ...

---

Pattern ② — Per-Route Dependency (specific routes only)

Use FastAPI's Depends() to apply tracking to one specific route at a time. This gives you:

• Different priority levels per route (e.g. /checkout = "critical")
• Opt-in tracking — only the routes you choose are monitored
• The control_plane dict as a typed function argument (instead of request.state)

from fastapi import FastAPI, Request, Depends
from ai_control_plane import ControlPlaneSDK
from ai_control_plane.middleware import control_plane_dep

app = FastAPI()
sdk = ControlPlaneSDK(
    api_key=os.getenv("CONTROL_PLANE_API_KEY"),
    tenant_id=os.getenv("TENANT_ID"),
    service_name="product-service",
)

# ── Endpoint: /products  (priority = "medium") ─────────────────────────────
@app.get("/products/{product_id}")
async def get_product(
    product_id: int,
    request: Request,
    cp=Depends(control_plane_dep(sdk, "/products", priority="medium")),
    #           ↑ Only this route is tracked. "/products" is the endpoint
    #             key that groups signals in the Control Plane — use a
    #             fixed string, not the dynamic path ("/products/42").
):
    if cp["should_skip"]:
        return {"product": None, "reason": cp["reason"]}

    product = await db.get_product(product_id)
    return {"product": product}


# ── Endpoint: /checkout  (priority = "critical") ───────────────────────────
@app.post("/checkout")
async def checkout(
    request: Request,
    cp=Depends(control_plane_dep(sdk, "/checkout", priority="critical")),
    #           ↑ Critical priority → Control Plane will protect this
    #             route last when load-shedding starts.
):
    if cp["is_load_shedding"]:
        from fastapi.responses import JSONResponse
        return JSONResponse(status_code=503, content={"error": "Try again shortly"})

    if cp["is_rate_limited_customer"]:
        from fastapi.responses import JSONResponse
        return JSONResponse(
            status_code=429,
            headers={"Retry-After": str(cp["retry_after"])},
            content={"error": "Too many requests"},
        )

    result = await process_checkout(request)
    return {"success": True, "order_id": result.id}


# ── Endpoint: /health  (NOT tracked — no Depends) ─────────────────────────
@app.get("/health")
async def health():
    # Health check doesn't need AI tracking — just return 200
    return {"status": "ok"}

---

Pattern ③ — Manual Tracking (full control)

No middleware at all — call track() and get_config() yourself. Useful for background tasks, batch jobs, or any non-HTTP code.

import time

@app.get("/reports")
async def get_report(request: Request):
    start = time.monotonic()
    try:
        # Ask AI for config before doing any work
        config = await sdk.get_config("/reports", priority="low",
                                      customer_identifier=request.client.host)

        if config["circuit_breaker"]:
            return {"data": [], "reason": config["reason"]}

        report = await generate_report()

        # Track success explicitly
        await sdk.track("/reports", (time.monotonic() - start) * 1000, "success")
        return {"data": report}

    except Exception as e:
        # Track error explicitly
        await sdk.track("/reports", (time.monotonic() - start) * 1000, "error")
        raise

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control_plane Dict Reference

All patterns expose the same fields:

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ControlPlaneSDK API

ControlPlaneSDK(control_plane_url, service_name, tenant_id, api_key, timeout)

await sdk.track(endpoint, latency_ms, status, priority, customer_identifier)

Send a performance signal. Fire-and-forget — never raises.

await sdk.get_config(endpoint, priority, customer_identifier)

Fetch AI-driven runtime config. Returns safe defaults if unreachable.

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Error Handling

The SDK never crashes your service:

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License

MIT