modify implementation

Author: ks6088ts

docs/references.md

@@ -13,6 +13,8 @@
 - [Combine control flow and state updates with Command](https://langchain-ai.github.io/langgraph/how-tos/graph-api/#combine-control-flow-and-state-updates-with-command)
 - [Command: a new tool for building multi-agent architectures in LangGraph](https://www.youtube.com/watch?v=6BJDKf90L9A)
 - [masamasa59/genai-agent-advanced-book > chapter6](https://github.com/masamasa59/genai-agent-advanced-book/blob/main/chapter6/arxiv_researcher/agent/paper_search_agent.py)
+- [langchain-ai/deepagents](https://github.com/langchain-ai/deepagents)
+- [Custom UI for Deep Agents](https://github.com/langchain-ai/deep-agents-ui)
 
 ### Sample Codes
 

langgraph.json

@@ -4,6 +4,7 @@
     "chat_with_tools_agent": "template_langgraph.agents.chat_with_tools_agent.agent:graph",
     "demo_agents_parallel_rag_agent": "template_langgraph.agents.demo_agents.parallel_rag_agent.agent:graph",
     "demo_agents_multi_agent": "template_langgraph.agents.demo_agents.multi_agent:graph",
+    "demo_agents_research_deep_agent": "template_langgraph.agents.demo_agents.research_deep_agent:graph",
     "demo_agents_weather_agent": "template_langgraph.agents.demo_agents.weather_agent:graph",
     "image_classifier_agent": "template_langgraph.agents.image_classifier_agent.agent:graph",
     "issue_formatter_agent": "template_langgraph.agents.issue_formatter_agent.agent:graph",

template_langgraph/agents/demo_agents/deep_agent_core/__init__.py

@@ -1,4 +1,4 @@
-from deepagents.graph import create_deep_agent
-from deepagents.model import get_default_model
-from deepagents.state import DeepAgentState
-from deepagents.sub_agent import SubAgent
+# ruff: noqa
+from template_langgraph.agents.demo_agents.deep_agent_core.graph import create_deep_agent
+from template_langgraph.agents.demo_agents.deep_agent_core.state import DeepAgentState
+from template_langgraph.agents.demo_agents.deep_agent_core.sub_agent import SubAgent

template_langgraph/agents/demo_agents/deep_agent_core/graph.py

@@ -1,15 +1,23 @@
+# ruff: noqa: E501
 from collections.abc import Callable, Sequence
 from typing import Any, TypeVar
 
-from deepagents.model import get_default_model
-from deepagents.state import DeepAgentState
-from deepagents.sub_agent import SubAgent, _create_task_tool
-from deepagents.tools import edit_file, ls, read_file, write_file, write_todos
 from langchain_core.language_models import LanguageModelLike
 from langchain_core.tools import BaseTool
 from langgraph.prebuilt import create_react_agent
 from langgraph.types import Checkpointer
 
+from template_langgraph.agents.demo_agents.deep_agent_core.state import DeepAgentState
+from template_langgraph.agents.demo_agents.deep_agent_core.sub_agent import SubAgent, _create_task_tool
+from template_langgraph.agents.demo_agents.deep_agent_core.tools import (
+    edit_file,
+    ls,
+    read_file,
+    write_file,
+    write_todos,
+)
+from template_langgraph.llms.azure_openais import AzureOpenAiWrapper
+
 StateSchema = TypeVar("StateSchema", bound=DeepAgentState)
 StateSchemaType = type[StateSchema]
 
@@ -51,14 +59,14 @@ def create_deep_agent(
                 - `description` (used by the main agent to decide whether to call the sub agent)
                 - `prompt` (used as the system prompt in the subagent)
                 - (optional) `tools`
-        state_schema: The schema of the deep agent. Should subclass from DeepAgentState
+        state_schema: The schema of the deep agent. Should subclass from template_langgraph.agents.demo_agents.deep_agent_coretate
         config_schema: The schema of the deep agent.
         checkpointer: Optional checkpointer for persisting agent state between runs.
     """
     prompt = instructions + base_prompt
     built_in_tools = [write_todos, write_file, read_file, ls, edit_file]
     if model is None:
-        model = get_default_model()
+        model = AzureOpenAiWrapper().chat_model
     state_schema = state_schema or DeepAgentState
     task_tool = _create_task_tool(list(tools) + built_in_tools, instructions, subagents or [], model, state_schema)
     all_tools = built_in_tools + list(tools) + [task_tool]

template_langgraph/agents/demo_agents/deep_agent_core/model.py

@@ -1,3 +1,4 @@
+# ruff: noqa: E501
 WRITE_TODOS_DESCRIPTION = """Use this tool to create and manage a structured task list for your current work session. This helps you track progress, organize complex tasks, and demonstrate thoroughness to the user.
 It also helps the user understand the progress of the task and overall progress of their requests.
 

template_langgraph/agents/demo_agents/deep_agent_core/prompts.py

@@ -11,7 +11,7 @@ class Todo(TypedDict):
     status: Literal["pending", "in_progress", "completed"]
 
 
-def file_reducer(l, r):
+def file_reducer(l, r):  # noqa
     if l is None:
         return r
     elif r is None:

template_langgraph/agents/demo_agents/deep_agent_core/state.py

@@ -1,14 +1,18 @@
 from typing import Annotated, Any, NotRequired
 
-from deepagents.prompts import TASK_DESCRIPTION_PREFIX, TASK_DESCRIPTION_SUFFIX
-from deepagents.state import DeepAgentState
 from langchain.chat_models import init_chat_model
 from langchain_core.messages import ToolMessage
 from langchain_core.tools import BaseTool, InjectedToolCallId, tool
 from langgraph.prebuilt import InjectedState, create_react_agent
 from langgraph.types import Command
 from typing_extensions import TypedDict
 
+from template_langgraph.agents.demo_agents.deep_agent_core.prompts import (
+    TASK_DESCRIPTION_PREFIX,
+    TASK_DESCRIPTION_SUFFIX,
+)
+from template_langgraph.agents.demo_agents.deep_agent_core.state import DeepAgentState
+
 
 class SubAgent(TypedDict):
     name: str

template_langgraph/agents/demo_agents/deep_agent_core/sub_agent.py

@@ -1,16 +1,18 @@
+# ruff: noqa: E501
 from typing import Annotated
 
-from deepagents.prompts import (
-    EDIT_DESCRIPTION,
-    TOOL_DESCRIPTION,
-    WRITE_TODOS_DESCRIPTION,
-)
-from deepagents.state import DeepAgentState, Todo
 from langchain_core.messages import ToolMessage
 from langchain_core.tools import InjectedToolCallId, tool
 from langgraph.prebuilt import InjectedState
 from langgraph.types import Command
 
+from template_langgraph.agents.demo_agents.deep_agent_core.prompts import (
+    EDIT_DESCRIPTION,
+    TOOL_DESCRIPTION,
+    WRITE_TODOS_DESCRIPTION,
+)
+from template_langgraph.agents.demo_agents.deep_agent_core.state import DeepAgentState, Todo
+
 
 @tool(description=WRITE_TODOS_DESCRIPTION)
 def write_todos(todos: list[Todo], tool_call_id: Annotated[str, InjectedToolCallId]) -> Command:

template_langgraph/agents/demo_agents/deep_agent_core/tools.py

@@ -1,29 +1,6 @@
-import os
-from typing import Literal
-
-from deepagents import create_deep_agent
-from tavily import TavilyClient
-
-# It's best practice to initialize the client once and reuse it.
-tavily_client = TavilyClient(api_key=os.environ["TAVILY_API_KEY"])
-
-
-# Search tool to use to do research
-def internet_search(
-    query: str,
-    max_results: int = 5,
-    topic: Literal["general", "news", "finance"] = "general",
-    include_raw_content: bool = False,
-):
-    """Run a web search"""
-    search_docs = tavily_client.search(
-        query,
-        max_results=max_results,
-        include_raw_content=include_raw_content,
-        topic=topic,
-    )
-    return search_docs
-
+# ruff: noqa: E501
+from template_langgraph.agents.demo_agents.deep_agent_core import create_deep_agent
+from template_langgraph.tools.common import get_default_tools
 
 sub_research_prompt = """You are a dedicated researcher. Your job is to conduct research based on the users questions.
 
@@ -35,7 +12,6 @@ def internet_search(
     "name": "research-agent",
     "description": "Used to research more in depth questions. Only give this researcher one topic at a time. Do not pass multiple sub questions to this researcher. Instead, you should break down a large topic into the necessary components, and then call multiple research agents in parallel, one for each sub question.",
     "prompt": sub_research_prompt,
-    "tools": ["internet_search"],
 }
 
 sub_critique_prompt = """You are a dedicated editor. You are being tasked to critique a report.
@@ -152,14 +128,14 @@ def internet_search(
 
 You have access to a few tools.
 
-## `internet_search`
+## `search_ai_search`
 
-Use this to run an internet search for a given query. You can specify the number of results, the topic, and whether raw content should be included.
+Use this to run an Azure AI Search for getting KABUTO related information. You can specify the number of results, the topic, and whether raw content should be included.
 """
 
 # Create the agent
-agent = create_deep_agent(
-    [internet_search],
-    research_instructions,
+graph = create_deep_agent(
+    tools=get_default_tools(),
+    instructions=research_instructions,
     subagents=[critique_sub_agent, research_sub_agent],
 ).with_config({"recursion_limit": 1000})