Choosing single‑agent orchestration is optimal when the task flow is linear, latency‑critical, and the coordination overhead of multiple agents outweighs the benefits of parallelism. Multi‑agent architectures shine for branching, heterogeneous skill sets, and when you need dynamic role assignment.
Decision Checklist
1. Task topology – Is the workflow a simple sequence (single) or a DAG with parallel branches (multi)?
2. Latency budget – Do you need sub‑second response times? Single‑agent avoids inter‑process RPC latency.
3. Skill diversity – Are you mixing LLM reasoning, database queries, and image analysis? Multi‑agent lets each agent specialize.
4. Scalability – Will load spike require horizontal scaling? Multi‑agent containers can be autoscaled independently.
5. State sharing – Does the process require a shared mutable context? Single‑agent keeps state in‑process; multi‑agent needs a datastore (e.g., Redis, Milvus).
6. Tooling constraints – Does your stack support orchestration frameworks? LangChain’s SequentialChain vs. CrewAI’s Crew or AutoGen’s GroupChat.
Comparison Table
| Criterion | Single‑Agent | Multi‑Agent |
|-----------|--------------|------------|
| Latency | Low (≤ 50 ms) | Higher (≈ 100‑300 ms) |
| Parallelism | None | ✅ via async tasks |
| Fault isolation | Low | High (per‑agent restart) |
| Code complexity | Simple | Higher (routing, message schemas) |
| Resource usage | Consolidated | Distributed |
Example: Switching from LangChain SequentialChain to CrewAI Crew
# LangChain single‑agent
from langchain.chains import SequentialChain
chain = SequentialChain(chains=[chain_a, chain_b, chain_c], input_variables=["input"], output_variables=["result"])
# CrewAI multi‑agent equivalent
from crewai import Crew, Agent
agent_a = Agent(...)
agent_b = Agent(...)
agent_c = Agent(...)
crew = Crew(agents=[agent_a, agent_b, agent_c], manager_llm=llm, verbose=True)
crew.kickoff(task="process input")