Merge pull request #22 from ks6088ts-labs/feature/issue-21_supervisor-agent

add an example supervisor agent

Author: ks6088ts

.pre-commit-config.yaml

@@ -13,4 +13,4 @@ repos:
     args: [ --fix ]
   - id: ruff-format
     types_or: [ python, pyi ]
-exclude: 'generated/.*|artifacts/.*'
+exclude: 'generated/.*|artifacts/.*|.*\.json$'

docs/index.md

@@ -43,6 +43,8 @@ uv run python -m template_langgraph.tasks.run_issue_formatter_agent
 
 - [Build a custom workflow](https://langchain-ai.github.io/langgraph/concepts/why-langgraph/)
 - [LangGraphの(LLMなし)Human-in-the-loopを試してみた](https://qiita.com/te_yama/items/db38201af60dec76384d)
+- [🤖 LangGraph Multi-Agent Supervisor](https://github.com/langchain-ai/langgraph-supervisor-py)
+- [Software Design誌「実践LLMアプリケーション開発」第24回サンプルコード](https://github.com/mahm/softwaredesign-llm-application/tree/main/24)
 
 ### Sample Codes
 

langgraph.json

@@ -3,10 +3,11 @@
     "."
   ],
   "graphs": {
+    "supervisor_agent": "template_langgraph.agents.supervisor_agent:graph",
     "chat_with_tools_agent": "template_langgraph.agents.chat_with_tools_agent.agent:graph",
     "kabuto_helpdesk_agent": "template_langgraph.agents.kabuto_helpdesk_agent:graph",
     "issue_formatter_agent": "template_langgraph.agents.issue_formatter_agent.agent:graph",
     "task_decomposer_agent": "template_langgraph.agents.task_decomposer_agent.agent:graph"
   },
   "env": ".env"
-}
+}

pyproject.toml

@@ -10,6 +10,7 @@ dependencies = [
     "langchain-openai>=0.3.28",
     "langchain-text-splitters>=0.3.9",
     "langgraph>=0.6.2",
+    "langgraph-supervisor>=0.0.29",
     "openai>=1.98.0",
     "pydantic-settings>=2.9.1",
     "pypdf>=5.9.0",

template_langgraph/agents/chat_with_tools_agent/agent.py

@@ -76,7 +76,9 @@ def create_graph(self):
         workflow.add_edge("finalize", END)
 
         # Compile the graph
-        return workflow.compile()
+        return workflow.compile(
+            name=ChatWithToolsAgent.__name__,
+        )
 
     def initialize(self, state: AgentState) -> AgentState:
         """Initialize the agent with the given state."""

template_langgraph/agents/issue_formatter_agent/agent.py

@@ -24,7 +24,9 @@ def create_graph(self):
         workflow.add_edge("analyze", END)
 
         # Compile the graph
-        return workflow.compile()
+        return workflow.compile(
+            name=IssueFormatterAgent.__name__,
+        )
 
     def analyze(self, state: AgentState) -> AgentState:
         """Analyze the issue and extract relevant information."""

template_langgraph/agents/supervisor_agent.py

@@ -0,0 +1,36 @@
+from langgraph_supervisor import create_supervisor
+
+from template_langgraph.agents.chat_with_tools_agent.agent import graph as chat_with_tools_agent_graph
+from template_langgraph.agents.issue_formatter_agent.agent import graph as issue_formatter_agent_graph
+from template_langgraph.agents.task_decomposer_agent.agent import graph as task_decomposer_agent_graph
+from template_langgraph.llms.azure_openais import AzureOpenAiWrapper
+from template_langgraph.loggers import get_logger
+
+logger = get_logger(__name__)
+
+PROMPT = """
+KABUTO に関する質問に答えるために、必要な情報を収集し適切な回答を提供します。
+- 過去の FAQ やドキュメントを参照して、質問に対する答えを見つける必要がある場合は Chat with Tools Agent を使用します。
+- 質問の内容を整理し、適切な形式で回答するために Issue Formatter Agent を使用します。
+- 質問が複雑であったり追加の情報が必要な場合は、 Task Decomposer Agent を使用してタスクを分解し、順次処理します。
+- 質問が明確でない場合は、追加の情報を求めるための質問を行います。
+- 質問が単純な場合は、直接回答を提供します。
+"""
+
+
+class SupervisorAgent:
+    def __init__(self):
+        self.agent = create_supervisor(
+            agents=[
+                chat_with_tools_agent_graph,
+                issue_formatter_agent_graph,
+                task_decomposer_agent_graph,
+            ],
+            model=AzureOpenAiWrapper().chat_model,
+            prompt=PROMPT,
+            debug=True,
+            supervisor_name=SupervisorAgent.__name__,
+        )
+
+
+graph = SupervisorAgent().agent

template_langgraph/agents/task_decomposer_agent/agent.py

@@ -32,7 +32,9 @@ def create_graph(self):
                 "end": END,
             },
         )
-        return workflow.compile()
+        return workflow.compile(
+            name=TaskDecomposerAgent.__name__,
+        )
 
     def chat(self, state: AgentState) -> AgentState:
         """Chat with tools using the state."""