Merge pull request #136 from huang195/a2a-agents-by-claude-skills

3 a2a agents that were generated by claude code using skills

Author: pdettori

a2a/cheerup_agent/.dockerignore

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+__pycache__
+*.pyc
+.venv
+.git

a2a/cheerup_agent/Dockerfile

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+FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim
+ARG RELEASE_VERSION="main"
+
+WORKDIR /app
+COPY . .
+RUN uv sync --no-cache --locked --link-mode copy
+
+ENV PRODUCTION_MODE=True \
+    RELEASE_VERSION=${RELEASE_VERSION}
+
+RUN chown -R 1001:1001 /app
+USER 1001
+
+CMD ["uv", "run", "--no-sync", "server", "--host", "0.0.0.0", "--port", "8000"]

a2a/cheerup_agent/pyproject.toml

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+[project]
+name = "cheerup-agent"
+version = "0.0.1"
+description = "A cheerful conversational agent that brightens your day."
+license = { text = "Apache" }
+requires-python = ">=3.11"
+dependencies = [
+    "a2a-sdk[http-server]>=0.2.16",
+    "openai>=1.0.0",
+    "pydantic-settings>=2.8.1",
+    "uvicorn>=0.30.0",
+    "opentelemetry-exporter-otlp",
+]
+
+[project.scripts]
+server = "cheerup_agent.agent:run"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"

a2a/cheerup_agent/src/cheerup_agent/__init__.py

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+from opentelemetry.sdk.resources import Resource
+from opentelemetry import trace
+from opentelemetry.sdk.trace import TracerProvider
+from opentelemetry.sdk.trace.export import BatchSpanProcessor
+from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+
+def setup_tracer():
+    resource = Resource.create(attributes={
+        "service.name": "cheerup-agent",
+    })
+    provider = TracerProvider(resource=resource)
+    processor = BatchSpanProcessor(OTLPSpanExporter())
+    provider.add_span_processor(processor)
+    trace.set_tracer_provider(provider)
+
+setup_tracer()

a2a/cheerup_agent/src/cheerup_agent/agent.py

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+import logging
+
+import uvicorn
+from starlette.requests import Request
+from starlette.responses import JSONResponse
+from starlette.routing import Route
+from textwrap import dedent
+
+from a2a.server.agent_execution import AgentExecutor, RequestContext
+from a2a.server.apps import A2AStarletteApplication
+from a2a.server.events.event_queue import EventQueue
+from a2a.server.request_handlers import DefaultRequestHandler
+from a2a.server.tasks import InMemoryTaskStore, TaskUpdater
+from a2a.types import AgentCapabilities, AgentCard, AgentSkill, TaskState, TextPart
+from a2a.utils import new_agent_text_message, new_task
+
+from cheerup_agent.cheerup_llm import chat
+
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
+
+
+def get_agent_card(host: str, port: int):
+    """Returns the Agent Card for the Cheerup Agent."""
+    capabilities = AgentCapabilities(streaming=False)
+    skill = AgentSkill(
+        id="cheerup_companion",
+        name="Cheerup Companion",
+        description="**Cheerup Companion** - A warm, uplifting friend that brightens your day with humor, encouragement, and positive vibes.",
+        tags=["cheerful", "motivation", "humor", "wellbeing"],
+        examples=[
+            "I'm having a rough day",
+            "Tell me something to make me smile",
+            "I need a pep talk",
+            "Make me laugh!",
+        ],
+    )
+    return AgentCard(
+        name="Cheerup Companion",
+        description=dedent(
+            """\
+            A cheerful conversational companion designed to brighten your day
+            and put you in a great mood.
+
+            ## What I can do
+            - Share jokes and fun facts to make you smile
+            - Give you a pep talk when you need encouragement
+            - Celebrate your wins, big or small
+            - Be a positive, supportive friend to chat with
+            """,
+        ),
+        url=f"http://{host}:{port}/",
+        version="1.0.0",
+        default_input_modes=["text"],
+        default_output_modes=["text"],
+        capabilities=capabilities,
+        skills=[skill],
+    )
+
+
+class CheerupExecutor(AgentExecutor):
+    """Handles cheerup agent execution for A2A."""
+
+    async def execute(self, context: RequestContext, event_queue: EventQueue):
+        task = context.current_task
+        if not task:
+            task = new_task(context.message)
+            await event_queue.enqueue_event(task)
+        task_updater = TaskUpdater(event_queue, task.id, task.context_id)
+
+        user_input = context.get_user_input()
+        logger.info("Cheerup agent received: %s (context=%s)", user_input, task.context_id)
+
+        await task_updater.update_status(
+            TaskState.working,
+            new_agent_text_message(
+                "Brewing up some good vibes...",
+                task_updater.context_id,
+                task_updater.task_id,
+            ),
+        )
+
+        try:
+            reply = await chat(task.context_id, user_input)
+
+            parts = [TextPart(text=reply)]
+            await task_updater.add_artifact(parts)
+            await task_updater.update_status(
+                TaskState.input_required,
+                new_agent_text_message(
+                    reply,
+                    task_updater.context_id,
+                    task_updater.task_id,
+                ),
+            )
+        except Exception as e:
+            logger.error("Cheerup agent error: %s", e)
+            parts = [TextPart(text="Sorry, something went wrong. Please try again.")]
+            await task_updater.add_artifact(parts)
+            await task_updater.failed()
+
+    async def cancel(self, context: RequestContext, event_queue: EventQueue) -> None:
+        raise Exception("cancel not supported")
+
+
+async def health(request: Request) -> JSONResponse:
+    return JSONResponse({"status": "ok"})
+
+
+async def agent_card_compat(request: Request) -> JSONResponse:
+    """Serve agent card at /.well-known/agent-card.json for Kagenti backend compatibility."""
+    card = get_agent_card(host="0.0.0.0", port=8000)
+    return JSONResponse(card.model_dump(mode="json", exclude_none=True))
+
+
+def run():
+    """Runs the A2A Agent application."""
+    agent_card = get_agent_card(host="0.0.0.0", port=8000)
+
+    request_handler = DefaultRequestHandler(
+        agent_executor=CheerupExecutor(),
+        task_store=InMemoryTaskStore(),
+    )
+
+    server = A2AStarletteApplication(
+        agent_card=agent_card,
+        http_handler=request_handler,
+    )
+
+    app = server.build()
+
+    # Add custom routes
+    app.routes.insert(0, Route("/health", health, methods=["GET"]))
+    app.routes.insert(0, Route("/.well-known/agent-card.json", agent_card_compat, methods=["GET"]))
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)

a2a/cheerup_agent/src/cheerup_agent/cheerup_llm.py

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+import logging
+from collections import defaultdict
+
+from openai import AsyncOpenAI
+
+from cheerup_agent.configuration import Configuration
+
+logger = logging.getLogger(__name__)
+
+SYSTEM_PROMPT = (
+    "You are a warm, uplifting, and genuinely cheerful companion. Your sole "
+    "purpose is to brighten the user's day and put them in a great mood.\n\n"
+    "Guidelines:\n"
+    "- Be enthusiastic, empathetic, and positive without being fake or dismissive.\n"
+    "- If the user shares something negative, acknowledge their feelings first, "
+    "then gently help them find a silver lining or shift perspective.\n"
+    "- Share fun facts, lighthearted jokes, encouraging words, or playful "
+    "observations when appropriate.\n"
+    "- Celebrate even small wins the user mentions.\n"
+    "- Use a conversational, friendly tone — like a supportive best friend.\n"
+    "- Keep responses concise and punchy — aim for warmth, not walls of text.\n"
+    "- If the user seems down, ask what might help: a joke, a compliment, "
+    "a pep talk, or just someone to listen.\n"
+    "- Sprinkle in creative compliments and affirmations naturally.\n"
+    "- Remember context from earlier in the conversation to make it personal.\n"
+)
+
+# Conversation memory keyed by context_id
+# NOTE: In production, add eviction or size limits to prevent unbounded memory growth.
+_conversations: dict[str, list[dict[str, str]]] = defaultdict(list)
+
+
+async def chat(context_id: str, user_message: str) -> str:
+    """Send a message and get a cheerful response, maintaining conversation history."""
+    config = Configuration()
+
+    client = AsyncOpenAI(
+        base_url=config.llm_api_base,
+        api_key=config.llm_api_key,
+    )
+
+    history = _conversations[context_id]
+    history.append({"role": "user", "content": user_message})
+
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}] + history
+
+    logger.info("Sending %d messages to LLM for context %s", len(messages), context_id)
+
+    response = await client.chat.completions.create(
+        model=config.llm_model,
+        messages=messages,
+    )
+
+    assistant_message = response.choices[0].message.content
+    history.append({"role": "assistant", "content": assistant_message})
+
+    logger.info("LLM response for context %s: %s", context_id, assistant_message[:200])
+    return assistant_message

a2a/cheerup_agent/src/cheerup_agent/configuration.py

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+from pydantic_settings import BaseSettings
+
+class Configuration(BaseSettings):
+    llm_model: str = "qwen3:4b"
+    llm_api_base: str = "http://host.docker.internal:11434/v1"
+    llm_api_key: str = "dummy"