commit

langgraph.json

@@ -2,7 +2,7 @@
   "dependencies": ["."],
   "graphs": {
     "agent": "./src/agent/graph.py:graph",
-    "my_agent": "./src/agent/my_graph.py:graph"
+    "my_agent": "./src/agent/my_graph.py:make_graph"
   },
   "env": ".env",
   "image_distro": "wolfi"

src/agent/my_graph.py

@@ -1,9 +1,11 @@
-from typing import Annotated
+from typing import Annotated, Any
 
 from langchain.chat_models import init_chat_model
 from langchain_core.tools import tool
+from langchain_mcp_adapters.client import MultiServerMCPClient
 from langchain_tavily import TavilySearch
 from langchain_core.messages import BaseMessage
+from langgraph.graph.state import CompiledStateGraph
 from typing_extensions import TypedDict
 
 from langgraph.checkpoint.memory import InMemorySaver
@@ -12,6 +14,8 @@ from langgraph.graph.message import add_messages
 from langgraph.prebuilt import ToolNode, tools_condition
 from langchain_community.chat_models.tongyi import ChatTongyi
 
+from src.mcp.mcp_tools import get_client
+
 llm = ChatTongyi(
     model="qwen-max",   # 此处以qwen-max为例，您可按需更换模型名称。模型列表：https://help.aliyun.com/zh/model-studio/getting-started/models
     streaming=True,
@@ -19,35 +23,45 @@ llm = ChatTongyi(
 )
 
 
-class State(TypedDict):
-    messages: Annotated[list, add_messages]
-
-graph_builder = StateGraph(State)
-
-# tool = TavilySearch(max_results=2)
-
 @tool
 def get_wheather(location: str) -> str:
     """输入城市查询天气"""
     return f"The weather in {location} is 20 degrees Celsius."
 
-tools = [get_wheather]
-llm_with_tools = llm.bind_tools(tools)
+class State(TypedDict):
+    messages: Annotated[list, add_messages]
 
-def chatbot(state: State):
-    return {"messages": [llm_with_tools.invoke(state["messages"])]}
 
-graph_builder.add_node("chatbot", chatbot)
 
-tool_node = ToolNode(tools=[tool])
-graph_builder.add_node("tools", tool_node)
+async def make_graph() -> CompiledStateGraph[Any, Any, Any, Any]:
+    graph_builder = StateGraph(State)
 
-graph_builder.add_conditional_edges(
-    "chatbot",
-    tools_condition,
-)
-graph_builder.add_edge("tools", "chatbot")
-graph_builder.set_entry_point("chatbot")
-# memory = InMemorySaver()
-# graph = graph_builder.compile(checkpointer=memory)
-graph = graph_builder.compile()
+    # tool = TavilySearch(max_results=2)
+
+    client = get_client()
+
+    mcp_tools = await client.get_tools()
+
+    tools = [get_wheather]
+    tools.extend(mcp_tools)
+    llm_with_tools = llm.bind_tools(tools)
+
+    def chatbot(state: State):
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+    graph_builder.add_node("chatbot", chatbot)
+
+    tool_node = ToolNode(tools=[tool])
+    graph_builder.add_node("tools", tool_node)
+
+    graph_builder.add_conditional_edges(
+        "chatbot",
+        tools_condition,
+    )
+    graph_builder.add_edge("tools", "chatbot")
+    graph_builder.set_entry_point("chatbot")
+    # memory = InMemorySaver()
+    # graph = graph_builder.compile(checkpointer=memory)
+    graph = graph_builder.compile()
+
+    return graph

src/mcp/mcp_servers.json

@@ -0,0 +1,9 @@
+{
+  "math": {
+    "command": "python",
+    "args": [
+      "/home/kexsh/src/knightutils/test/mcp/math_server.py"
+    ],
+    "transport": "stdio"
+  }
+}

src/mcp/mcp_tools.py

@@ -0,0 +1,9 @@
+from langchain_mcp_adapters.client import MultiServerMCPClient
+import json
+
+def get_client() -> MultiServerMCPClient:
+    mcp_tools = json.load(open("mcp_tools.json"))
+
+    client = MultiServerMCPClient(mcp_tools)
+
+    return client