How to Fix 'cold start latency during development' in AutoGen (Python)

What this error means

cold start latency during development usually shows up when your AutoGen agent takes too long to initialize on the first run. In Python projects, that often means model clients, tools, or nested agents are being created repeatedly instead of once and reused.

You’ll typically see it when running local scripts, notebooks, or FastAPI endpoints that rebuild the agent graph on every request.

The Most Common Cause

The #1 cause is creating AssistantAgent, UserProxyAgent, or model clients inside a request path or loop. That forces AutoGen to reinitialize everything on every call, which looks like a cold start problem.

Here’s the broken pattern:

# broken.py
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

def handle_request(user_message: str):
    model_client = OpenAIChatCompletionClient(
        model="gpt-4o-mini",
        api_key="YOUR_KEY",
    )

    agent = AssistantAgent(
        name="support_agent",
        model_client=model_client,
        system_message="You are a support assistant.",
    )

    result = agent.run_sync(task=user_message)
    return result.messages[-1].content

And here’s the fixed pattern:

# fixed.py
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(
    model="gpt-4o-mini",
    api_key="YOUR_KEY",
)

agent = AssistantAgent(
    name="support_agent",
    model_client=model_client,
    system_message="You are a support assistant.",
)

def handle_request(user_message: str):
    result = agent.run_sync(task=user_message)
    return result.messages[-1].content

If you’re using FastAPI, the same rule applies. Don’t build agents inside the route handler.

# bad
@app.post("/chat")
def chat(payload: ChatRequest):
    agent = build_agent()
    return agent.run_sync(task=payload.message)

# good
agent = build_agent()

@app.post("/chat")
def chat(payload: ChatRequest):
    return agent.run_sync(task=payload.message)

Other Possible Causes

1. Tool functions do expensive work at import time

If your tool module connects to databases, loads embeddings, or reads large files during import, startup slows down before AutoGen even runs.

# bad
db = connect_to_postgres()  # runs on import

def lookup_customer(customer_id: str) -> str:
    return db.fetch(customer_id)

Move expensive setup behind a lazy initializer.

db = None

def get_db():
    global db
    if db is None:
        db = connect_to_postgres()
    return db

2. You are recreating group chat state every turn

If you use GroupChat, RoundRobinGroupChat, or similar orchestration objects, don’t rebuild them for each message.

# bad
def run_chat(message: str):
    group_chat = GroupChat(agents=[agent1, agent2], messages=[])
    manager = GroupChatManager(group_chat=group_chat)

Keep the conversation object alive for the session scope.

group_chat = GroupChat(agents=[agent1, agent2], messages=[])
manager = GroupChatManager(group_chat=group_chat)

3. Model client configuration is forcing repeated auth or network setup

Misconfigured Azure/OpenAI clients can add startup delay if they validate credentials repeatedly or hit metadata endpoints.

client = AzureOpenAIChatCompletionClient(
    model="gpt-4o",
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
    api_version="2024-02-15-preview",
)

Make sure env vars are loaded once and not recomputed in each request path. Also avoid constructing the client in helper functions called repeatedly.

4. You are running in notebook cells that redefine everything

In Jupyter, rerunning cells can create duplicate agents and stale event loops. That often looks like “it works once, then gets slow.”

# notebook anti-pattern
agent = AssistantAgent(...)
result = await agent.run(task="...")

Prefer one initialization cell and one execution cell. If you need to iterate fast, restart the kernel after changing core wiring.

How to Debug It

1. •Measure initialization separately from inference
   • •Add timestamps around client and agent construction.
   • •If startup takes most of the time, you’ve found the problem.

import time

start = time.perf_counter()
client = OpenAIChatCompletionClient(model="gpt-4o-mini", api_key="YOUR_KEY")
agent = AssistantAgent(name="support_agent", model_client=client)
print("init:", time.perf_counter() - start)

2. •

   Check whether objects are recreated per request

   • •Log id(agent) and id(model_client).
   • •If those IDs change on every call, you’re rebuilding them.
3. •

   Strip the app down to one agent and one tool

   • •Remove database calls, retrieval tools, and group chat orchestration.
   • •If latency disappears, add components back one by one.
4. •

   Watch for repeated network calls during startup

   • •Enable debug logs for your HTTP client.
   • •If you see auth requests or schema fetches before the first prompt, that’s your cold start source.

Prevention

• •Initialize OpenAIChatCompletionClient, AssistantAgent, and tool registries at process startup, not inside handlers.
• •Keep expensive I/O out of imports; use lazy loading for DB connections, embeddings, and file reads.
• •In web apps, treat agents as application-scoped objects unless conversation state must be isolated per user session.

If you want stable latency in AutoGen Python apps, the rule is simple: build once, reuse often, and keep startup work out of hot paths.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.