Integrations: from stand-in to production

Integrations are the real backends that take the place of the keyless stand-ins this course ran on, each a drop-in for the same call so the crew code does not change.

Last updated: 28 Sep, 2026 · CrewAI 1.15

Every lesson stayed runnable with no key by using a stand-in: a model you wrote, a word-hash embedder, a local store, a server started as a subprocess, a listener that printed events. Production swaps each one for a real backend. This lesson maps them, so a reader leaves knowing what to run for real, not only the stand-in.

What you used against a real one

Each row is the same argument with a different value. The left is what ran here; the middle is the production version; the right is where it comes from.

The model is a true drop-in

The stand-in subclassed BaseLLM, and CrewAI's LLM subclasses BaseLLM too. Because both are the same kind of object, swapping one for the other is one assignment. This line needs a key, so it does not run here.

from crewai import LLM

clerk.llm = LLM(model="gpt-4o-mini")  # needs OPENAI_API_KEY

The store, from a folder to a service

Memory writes to a local LanceDB folder by default. Point storage at a path to keep runs together, or at "qdrant-edge" to use Qdrant. The remember and recall calls are the same either way, which the run below shows: one Memory object writes to the folder, and a second one reopens the same folder and reads it back.

A store that outlives the process

import os
os.environ["OTEL_SDK_DISABLED"] = "true"
os.environ["CREWAI_DISABLE_TELEMETRY"] = "true"
from crewai import Memory
from crewai.events import crewai_event_bus
from embed import word_hash
from shop_llm import ShopLLM

one = Memory(llm=ShopLLM(model="shop"), embedder=word_hash, storage="./deskmem")
with crewai_event_bus.scoped_handlers():
    one.remember("Asha prefers email, not phone calls.",
                 scope="/customer/asha", categories=["preference"], importance=0.8)

two = Memory(llm=ShopLLM(model="shop"), embedder=word_hash, storage="./deskmem")
for match in two.recall("How does Asha want to be contacted?", depth="shallow", limit=1):
    print(match.record.content)

Reading the round trip

• The second Memory read the first one's fact from the same folder, so nothing was held only in the first process.
• The API did not change. Only storage moved from the default to a path; remember and recall are identical.
• The same swap reaches Qdrant with storage="qdrant-edge", which is how a desk shares memory across machines.

Stand-in against production, by concern

When to swap each piece

• Swap the model and embedder first, since they decide answer quality.
• Swap the store when more than one process or machine must share memory.
• Keep the stand-ins in your test suite, where a stable output is worth more than a real one.

Related

• Previous: Real model: swapping in a hosted LLM
• Next: Desk state and a reply guardrail
• See also: Watching a crew work
• Reference: CrewAI docs, Observability