CREWAI MULTI-AGENT SWARM OPTIMIZATION
Stop Swarm Freezes in CrewAI Multi-Agent Systems
Multi-agent systems multiply latency: 8 agents exchanging tasks generate 30+ roundtrips to frontier LLMs. JevProxy resolves deterministic delegation and tool calls in 18.4ms for $0.0001 per step.
18.4ms
Reflex Handoff Latency
1,450ms
Default Cloud LLM Call
75x
Agent Handoff Speedup
65%
Swarm Cost Savings
CrewAI Python Configuration
from crewai import Agent, Crew, Task, LLM
import os
# Point CrewAI LLM to JevProxy Reflex Gateway
fast_llm = LLM(
model="gpt-4o",
api_key=os.environ.get("JEVPROXY_API_KEY"),
base_url="https://api.jevproxy.com/v1"
)
# Agents benefit from 18.4ms deterministic tool routing
researcher = Agent(
role="Senior Research Analyst",
goal="Gather technical specs and verify facts",
backstory="You are an expert researcher with fast reflex verification.",
llm=fast_llm,
verbose=True
)Frequently Asked Questions
Does this work with CrewAI hierarchical process?
Yes. In hierarchical crews where a manager agent delegates subtasks to worker agents, JevProxy accelerates manager routing decisions, cutting total swarm turnaround times significantly.
Are streaming responses supported?
Yes. Full Server-Sent Events (SSE) streaming is natively supported for both reflex responses and upstream pass-through calls.
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