Swarms API、Amazon Bedrock Agents 与 Vertex AI Agent Builder 对比：2026 年该选哪个托管多智能体 API？

curl -X POST https://api.swarms.world/v1/swarm/completions \
  -H "x-api-key: $SWARMS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Market Brief Desk",
    "swarm_type": "SequentialWorkflow",
    "task": "Write a competitive brief on the shared electric scooter market in Berlin for an operator considering entry in 2027. Cover market size, three main competitors, regulatory risks and a recommendation. Keep it under 500 words.",
    "agents": [
      {
        "agent_name": "Researcher",
        "system_prompt": "You are a market researcher. List the facts, figures and competitor details relevant to the task as concise bullet points.",
        "model_name": "gpt-5.4-mini",
        "max_loops": 1
      },
      {
        "agent_name": "Analyst",
        "system_prompt": "You are a strategy analyst. Turn the research into structured findings with a recommendation and reasoning.",
        "model_name": "claude-haiku-5-5",
        "max_loops": 1
      },
      {
        "agent_name": "Writer",
        "system_prompt": "You are a business writer. Write the final brief under 500 words with the sections: Market size, Main competitors, Regulatory risks, Recommendation.",
        "model_name": "claude-sonnet-5-5",
        "max_loops": 1
      }
    ],
    "max_loops": 1
  }'

import os
from swarms_client import SwarmsClient

client = SwarmsClient(api_key=os.environ["SWARMS_API_KEY"])

result = client.swarms.run(
    name="Market Brief Desk",
    swarm_type="SequentialWorkflow",
    task=(
        "Write a competitive brief on the shared electric scooter market in Berlin "
        "for an operator considering entry in 2027. Cover market size, three main "
        "competitors, regulatory risks and a recommendation. Keep it under 500 words."
    ),
    agents=[
        {
            "agent_name": "Researcher",
            "system_prompt": "You are a market researcher. List the facts, figures and competitor details relevant to the task as concise bullet points.",
            "model_name": "gpt-5.4-mini",
            "max_loops": 1,
        },
        {
            "agent_name": "Analyst",
            "system_prompt": "You are a strategy analyst. Turn the research into structured findings with a recommendation and reasoning.",
            "model_name": "claude-haiku-5-5",
            "max_loops": 1,
        },
        {
            "agent_name": "Writer",
            "system_prompt": "You are a business writer. Write the final brief under 500 words with the sections: Market size, Main competitors, Regulatory risks, Recommendation.",
            "model_name": "claude-sonnet-5-5",
            "max_loops": 1,
        },
    ],
    max_loops=1,
)

for step in result["output"]:
    print(f"\n## {step['role']}\n{step['content']}")

print(result["usage"])

{
  "input_tokens": 5064,
  "output_tokens": 3424,
  "total_tokens": 8488,
  "total_cost": 0.058763,
  "billing_info": {
    "cost_breakdown": {
      "agent_cost": 0.03,
      "input_token_cost": 0.007076,
      "output_token_cost": 0.021687,
      "num_agents": 3
    }
  }
}

# agent.py
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands import Agent
from strands.multiagent import Swarm

researcher = Agent(
    name="researcher",
    system_prompt="You are a market researcher. Gather facts, figures and competitor details, then hand off to the analyst.",
)
analyst = Agent(
    name="analyst",
    system_prompt="You are a strategy analyst. Structure the research into findings and a recommendation, then hand off to the writer.",
)
writer = Agent(
    name="writer",
    system_prompt="You are a business writer. Write the final brief under 500 words. Do not hand off.",
)

swarm = Swarm(
    [researcher, analyst, writer],
    entry_point=researcher,
    max_handoffs=6,
    max_iterations=6,
    execution_timeout=600.0,
)

app = BedrockAgentCoreApp()


@app.entrypoint
def invoke(payload: dict) -> dict:
    result = swarm(payload["prompt"])
    return {
        "status": str(result.status),
        "path": [node.node_id for node in result.node_history],
        "output": str(result.results[result.node_history[-1].node_id].result),
    }


if __name__ == "__main__":
    app.run()

pip install bedrock-agentcore strands-agents bedrock-agentcore-starter-toolkit
agentcore configure --entrypoint agent.py
agentcore launch
agentcore invoke '{"prompt": "Write a competitive brief on the shared electric scooter market in Berlin for an operator considering entry in 2027..."}'

# market_brief/agent.py
from google.adk.agents.llm_agent import LlmAgent
from google.adk.agents.sequential_agent import SequentialAgent

researcher = LlmAgent(
    name="Researcher",
    model="gemini-2.5-flash",
    instruction=(
        "You are a market researcher. List the facts, figures and competitor "
        "details relevant to the user's request as concise bullet points."
    ),
    output_key="research",
)

analyst = LlmAgent(
    name="Analyst",
    model="gemini-2.5-flash",
    instruction=(
        "You are a strategy analyst. Using the research below, write structured "
        "findings with a recommendation and reasoning.\n\nResearch:\n{research}"
    ),
    output_key="findings",
)

writer = LlmAgent(
    name="Writer",
    model="gemini-2.5-pro",
    instruction=(
        "You are a business writer. Using the findings below, write the final "
        "brief under 500 words with the sections: Market size, Main competitors, "
        "Regulatory risks, Recommendation.\n\nFindings:\n{findings}"
    ),
)

root_agent = SequentialAgent(
    name="MarketBriefPipeline",
    sub_agents=[researcher, analyst, writer],
    description="Research, analyse and write a competitive market brief.",
)

pip install google-adk
adk run market_brief
adk deploy agent_engine --project $GOOGLE_CLOUD_PROJECT --region us-central1 \
  --staging_bucket gs://$STAGING_BUCKET market_brief

curl -H "x-api-key: $SWARMS_API_KEY" https://api.swarms.world/v1/swarms/available