diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/README.md b/samples/python/agents/a2a_multi_agent_on_single_port/README.md new file mode 100644 index 00000000..8226500c --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/README.md @@ -0,0 +1,10 @@ +# Example: Using a2a-python SDK Without an LLM Framework + +This repository demonstrates how to set up and use the [a2a-python SDK](https://github.com/google/a2a-python) to create a simple server and client, without relying on any agent framework. + +## Overview + +- **A2A (Agent-to-Agent):** A protocol and SDK for communication with AI Agents. +- **This Example:** Shows how to support multiple A2AStarletteApplication instances or AgentExecutor implementations on a single port. + +More information refer to [a2a-mcp-without-framework](https://github.com/a2aproject/a2a-samples/blob/main/samples/python/agents/a2a-mcp-without-framework/README.md) diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/pyproject.toml b/samples/python/agents/a2a_multi_agent_on_single_port/pyproject.toml new file mode 100644 index 00000000..297512e4 --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/pyproject.toml @@ -0,0 +1,24 @@ +[project] +name = "no-llm-framework" +version = "0.1.0" +description = "Use A2A without any agent framework" +readme = "README.md" +authors = [{ name = "prem", email = "prem.ch@ku.th" }] +requires-python = ">=3.13" +dependencies = [ + "a2a-sdk>=0.3.0", + "asyncclick>=8.1.8", + "colorama>=0.4.6", + "fastmcp>=2.3.4", + "google-genai", + "jinja2>=3.1.6", + "rich>=14.0.0", + "starlette>=0.47.2" +] +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[project.scripts] +a2a-server = "no_llm_framework.server.__main__:main" +a2a-client = "no_llm_framework.client.__main__:main" diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/client/__init__.py b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/client/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/client/__main__.py b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/client/__main__.py new file mode 100644 index 00000000..b8763a45 --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/client/__main__.py @@ -0,0 +1,50 @@ +import asyncio +from typing import Literal + + +import asyncclick as click +import colorama +from no_llm_framework.client.agent import Agent + + +@click.command() +@click.option('--host', 'host', default='localhost') +@click.option('--port', 'port', default=9999) +@click.option('--mode', 'mode', default='streaming') +@click.option('--question', 'question', required=True) +async def a_main( + host: str, + port: int, + mode: Literal['completion', 'streaming'], + question: str, +): + """Main function to run the A2A Repo Agent client. + + Args: + host (str): The host address to run the server on. + port (int): The port number to run the server on. + mode (Literal['completion', 'streaming']): The mode to run the server on. + question (str): The question to ask the Agent. + """ # noqa: E501 + agent_index = 1 + agent = Agent( + mode='stream', + token_stream_callback=None, + agent_urls=[f'http://{host}:{port}/{agent_index}'], + ) + async for chunk in agent.stream(question): + if chunk.startswith(' Generator[str]: + """Stream LLM response. + + Args: + prompt (str): The prompt to send to the LLM. + + Returns: + Generator[str, None, None]: A generator of the LLM response. + """ + client = genai.Client(vertexai=False, api_key=GOOGLE_API_KEY) + for chunk in client.models.generate_content_stream( + model='gemini-2.5-flash-lite', + contents=prompt, + ): + yield chunk.text + + +class Agent: + """Agent for interacting with the Google Gemini LLM in different modes.""" + + def __init__( + self, + mode: Literal['complete', 'stream'] = 'stream', + token_stream_callback: Callable[[str], None] | None = None, + agent_urls: list[str] | None = None, + agent_prompt: str | None = None, + ): + self.mode = mode + self.token_stream_callback = token_stream_callback + self.agent_urls = agent_urls + self.agents_registry: dict[str, AgentCard] = {} + + async def get_agents(self) -> tuple[dict[str, AgentCard], str]: + """Retrieve agent cards from all agent URLs and render the agent prompt. + + Returns: + tuple[dict[str, AgentCard], str]: A dictionary mapping agent names to AgentCard objects, and the rendered agent prompt string. + """ + async with httpx.AsyncClient() as httpx_client: + card_resolvers = [ + A2ACardResolver(httpx_client, url) for url in self.agent_urls + ] + agent_cards = await asyncio.gather( + *[ + card_resolver.get_agent_card() + for card_resolver in card_resolvers + ] + ) + agents_registry = { + agent_card.name: agent_card for agent_card in agent_cards + } + agent_prompt = agents_template.render(agent_cards=agent_cards) + return agents_registry, agent_prompt + + def call_llm(self, prompt: str) -> str: + """Call the LLM with the given prompt and return the response as a string or generator. + + Args: + prompt (str): The prompt to send to the LLM. + + Returns: + str or Generator[str]: The LLM response as a string or generator, depending on mode. + """ + if self.mode == 'complete': + return stream_llm(prompt) + + result = '' + for chunk in stream_llm(prompt): + result += chunk + return result + + async def decide( + self, + question: str, + agents_prompt: str, + called_agents: list[dict] | None = None, + ) -> Generator[str, None]: + """Decide which agent(s) to use to answer the question. + + Args: + question (str): The question to answer. + agents_prompt (str): The prompt describing available agents. + called_agents (list[dict] | None): Previously called agents and their answers. + + Returns: + Generator[str, None]: The LLM's response as a generator of strings. + """ + if called_agents: + call_agent_prompt = agent_answer_template.render( + called_agents=called_agents + ) + else: + call_agent_prompt = '' + prompt = decide_template.render( + question=question, + agent_prompt=agents_prompt, + call_agent_prompt=call_agent_prompt, + ) + return self.call_llm(prompt) + + def extract_agents(self, response: str) -> list[dict]: + """Extract the agents from the response. + + Args: + response (str): The response from the LLM. + """ + pattern = r'```json\n(.*?)\n```' + match = re.search(pattern, response, re.DOTALL) + if match: + return json.loads(match.group(1)) + return [] + + async def send_message_to_an_agent( + self, agent_card: AgentCard, message: str + ): + """Send a message to a specific agent and yield the streaming response. + + Args: + agent_card (AgentCard): The agent to send the message to. + message (str): The message to send. + + Yields: + str: The streaming response from the agent. + """ + async with httpx.AsyncClient() as httpx_client: + client = A2AClient(httpx_client, agent_card=agent_card) + message = MessageSendParams( + message=Message( + role=Role.user, + parts=[Part(TextPart(text=message))], + message_id=uuid4().hex, + task_id=uuid4().hex, + ) + ) + + streaming_request = SendStreamingMessageRequest( + id=str(uuid4().hex), params=message + ) + async for chunk in client.send_message_streaming(streaming_request): + if isinstance( + chunk.root, SendStreamingMessageSuccessResponse + ) and isinstance(chunk.root.result, TaskStatusUpdateEvent): + message = chunk.root.result.status.message + if message: + yield message.parts[0].root.text + + async def stream(self, question: str): + """Stream the process of answering a question, possibly involving multiple agents. + + Args: + question (str): The question to answer. + + Yields: + str: Streaming output, including agent responses and intermediate steps. + """ + agent_answers: list[dict] = [] + for _ in range(3): + agents_registry, agent_prompt = await self.get_agents() + response = '' + for chunk in await self.decide( + question, agent_prompt, agent_answers + ): + response += chunk + if self.token_stream_callback: + self.token_stream_callback(chunk) + yield chunk + + agents = self.extract_agents(response) + if agents: + for agent in agents: + agent_response = '' + agent_card = agents_registry[agent['name']] + yield f'\n' + async for chunk in self.send_message_to_an_agent( + agent_card, agent['prompt'] + ): + agent_response += chunk + if self.token_stream_callback: + self.token_stream_callback(chunk) + yield chunk + yield '\n' + match = re.search( + r'(.*?)', agent_response, re.DOTALL + ) + answer = match.group(1).strip() if match else agent_response + agent_answers.append( + { + 'name': agent['name'], + 'prompt': agent['prompt'], + 'answer': answer, + } + ) + else: + return + + +if __name__ == '__main__': + import asyncio + + import colorama + + async def main(): + """Main function to run the A2A Repo Agent client.""" + agent = Agent( + mode='stream', + token_stream_callback=None, + agent_urls=['http://localhost:9999/'], + ) + + async for chunk in agent.stream('What is A2A protocol?'): + if chunk.startswith(' + +Note: +- You can leave the selected agents empty if you think none of the agents are relevant to the question or given contexts are enough to answer the question. +- You can select multiple agents if you think multiple agents are relevant to the question. +- You `agent_name` must be one of the agent names in the agents list, and you must spell it correctly. +- If there is no need to call any agent, Please give your answer by continuing the following format: + + + + + diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/__init__.py b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/__main__.py b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/__main__.py new file mode 100644 index 00000000..2736afab --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/__main__.py @@ -0,0 +1,220 @@ +import click +import uvicorn +from a2a.server.apps.jsonrpc import A2AStarletteApplication +from a2a.server.request_handlers.default_request_handler import ( + DefaultRequestHandler, +) +from a2a.server.tasks.inmemory_task_store import InMemoryTaskStore +from a2a.types import ( + AgentCapabilities, + AgentCard +) +from starlette.applications import Starlette +from starlette.routing import Route, Router +from starlette.requests import Request +from typing import Any, List +from a2a.server.request_handlers.jsonrpc_handler import JSONRPCHandler + +from no_llm_framework.server.agent_executor import CoreAgentExecutor +from starlette.responses import JSONResponse + + +class DynamicContextBuilder: + def __init__(self, original_builder, agent_index): + self.original_builder = original_builder + self.agent_index = agent_index + + async def build(self, request: Request): + if self.original_builder and hasattr(self.original_builder, "build"): + context = await self.original_builder.build(request) + else: + context = None + + if context: + setattr(context, "agent_index", self.agent_index) + return context + + +class DatabaseA2AStarletteApplication(A2AStarletteApplication): + def __init__(self, http_handler: DefaultRequestHandler): + temp_card = AgentCard( + name="Temporary Agent", + description="Initial agent card", + url="http://localhost:9999/a2a/default", + version="1.0.0", + defaultInputModes=["text"], + defaultOutputModes=["text"], + capabilities=AgentCapabilities( + inputModes=["text"], outputModes=["text"], streaming=True + ), + skills=[], + examples=[], + ) + + self.task_store = InMemoryTaskStore() + + self.agent_card = temp_card + self.http_handler = http_handler + self.handler = JSONRPCHandler( + agent_card=self.agent_card, request_handler=self.http_handler + ) + + self.agent_router = Router() + + self.agent_router.add_route( + "/.well-known/agent.json", self._handle_dynamic_agent_card, methods=["GET"] + ) + self.agent_router.add_route("/", self._handle_dynamic_request, methods=["POST"]) + + def _get_agent_card_from_config(self, agent_index: str, request: Request) -> AgentCard: + + agent_data = { + "name": "default", + "description": "default", + "version": "1.0.0", + "examples": "What is A2A protocol?" + } + + return AgentCard( + name=agent_data["name"], + description=agent_data["description"], + url=f"http://{request.url.hostname}:{request.url.port}/a2a/{agent_index}", + version=agent_data["version"], + defaultInputModes=["text"], + defaultOutputModes=["text"], + capabilities=AgentCapabilities( + inputModes=["text"], + outputModes=["text"], + streaming=True, + ), + skills=[], + examples=agent_data["examples"] or [], + ) + + def routes( + self, + rpc_url: str = "/", + extended_agent_card_url: str = "/agent/authenticatedExtendedCard", + **kwargs, + ) -> List[Route]: + routes = [ + Route(rpc_url, self._handle_requests, methods=["POST"]), + + Route( + "/a2a/{agent_index}/.well-known/agent.json", + self._handle_dynamic_agent_card, + methods=["GET"], + ), + + Route("/a2a/{agent_index}", self._handle_dynamic_request, methods=["POST"]), + ] + + if self.agent_card and self.agent_card.supportsAuthenticatedExtendedCard: + routes.append( + Route( + extended_agent_card_url, + self._handle_get_authenticated_extended_agent_card, + methods=["GET"], + ) + ) + + return routes + + async def _handle_dynamic_agent_card(self, request: Request) -> JSONResponse: + + agent_index = request.path_params["agent_index"] + try: + card = self._get_agent_card_from_config(agent_index, request) + return JSONResponse(card.dict()) + except Exception as e: + return JSONResponse( + {"error": f"Agent not found: {str(e)}"}, status_code=404 + ) + + async def _handle_dynamic_request(self, request: Request) -> JSONResponse: + + original_card = None + original_context_builder = None + + try: + agent_index = request.path_params.get("agent_index") + self.task_store = InMemoryTaskStore() + self.http_handler.agent_executor = CoreAgentExecutor( + agent_index=agent_index + ) + + if not agent_index: + return JSONResponse( + {"error": "Missing agent_index in path parameters"}, status_code=400 + ) + + original_card = self.agent_card + original_context_builder = getattr(self, "_context_builder", None) + + try: + self.agent_card = self._get_agent_card_from_config(agent_index, request) + self.handler.agent_card.name = self.agent_card.name + self.handler.agent_card.description = self.agent_card.description + except Exception as e: + return JSONResponse( + {"error": f"Failed to get agent card: {str(e)}"}, status_code=404 + ) + + self._context_builder = DynamicContextBuilder( + original_context_builder, agent_index + ) + + response = await self._handle_requests(request) + return response + except Exception as e: + import traceback + + traceback.print_exc() + return JSONResponse( + { + "error": "Internal server error", + "details": str(e), + "type": type(e).__name__, + }, + status_code=500, + ) + finally: + + if original_card is not None: + self.agent_card = original_card + if original_context_builder is not None: + self._context_builder = original_context_builder + + def build( + self, + agent_card_url: str = "/.well-known/agent.json", + rpc_url: str = "/", + extended_agent_card_url: str = "/agent/authenticatedExtendedCard", + **kwargs: Any, + ) -> Starlette: + app = Starlette(**kwargs) + self.add_routes_to_app( + app, + agent_card_url=agent_card_url, + rpc_url=rpc_url, + extended_agent_card_url=extended_agent_card_url, + ) + return app + + +@click.command() +@click.option("--host", default="localhost") +@click.option("--port", default=9999) +def main(host: str, port: int): + + task_store = InMemoryTaskStore() + request_handler = DefaultRequestHandler( + agent_executor=CoreAgentExecutor(1), + task_store=task_store, + ) + server = DatabaseA2AStarletteApplication(request_handler) + uvicorn.run(server.build(), host=host, port=port) + + +if __name__ == "__main__": + main() diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/agent.py b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/agent.py new file mode 100644 index 00000000..446ad649 --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/agent.py @@ -0,0 +1,187 @@ +import asyncio +import json +import re + +from collections.abc import AsyncGenerator, Callable, Generator +from pathlib import Path +from typing import Literal + +from google import genai +from jinja2 import Template +from mcp.types import CallToolResult + +from no_llm_framework.server.constant import GOOGLE_API_KEY +from no_llm_framework.server.mcp import call_mcp_tool, get_mcp_tool_prompt + + +dir_path = Path(__file__).parent + +with Path(dir_path / 'decide.jinja').open('r') as f: + decide_template = Template(f.read()) + +with Path(dir_path / 'tool.jinja').open('r') as f: + tool_template = Template(f.read()) + +with Path(dir_path / 'called_tools_history.jinja').open('r') as f: + called_tools_history_template = Template(f.read()) + + +def stream_llm(prompt: str) -> Generator[str, None]: + """Stream LLM response. + + Args: + prompt (str): The prompt to send to the LLM. + + Returns: + Generator[str, None, None]: A generator of the LLM response. + """ + client = genai.Client(vertexai=False, api_key=GOOGLE_API_KEY) + for chunk in client.models.generate_content_stream( + model='gemini-2.5-flash-lite', + contents=prompt, + ): + yield chunk.text + + +class Agent: + """Agent for interacting with the Google Gemini LLM in different modes.""" + + def __init__( + self, + mode: Literal['complete', 'stream'] = 'stream', + token_stream_callback: Callable[[str], None] | None = None, + mcp_url: str | None = None, + ): + self.mode = mode + self.token_stream_callback = token_stream_callback + self.mcp_url = mcp_url + + def call_llm(self, prompt: str) -> Generator[str, None]: + """Call the LLM with the given prompt and return a generator of responses. + + Args: + prompt (str): The prompt to send to the LLM. + + Returns: + Generator[str, None]: A generator yielding the LLM's response. + """ + return stream_llm(prompt) + + async def decide( + self, question: str, called_tools: list[dict] | None = None + ) -> Generator[str, None]: + """Decide which tool to use to answer the question. + + Args: + question (str): The question to answer. + called_tools (list[dict]): The tools that have been called. + """ + if self.mcp_url is None: + return self.call_llm(question) + tool_prompt = await get_mcp_tool_prompt(self.mcp_url) + if called_tools: + called_tools_prompt = called_tools_history_template.render( + called_tools=called_tools + ) + else: + called_tools_prompt = '' + + prompt = decide_template.render( + question=question, + tool_prompt=tool_prompt, + called_tools=called_tools_prompt, + ) + return self.call_llm(prompt) + + def extract_tools(self, response: str) -> list[dict]: + """Extract the tools from the response. + + Args: + response (str): The response from the LLM. + """ + pattern = r'```json\n(.*?)\n```' + match = re.search(pattern, response, re.DOTALL) + if match: + return json.loads(match.group(1)) + return [] + + async def call_tool(self, tools: list[dict]) -> list[CallToolResult]: + """Call the tool. + + Args: + tools (list[dict]): The tools to call. + """ + return await asyncio.gather( + *[ + call_mcp_tool(self.mcp_url, tool['name'], tool['arguments']) + for tool in tools + ] + ) + + async def stream(self, question: str) -> AsyncGenerator[str]: + """Stream the process of answering a question, possibly involving tool calls. + + Args: + question (str): The question to answer. + + Yields: + dict: Streaming output, including intermediate steps and final result. + """ + called_tools = [] + for i in range(10): + yield { + 'is_task_complete': False, + 'require_user_input': False, + 'content': f'Step {i}', + } + + response = '' + for chunk in await self.decide(question, called_tools): + response += chunk + yield { + 'is_task_complete': False, + 'require_user_input': False, + 'content': chunk, + } + tools = self.extract_tools(response) + if not tools: + break + results = await self.call_tool(tools) + + called_tools += [ + { + 'tool': tool['name'], + 'arguments': tool['arguments'], + 'isError': result.isError, + 'result': result.content[0].text, + } + for tool, result in zip(tools, results, strict=True) + ] + called_tools_history = called_tools_history_template.render( + called_tools=called_tools, question=question + ) + yield { + 'is_task_complete': False, + 'require_user_input': False, + 'content': called_tools_history, + } + + yield { + 'is_task_complete': True, + 'require_user_input': False, + 'content': 'Task completed', + } + + +if __name__ == '__main__': + agent = Agent( + token_stream_callback=lambda token: print(token, end='', flush=True), + mcp_url='https://gitmcp.io/google/A2A', + ) + + async def main(): + """Main function.""" + async for chunk in agent.stream('What is A2A Protocol?'): + print(chunk) + + asyncio.run(main()) diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/agent_executor.py b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/agent_executor.py new file mode 100644 index 00000000..409e3df7 --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/agent_executor.py @@ -0,0 +1,104 @@ +from a2a.server.agent_execution import AgentExecutor, RequestContext +from a2a.server.events import EventQueue +from a2a.types import ( + TaskArtifactUpdateEvent, + TaskState, + TaskStatus, + TaskStatusUpdateEvent, +) +from a2a.utils import new_agent_text_message, new_task, new_text_artifact +from typing import override + +from src.no_llm_framework.server.agent import Agent + +class CoreAgentExecutor(AgentExecutor): + """Test AgentProxy Implementation.""" + + def __init__(self, agent_index: int = None): + + mcp_url_dict = {"1": "https://gitmcp.io/google/A2A", "2": "https://gitmcp.io/google/A2A"} + + self.agent = Agent( + mode='stream', + token_stream_callback=print, + mcp_url=[mcp_url_dict[str(agent_index)]], + ) + + @override + async def execute( + self, + context: RequestContext, + event_queue: EventQueue, + ) -> None: + query = context.get_user_input() + task = context.current_task + + if not context.message: + raise Exception('No message provided') + + if not task: + task = new_task(context.message) + await event_queue.enqueue_event(task) + + async for event in self.agent.stream(query): + if event['is_task_complete']: + await event_queue.enqueue_event( + TaskArtifactUpdateEvent( + append=False, + context_id=task.context_id, + task_id=task.id, + last_chunk=True, + artifact=new_text_artifact( + name='current_result', + description='Result of request to agent.', + text=event['content'], + ), + ) + ) + await event_queue.enqueue_event( + TaskStatusUpdateEvent( + status=TaskStatus(state=TaskState.completed), + final=True, + context_id=task.context_id, + task_id=task.id, + ) + ) + elif event['require_user_input']: + await event_queue.enqueue_event( + TaskStatusUpdateEvent( + status=TaskStatus( + state=TaskState.input_required, + message=new_agent_text_message( + event['content'], + task.context_id, + task.id, + ), + ), + final=True, + context_id=task.context_id, + task_id=task.id, + ) + ) + else: + await event_queue.enqueue_event( + TaskStatusUpdateEvent( + append=True, + status=TaskStatus( + state=TaskState.working, + message=new_agent_text_message( + event['content'], + task.context_id, + task.id, + ), + ), + final=False, + context_id=task.context_id, + task_id=task.id, + ) + ) + + @override + async def cancel( + self, context: RequestContext, event_queue: EventQueue + ) -> None: + raise Exception('cancel not supported') diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/called_tools_history.jinja b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/called_tools_history.jinja new file mode 100644 index 00000000..99d05361 --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/called_tools_history.jinja @@ -0,0 +1,5 @@ +Previous tools have been called. {% for tool in called_tools %} +- Tool: {{ tool.name }} +- Arguments: {{ tool.arguments }} +- Result: {{ tool.result }} +{% endfor %} \ No newline at end of file diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/constant.py b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/constant.py new file mode 100644 index 00000000..a51e2c82 --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/constant.py @@ -0,0 +1,3 @@ +import os + +GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY') diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/decide.jinja b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/decide.jinja new file mode 100644 index 00000000..590cd376 --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/decide.jinja @@ -0,0 +1,43 @@ +You duty is to decide which tool to use to answer the question. +The question is: + +{{ question }} + +{{ called_tools }} + +{{ tool_prompt }} + +You must answer in the following format: + + + +Thoughts: +- ... +- ... +- ... + + + +```json +[ + { + "name": "tool_name", + "arguments": { + "argument_name": "argument_value" + } + }, + ... +] +``` + + +Note: +- You can leave the selected tools empty if you think none of the tools are relevant to the question or given contexts are enough to answer the question. +- You can select multiple tools if you think multiple tools are relevant to the question. +- You `tool_name` must be one of the tool names in the tools list, and you must spell it correctly. +- If there is no need to call any tool, Please give your answer by continuing the following format: + + + + + diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/mcp.py b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/mcp.py new file mode 100644 index 00000000..3ed77c20 --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/mcp.py @@ -0,0 +1,65 @@ +import asyncio +from pathlib import Path + +from jinja2 import Template +from mcp.client.session import ClientSession +from mcp.client.sse import sse_client +from mcp.types import CallToolResult, TextContent + +dir_path = Path(__file__).parent + +with Path(dir_path / 'tool.jinja').open('r') as f: + template = Template(f.read()) + + +async def get_mcp_tool_prompt(url: str) -> str: + """Get the MCP tool prompt for a given URL. + + Args: + url (str): The URL of the MCP tool. + + Returns: + str: The MCP tool prompt. + """ + async with ( + sse_client(url) as (read, write), + ClientSession(read, write) as session, + ): + await session.initialize() + + resources = await session.list_tools() + return template.render(tools=resources.tools) + + +async def call_mcp_tool( + url: str, tool_name: str, arguments: dict | None = None +) -> CallToolResult: + """Call an MCP tool with the given URL and tool name. + + Args: + url (str): The URL of the MCP tool. + tool_name (str): The name of the tool to call. + arguments (dict | None, optional): The arguments to pass to the tool. Defaults to None. + + Returns: + CallToolResult: The result of the tool call. + """ # noqa: E501 + async with ( + sse_client( + url=url, + ) as (read, write), + ClientSession(read, write) as session, + ): + await session.initialize() + + return await session.call_tool(tool_name, arguments=arguments) + + +if __name__ == '__main__': + print(asyncio.run(get_mcp_tool_prompt('https://gitmcp.io/google/A2A'))) + result = asyncio.run( + call_mcp_tool('https://gitmcp.io/google/A2A', 'fetch_A2A_documentation') + ) + for content in result.content: + if isinstance(content, TextContent): + print(content.text) diff --git a/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/tool.jinja b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/tool.jinja new file mode 100644 index 00000000..d56bfb4b --- /dev/null +++ b/samples/python/agents/a2a_multi_agent_on_single_port/src/no_llm_framework/server/tool.jinja @@ -0,0 +1,4 @@ +Tools{% for tool in 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