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DeepResearchAgent/README.md

@@ -6,7 +6,6 @@ Deep Research Agents are a new class of autonomous AI systems designed to perfor
 
 In this application, we leverage the deep research agent implementation of [langchain-ai/deepagents](https://github.com/langchain-ai/deepagents), and deploy it on the Intel platform with opea microserice.
 
-
 ## Setup Deployment Environment
 
 ```shell

DeepResearchAgent/agent_factory.py

@@ -1,14 +1,18 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
 import os
 from datetime import datetime
 from typing import Any
+
 from langchain_openai import ChatOpenAI
 
 
 def create_deepagents_research_agent() -> Any:
     from deepagents import create_deep_agent
     from research_agents.deepagents.prompts import (
-        RESEARCHER_INSTRUCTIONS,
         RESEARCH_WORKFLOW_INSTRUCTIONS,
+        RESEARCHER_INSTRUCTIONS,
         SUBAGENT_DELEGATION_INSTRUCTIONS,
     )
     from research_agents.deepagents.tools import tavily_search, think_tool
@@ -18,9 +22,11 @@ def create_deepagents_research_agent() -> Any:
     max_researcher_iterations = os.environ.get("MAX_RESEARCHER_ITERATIONS", 3)
 
     # Custom instructions (if any)
-    instructions_researcher=os.environ.get("RESEARCHER_INSTRUCTIONS", RESEARCHER_INSTRUCTIONS)
-    instructions_research_workflow=os.environ.get("RESEARCH_WORKFLOW_INSTRUCTIONS", RESEARCH_WORKFLOW_INSTRUCTIONS)
-    instructions_subagent_delegation=os.environ.get("SUBAGENT_DELEGATION_INSTRUCTIONS", SUBAGENT_DELEGATION_INSTRUCTIONS)
+    instructions_researcher = os.environ.get("RESEARCHER_INSTRUCTIONS", RESEARCHER_INSTRUCTIONS)
+    instructions_research_workflow = os.environ.get("RESEARCH_WORKFLOW_INSTRUCTIONS", RESEARCH_WORKFLOW_INSTRUCTIONS)
+    instructions_subagent_delegation = os.environ.get(
+        "SUBAGENT_DELEGATION_INSTRUCTIONS", SUBAGENT_DELEGATION_INSTRUCTIONS
+    )
 
     # Combine orchestrator instructions (RESEARCHER_INSTRUCTIONS only for sub-agents)
     INSTRUCTIONS = (
@@ -33,7 +39,7 @@ def create_deepagents_research_agent() -> Any:
             max_researcher_iterations=max_researcher_iterations,
         )
     )
-    
+
     # Get current date
     current_date = datetime.now().strftime("%Y-%m-%d")
 
@@ -50,17 +56,18 @@ def create_deepagents_research_agent() -> Any:
         openai_api_base=os.environ.get("OPENAI_BASE_URL", "http://0.0.0.0:8000/v1/"),
         openai_api_key=os.environ.get("OPENAI_API_KEY", "empty-api-key"),
         model_name=os.environ.get("LLM_MODEL_ID", "meta-llama/Llama-3.3-70B-Instruct"),
-        temperature=0.0
+        temperature=0.0,
     )
-    
+
     # Create the agent
     return create_deep_agent(
         model=model,
         tools=[tavily_search, think_tool],
         system_prompt=INSTRUCTIONS,
         subagents=[research_sub_agent],
     )
-    
+
+
 def create_agent(impl="DeepAgents") -> Any:
     if impl == "DeepAgents":
         return create_deepagents_research_agent()

DeepResearchAgent/docker_compose/intel/hpu/gaudi/compose.yaml

@@ -56,4 +56,4 @@ services:
       MAX_RESEARCHER_ITERATIONS: ${MAX_RESEARCHER_ITERATIONS:-3}
       RESEARCHER_INSTRUCTIONS: ${RESEARCHER_INSTRUCTIONS:-""}
       RESEARCH_WORKFLOW_INSTRUCTIONS: ${RESEARCH_WORKFLOW_INSTRUCTIONS:-""}
-      SUBAGENT_DELEGATION_INSTRUCTIONS: ${SUBAGENT_DELEGATION_INSTRUCTIONS:-""}
+      SUBAGENT_DELEGATION_INSTRUCTIONS: ${SUBAGENT_DELEGATION_INSTRUCTIONS:-""}

DeepResearchAgent/docker_compose/intel/hpu/gaudi/set_env.sh

@@ -101,4 +101,4 @@ export MAX_RESEARCHER_ITERATIONS="${MAX_RESEARCHER_ITERATIONS:-3}"
 # Custom instructions for agent behavior (leave empty for defaults)
 export RESEARCHER_INSTRUCTIONS=""               # Instructions for individual researchers
 export RESEARCH_WORKFLOW_INSTRUCTIONS=""        # Instructions for overall research workflow
-export SUBAGENT_DELEGATION_INSTRUCTIONS=""      # Instructions for task delegation between agents
+export SUBAGENT_DELEGATION_INSTRUCTIONS=""      # Instructions for task delegation between agents

DeepResearchAgent/research_agent.py

@@ -1,14 +1,14 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-import os
 import logging
+import os
 from typing import List, Union
-from comps import opea_microservices, register_microservice, CustomLogger
-from comps.cores.telemetry.opea_telemetry import opea_telemetry
-from pydantic import BaseModel
 
 from agent_factory import create_agent
+from comps import CustomLogger, opea_microservices, register_microservice
+from comps.cores.telemetry.opea_telemetry import opea_telemetry
+from pydantic import BaseModel
 from research_agents.deepagents.utils import format_message
 
 logger = CustomLogger(__name__)
@@ -30,10 +30,10 @@ class SimpleRequest(BaseModel):
 async def run(request: SimpleRequest):
 
     logger.debug(f"Received question: {request.question}")
-    
+
     logger.debug("Creating DeepAgents research agent...")
     agent = create_agent(impl="DeepAgents")
-    
+
     logger.debug("Invoking agent with the provided question...")
     result = agent.invoke(
         {
@@ -43,12 +43,12 @@ async def run(request: SimpleRequest):
                     "content": f"Question: {request.question}",
                 }
             ],
-        }, 
+        },
     )
     logger.debug("Agent invocation completed.")
     if os.getenv("LOGFLAG", "").lower() == "true":
         format_message(result["messages"])
-        
+
     return {"answer": result["messages"][-1].content}
 
 

DeepResearchAgent/research_agents/deepagents/README.md

@@ -1,2 +1,3 @@
 # Deep Research Agent of DeepAgents
-The code is from LangChain [DeepAgents](https://github.com/langchain-ai/deepagents-quickstarts/tree/main/deep_research).
+
+The code is from LangChain [DeepAgents](https://github.com/langchain-ai/deepagents-quickstarts/tree/main/deep_research).

DeepResearchAgent/research_agents/deepagents/prompts.py

@@ -1,3 +1,5 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
 """Prompt templates and tool descriptions for the research deepagent."""
 
 RESEARCH_WORKFLOW_INSTRUCTIONS = """# Research Workflow
@@ -68,7 +70,7 @@
 
 <Task>
 Your job is to use tools to gather information about the user's input topic.
-You can use any of the research tools provided to you to find resources that can help answer the research question. 
+You can use any of the research tools provided to you to find resources that can help answer the research question.
 You can call these tools in series or in parallel, your research is conducted in a tool-calling loop.
 </Task>
 
@@ -170,4 +172,4 @@
 ## Research Limits
 - Stop after {max_researcher_iterations} delegation rounds if you haven't found adequate sources
 - Stop when you have sufficient information to answer comprehensively
-- Bias towards focused research over exhaustive exploration"""
+- Bias towards focused research over exhaustive exploration"""

DeepResearchAgent/research_agents/deepagents/tools.py

@@ -1,3 +1,5 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
 """Research Tools.
 
 This module provides search and content processing utilities for the research agent,
@@ -39,9 +41,7 @@ def fetch_webpage_content(url: str, timeout: float = 10.0) -> str:
 def tavily_search(
     query: str,
     max_results: Annotated[int, InjectedToolArg] = 1,
-    topic: Annotated[
-        Literal["general", "news", "finance"], InjectedToolArg
-    ] = "general",
+    topic: Annotated[Literal["general", "news", "finance"], InjectedToolArg] = "general",
 ) -> str:
     """Search the web for information on a given query.
 

DeepResearchAgent/research_agents/deepagents/utils.py

@@ -1,3 +1,5 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
 """Utility functions for displaying messages and prompts in Jupyter notebooks."""
 
 import json
@@ -31,11 +33,7 @@ def format_message_content(message):
         parts.append(str(message.content))
 
     # Handle tool calls attached to the message (OpenAI format) - only if not already processed
-    if (
-        not tool_calls_processed
-        and hasattr(message, "tool_calls")
-        and message.tool_calls
-    ):
+    if not tool_calls_processed and hasattr(message, "tool_calls") and message.tool_calls:
         for tool_call in message.tool_calls:
             parts.append(f"\n🔧 Tool Call: {tool_call['name']}")
             parts.append(f"   Args: {json.dumps(tool_call['args'], indent=2)}")
@@ -76,12 +74,8 @@ def show_prompt(prompt_text: str, title: str = "Prompt", border_style: str = "bl
     # Create a formatted display of the prompt
     formatted_text = Text(prompt_text)
     formatted_text.highlight_regex(r"<[^>]+>", style="bold blue")  # Highlight XML tags
-    formatted_text.highlight_regex(
-        r"##[^#\n]+", style="bold magenta"
-    )  # Highlight headers
-    formatted_text.highlight_regex(
-        r"###[^#\n]+", style="bold cyan"
-    )  # Highlight sub-headers
+    formatted_text.highlight_regex(r"##[^#\n]+", style="bold magenta")  # Highlight headers
+    formatted_text.highlight_regex(r"###[^#\n]+", style="bold cyan")  # Highlight sub-headers
 
     # Display in a panel for better presentation
     console.print(