feat: add remote agent

docs/docs/agent.md

@@ -15,6 +15,28 @@ Agent中主要包括如下属性：
 | knowledgebase | Vector database | 知识库，后端通常为一个向量数据库（Vector database），能够检索 |
 | tracers | list | 追踪器列表，能够定义不同的追踪方式，并在Agent执行完毕后对整体Tracing信息保存至本地 |
 
+## A2A智能体
+
+当你的智能体部署到云上后，可以在本地被初始化为一个Remote Agent，也就是能够通过A2A协议来访问的智能体，初始化方法如下：
+
+```python
+remote_agent = RemoteVeAgent(
+    name="a2a_agent",
+    url="..." # <--- url from cloud platform
+)
+
+short_term_memory = ShortTermMemory()
+runner = Runner(
+    agent=remote_agent,
+    short_term_memory=short_term_memory
+    )
+
+res = await runner.run(
+    messages="...",
+    session_id="sample_session"
+    )
+```
+
 ## 运行
 
 在生产环境中，我们推荐您使用`Runner`来进行多租户服务：
@@ -29,6 +51,11 @@ USER_ID = ""
 SESSION_ID = ""
 
 agent = Agent()
-runner = Runner(agent=agent, short_term_memory=ShortTermMemory())
+
+runner = Runner(
+    agent=agent,
+    short_term_memory=ShortTermMemory()
+    )
+
 response = await runner.run(messages=prompt, session_id=session_id)
 ```

veadk/a2a/remote_ve_agent.py

@@ -0,0 +1,21 @@
+import json
+
+import requests
+from a2a.types import AgentCard
+from google.adk.agents.remote_a2a_agent import RemoteA2aAgent
+
+AGENT_CARD_WELL_KNOWN_PATH = "/.well-known/agent-card.json"
+
+
+class RemoteVeAgent(RemoteA2aAgent):
+    def __init__(self, name: str, url: str):
+        agent_card_dict = requests.get(url + AGENT_CARD_WELL_KNOWN_PATH).json()
+        agent_card_dict["url"] = url
+
+        agent_card_json_str = json.dumps(agent_card_dict, ensure_ascii=False, indent=2)
+
+        agent_card_object = AgentCard.model_validate_json(str(agent_card_json_str))
+
+        super().__init__(
+            name=name, description="weather reporter", agent_card=agent_card_object
+        )

veadk/runner.py

@@ -19,6 +19,7 @@
 from google.genai import types
 from google.genai.types import Blob
 
+from veadk.a2a.remote_ve_agent import RemoteVeAgent
 from veadk.agent import Agent
 from veadk.evaluation import EvalSetRecorder
 from veadk.memory.short_term_memory import ShortTermMemory
@@ -41,7 +42,7 @@
 class Runner:
     def __init__(
         self,
-        agent: Agent,
+        agent: Agent | RemoteVeAgent,
         short_term_memory: ShortTermMemory,
         app_name: str = "veadk_default_app",
         user_id: str = "veadk_default_user",
@@ -56,7 +57,10 @@ def __init__(
         self.short_term_memory = short_term_memory
         self.session_service = short_term_memory.session_service
 
-        self.long_term_memory = self.agent.long_term_memory
+        if isinstance(self.agent, Agent):
+            self.long_term_memory = self.agent.long_term_memory
+        else:
+            self.long_term_memory = None
 
         # maintain a in-memory runner for fast inference
         self.runner = ADKRunner(
@@ -67,13 +71,6 @@ def __init__(
         )
 
     def _convert_messages(self, messages) -> list:
-        # RunnerMessage = Union[
-        #     str,  # single turn text-based prompt
-        #     list[str],  # multiple turn text-based prompt
-        #     dict,  # single turn prompt with media
-        #     list[dict],  # multiple turn prompt with media
-        #     list[dict | str],  # multiple turn prompt with media and text-based prompt
-        # ]
         if isinstance(messages, str):
             messages = [types.Content(role="user", parts=[types.Part(text=messages)])]
         elif isinstance(messages, MediaMessage):
@@ -159,7 +156,8 @@ async def run(
             final_output = await self._run(session_id, message, stream)
 
         # try to save tracing file
-        self.save_tracing_file(session_id)
+        if isinstance(self.agent, Agent):
+            self.save_tracing_file(session_id)
 
         return final_output