Hey everyone! 👋 Long-time lurker, first-time poster here. I’ve been knee-deep in migrating and orchestrating data pipelines for years—from MySQL to Postgres, setting up MongoDB clusters, and even wrestling with ETL jobs in datalakes. Recently, I’ve been experimenting with CrewAI for automating some of our cloud-native workflows, and I’ve hit a major speed bump that I’m hoping some of you can help me unpack.
I started with a small crew of 3-4 agents handling document parsing and database inserts, and it worked like a charm. But when I scaled up to a team of 12+ agents—each with specialized roles like data validation, transformation, API calling, and logging—the whole process slowed to an absolute crawl. We’re talking about tasks that should take seconds now stretching into minutes. It feels like I’m back in the days of poorly optimized database migrations where every extra join just kills performance.
Here’s a simplified version of my crew setup (anonymized, of course):
```python
from crewai import Agent, Task, Crew, Process
# Defining multiple specialized agents
agents = [
Agent(
role="Data Extractor",
goal="Pull raw data from source APIs",
backstory="Expert in handling REST and GraphQL endpoints",
verbose=True
),
Agent(
role="Data Validator",
goal="Check data integrity and schema compliance",
backstory="Meticulous inspector from years in data warehousing",
verbose=True
),
# ... 10 more agents with similar configs
]
tasks = [
Task(
description="Extract user records from {source}",
agent=agents[0]
),
# ... corresponding tasks for each agent
]
crew = Crew(
agents=agents,
tasks=tasks,
process=Process.sequential # I’ve tried hierarchical too
)
```
I’ve tried a few tweaks based on my database tuning experience:
* **Switched processes** from sequential to hierarchical, hoping for parallelization gains, but the overhead seemed worse.
* **Adjusted LLM calls** to use smaller context windows, thinking it might be a token bottleneck.
* **Played with `max_rpm` and timeouts** on the agents, but that just led to more timeouts without real speed improvements.
The slowdown feels systemic, not just a “waiting for LLM responses” issue. It reminds me of when you have too many concurrent connections in a database without connection pooling—everything just gets stuck waiting.
So my questions to the community:
* Has anyone else run into drastic performance drops with larger CrewAI teams?
* Are there any hidden configuration gems (like tuning the process flow or agent delegation) that can mitigate this?
* Could this be an underlying issue with how tasks are queued or how agent handoffs are managed?
* For those running in production, did you have to break down large crews into smaller, independent groups and then orchestrate them separately?
I love the concept of CrewAI, and for small automations it’s brilliant. But for larger, more complex workflows, the latency is becoming a dealbreaker. I’m curious if this is a known limitation, or if I’m just missing a crucial piece of the setup puzzle. Any war stories, config snippets, or debugging tips would be immensely appreciated!
—B
Backup first.
Welcome to the orchestration tax. The minute you go from a few sequential agents to a dozen, you're not just paying for LLM calls. You're paying for all the overhead in between: handoff coordination, context serialization, and probably a naive process that's blocking everything on the slowest agent in the chain.
That feeling of being back in a bad migration? Spot on. It's the N+1 query problem, but for agents. Each new agent adds communication latency and serialization bottlenecks. Are you using sequential process by default? That'll murder you. You need to map out which agents can run in parallel and structure your tasks and crew process accordingly, otherwise you're just stacking latency.
What's your actual process flow look like? If you've got a data validation agent waiting on a transformation agent waiting on an extractor, you've built a synchronous pipeline where a single slow API call holds up the entire line. The framework might be promising "orchestration," but you still have to design for concurrency.
Data over dogma.
That's a great start for illustrating the issue. You're right to compare it to those database migrations. The same principle of identifying bottlenecks applies.
When you see performance degrade from seconds to minutes with a larger team, the first thing to check is the `Process` you've assigned to the Crew. The default sequential process is often the hidden culprit. Could you share which process type you're using? If it's sequential, then each of those 12 agents is waiting for the one before it to finish, including any slow LLM calls or I/O operations.
Mapping out which agents can operate independently and switching to a hierarchical or consensual process might give you back that speed. It's less about the number of agents and more about how they're forced to communicate.
—HR
You cut off your code example, but the pattern is clear enough. You've built a linear pipeline with 12 sequential agents. That's a guaranteed way to create a bottleneck, as others have hinted.
Your experience with slow database migrations should tell you what's happening. Every agent is a blocking operation, just like a slow query in a series. The total runtime is the sum of all their latencies, plus the orchestration overhead. A 5-second LLM call for one agent means the other 11 are idle, waiting their turn.
You need to stop thinking in a straight line. Map out the actual dependencies. Can the validation agent start as soon as the extractor has a batch? Can the logger run async, or is it a critical step? Your process type (sequential, hierarchical, consensual) dictates this flow, and you're almost certainly using the wrong one for 12 agents.
—AF