Gemini Terminal Agent

import asyncio
import os

from mcp_agent.app import MCPApp
from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm_openai import OpenAIAugmentedLLM

app = MCPApp(name="hello_world_agent")

async def example_usage():
    async with app.run() as mcp_agent_app:
        logger = mcp_agent_app.logger
        # This agent can read the filesystem or fetch URLs
        finder_agent = Agent(
            name="finder",
            instruction="""You can read local files or fetch URLs.
                Return the requested information when asked.""",
            server_names=["fetch", "filesystem"], # MCP servers this Agent can use
        )

        async with finder_agent:
            # Automatically initializes the MCP servers and adds their tools for LLM use
            tools = await finder_agent.list_tools()
            logger.info(f"Tools available:", data=tools)

            # Attach an OpenAI LLM to the agent (defaults to GPT-4o)
            llm = await finder_agent.attach_llm(OpenAIAugmentedLLM)

            # This will perform a file lookup and read using the filesystem server
            result = await llm.generate_str(
                message="Show me what's in README.md verbatim"
            )
            logger.info(f"README.md contents: {result}")

            # Uses the fetch server to fetch the content from URL
            result = await llm.generate_str(
                message="Print the first two paragraphs from https://www.anthropic.com/research/building-effective-agents"
            )
            logger.info(f"Blog intro: {result}")

            # Multi-turn interactions by default
            result = await llm.generate_str("Summarize that in a 128-char tweet")
            logger.info(f"Tweet: {result}")

if __name__ == "__main__":
    asyncio.run(example_usage())

execution_engine: asyncio
logger:
  transports: [console] # You can use [file, console] for both
  level: debug
  path: "logs/mcp-agent.jsonl" # Used for file transport
  # For dynamic log filenames:
  # path_settings:
  #   path_pattern: "logs/mcp-agent-{unique_id}.jsonl"
  #   unique_id: "timestamp"  # Or "session_id"
  #   timestamp_format: "%Y%m%d_%H%M%S"

mcp:
  servers:
    fetch:
      command: "uvx"
      args: ["mcp-server-fetch"]
    filesystem:
      command: "npx"
      args:
        [
          "-y",
          "@modelcontextprotocol/server-filesystem",
          "<add_your_directories>",
        ]

openai:
  # Secrets (API keys, etc.) are stored in an mcp_agent.secrets.yaml file which can be gitignored
  default_model: gpt-4o

from mcp_agent.agents.agent import Agent
from mcp_agent.workflows.llm.augmented_llm_anthropic import AnthropicAugmentedLLM

finder_agent = Agent(
    name="finder",
    instruction="You are an agent with filesystem + fetch access. Return the requested file or URL contents.",
    server_names=["fetch", "filesystem"],
)

async with finder_agent:
   llm = await finder_agent.attach_llm(AnthropicAugmentedLLM)

   result = await llm.generate_str(
      message="Print the first 2 paragraphs of https://www.anthropic.com/research/building-effective-agents",
      # Can override model, tokens and other defaults
   )
   logger.info(f"Result: {result}")

   # Multi-turn conversation
   result = await llm.generate_str(
      message="Summarize those paragraphs in a 128 character tweet",
   )
   logger.info(f"Result: {result}")

proofreader = Agent(name="proofreader", instruction="Review grammar...")
fact_checker = Agent(name="fact_checker", instruction="Check factual consistency...")
style_enforcer = Agent(name="style_enforcer", instruction="Enforce style guidelines...")

grader = Agent(name="grader", instruction="Combine feedback into a structured report.")

parallel = ParallelLLM(
    fan_in_agent=grader,
    fan_out_agents=[proofreader, fact_checker, style_enforcer],
    llm_factory=OpenAIAugmentedLLM,
)

result = await parallel.generate_str("Student short story submission: ...", RequestParams(model="gpt4-o"))

def print_hello_world:
     print("Hello, world!")

finder_agent = Agent(name="finder", server_names=["fetch", "filesystem"])
writer_agent = Agent(name="writer", server_names=["filesystem"])

llm = OpenAIAugmentedLLM()
router = LLMRouter(
    llm=llm,
    agents=[finder_agent, writer_agent],
    functions=[print_hello_world],
)

results = await router.route( # Also available: route_to_agent, route_to_server
    request="Find and print the contents of README.md verbatim",
    top_k=1
)
chosen_agent = results[0].result
async with chosen_agent:
    ...

optimizer = Agent(name="cover_letter_writer", server_names=["fetch"], instruction="Generate a cover letter ...")
evaluator = Agent(name="critiquer", instruction="Evaluate clarity, specificity, relevance...")

llm = EvaluatorOptimizerLLM(
    optimizer=optimizer,
    evaluator=evaluator,
    llm_factory=OpenAIAugmentedLLM,
    min_rating=QualityRating.EXCELLENT, # Keep iterating until the minimum quality bar is reached
)

result = await eo_llm.generate_str("Write a job cover letter for an AI framework developer role at LastMile AI.")
print("Final refined cover letter:", result)

finder_agent = Agent(name="finder", server_names=["fetch", "filesystem"])
writer_agent = Agent(name="writer", server_names=["filesystem"])
proofreader = Agent(name="proofreader", ...)
fact_checker = Agent(name="fact_checker", ...)
style_enforcer = Agent(name="style_enforcer", instructions="Use APA style guide from ...", server_names=["fetch"])

orchestrator = Orchestrator(
    llm_factory=AnthropicAugmentedLLM,
    available_agents=[finder_agent, writer_agent, proofreader, fact_checker, style_enforcer],
)

task = "Load short_story.md, evaluate it, produce a graded_report.md with multiple feedback aspects."
result = await orchestrator.generate_str(task, RequestParams(model="gpt-4o"))
print(result)

triage_agent = SwarmAgent(...)
flight_mod_agent = SwarmAgent(...)
lost_baggage_agent = SwarmAgent(...)

# The triage agent decides whether to route to flight_mod_agent or lost_baggage_agent
swarm = AnthropicSwarm(agent=triage_agent, context_variables={...})

test_input = "My bag was not delivered!"
result = await swarm.generate_str(test_input)
print("Result:", result)

optimizer = Agent(name="plan_optimizer", server_names=[...], instruction="Generate a plan given an objective ...")
evaluator = Agent(name="plan_evaluator", instruction="Evaluate logic, ordering and precision of plan......")

planner_llm = EvaluatorOptimizerLLM(
    optimizer=optimizer,
    evaluator=evaluator,
    llm_factory=OpenAIAugmentedLLM,
    min_rating=QualityRating.EXCELLENT,
)

orchestrator = Orchestrator(
    llm_factory=AnthropicAugmentedLLM,
    available_agents=[finder_agent, writer_agent, proofreader, fact_checker, style_enforcer],
    planner=planner_llm # It's that simple
)

...

Swarm の例で実際の動作を確認できます。

from mcp_agent.human_input.handler import console_input_callback

lost_baggage = SwarmAgent(
    name="Lost baggage traversal",
    instruction=lambda context_variables: f"""
        {
        FLY_AIR_AGENT_PROMPT.format(
            customer_context=context_variables.get("customer_context", "None"),
            flight_context=context_variables.get("flight_context", "None"),
        )
    }\n Lost baggage policy: policies/lost_baggage_policy.md""",
    functions=[
        escalate_to_agent,
        initiate_baggage_search,
        transfer_to_triage,
        case_resolved,
    ],
    server_names=["fetch", "filesystem"],
    human_input_callback=console_input_callback, # Request input from the console
)

from mcp_agent.context import get_current_context
from mcp_agent.mcp.mcp_connection_manager import MCPConnectionManager

context = get_current_context()
connection_manager = MCPConnectionManager(context.server_registry)

async with connection_manager:
fetch_client = await connection_manager.get_server("fetch") # Initializes fetch server
result = fetch_client.list_tool()
fetch_client2 = await connection_manager.get_server("fetch") # Reuses same server connection

# All servers managed by connection manager are automatically disconnected/shut down

from mcp_agent.mcp.mcp_aggregator import MCPAggregator

aggregator = await MCPAggregator.create(server_names=["fetch", "filesystem"])

async with aggregator:
   # combined list of tools exposed by 'fetch' and 'filesystem' servers
   tools = await aggregator.list_tools()

   # namespacing -- invokes the 'fetch' server to call the 'fetch' tool
   fetch_result = await aggregator.call_tool(name="fetch-fetch", arguments={"url": "https://www.anthropic.com/research/building-effective-agents"})

   # no namespacing -- first server in the aggregator exposing that tool wins
   read_file_result = await aggregator.call_tool(name="read_file", arguments={})