Transit the implementation of DeepResearchAgent

DeepResearchAgent/Dockerfile

@@ -5,9 +5,9 @@ ARG IMAGE_REPO=opea
 ARG BASE_TAG=latest
 FROM opea/comps-base:$BASE_TAG
 
-COPY ./deep_researcher.yaml $HOME/deep_researcher.yaml
-COPY ./utils.py $HOME/utils.py
+COPY ./research_agents $HOME/research_agents
 COPY ./requirements.txt $HOME/requirements.txt
+COPY ./agent_factory.py $HOME/agent_factory.py
 COPY ./research_agent.py $HOME/research_agent.py
 
 USER root

DeepResearchAgent/README.md

@@ -4,17 +4,14 @@ Deep Research Agents are a new class of autonomous AI systems designed to perfor
 
 ## Overview
 
-In this application, we leverage the deep research agent implementation of [langchain-ai/open_deep_research](https://github.com/langchain-ai/open_deep_research), and deploy it on the Intel platform with opea microserice.
-
-![Architecture Overview](assets/img/opea-deep-research-agent.png)
+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
 
-```
-# Configure deep_researcher.yaml with your llm model served by the vllm
-
+```shell
 # get your TAVILY_API_KEY from https://app.tavily.com/
 export TAVILY_API_KEY=""
+
 # get your HuggingFace Access Token from https://huggingface.co/docs/transformers.js/en/guides/private#step-1-generating-a-user-access-token
 export HF_TOKEN=""
 
@@ -31,9 +28,8 @@ source ./set_env.sh
 
 To deploy the Deep Research Agent services, execute the docker compose up command with the appropriate arguments. For a default deployment, execute:
 
-```
+```shell
 docker compose -f docker_compose/intel/hpu/gaudi/compose.yaml up -d
-
 ```
 
 ## Validate Microservice

DeepResearchAgent/agent_factory.py

@@ -0,0 +1,75 @@
+# 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 (
+        RESEARCH_WORKFLOW_INSTRUCTIONS,
+        RESEARCHER_INSTRUCTIONS,
+        SUBAGENT_DELEGATION_INSTRUCTIONS,
+    )
+    from research_agents.deepagents.tools import tavily_search, think_tool
+
+    # Limits
+    max_concurrent_research_units = os.environ.get("MAX_CONCURRENT_RESEARCH_UNITS", 3)
+    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
+    )
+
+    # Combine orchestrator instructions (RESEARCHER_INSTRUCTIONS only for sub-agents)
+    INSTRUCTIONS = (
+        instructions_research_workflow
+        + "\n\n"
+        + "=" * 80
+        + "\n\n"
+        + instructions_subagent_delegation.format(
+            max_concurrent_research_units=max_concurrent_research_units,
+            max_researcher_iterations=max_researcher_iterations,
+        )
+    )
+
+    # Get current date
+    current_date = datetime.now().strftime("%Y-%m-%d")
+
+    # Research agent definition
+    research_sub_agent = {
+        "name": "research-agent",
+        "description": "Delegate research to the sub-agent researcher. Only give this researcher one topic at a time.",
+        "system_prompt": instructions_researcher.format(date=current_date),
+        "tools": [tavily_search, think_tool],
+    }
+
+    # LLM serving endpoint
+    model = ChatOpenAI(
+        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,
+    )
+
+    # 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()
+    else:
+        raise ValueError(f"Unknown agent implementation: {impl}")

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

@@ -8,22 +8,12 @@ x-common-environment:
   http_proxy: ${http_proxy}
   https_proxy: ${https_proxy}
 
-x-common-agent-environment:
-  &common-agent-env
-  <<: *common-env
-  HF_TOKEN: ${HF_TOKEN}
-  model: ${LLM_MODEL_ID}
-  TAVILY_API_KEY: ${TAVILY_API_KEY}
-  OPENAI_API_KEY: ${OPENAI_API_KEY}
-  OPENAI_BASE_URL: ${OPENAI_BASE_URL}
-
 services:
-
   vllm-service:
     image: opea/vllm-gaudi:1.22.0
     container_name: vllm-gaudi-server
     ports:
-      - "8000:8000"
+      - ${VLLM_PORT:-8000}:8000
     volumes:
       - ${HF_CACHE_DIR:-./data}:/data
     environment:
@@ -34,18 +24,18 @@ services:
       OMPI_MCA_btl_vader_single_copy_mechanism: none
       LLM_MODEL_ID: ${LLM_MODEL_ID}
       VLLM_TORCH_PROFILER_DIR: "/mnt"
-      VLLM_SKIP_WARMUP: true
+      VLLM_SKIP_WARMUP: ${VLLM_SKIP_WARMUP:-true}
       PT_HPU_ENABLE_LAZY_COLLECTIVES: true
     healthcheck:
-      test: ["CMD-SHELL", "curl -f http://$HOST_IP:8000/health || exit 1"]
+      test: ["CMD-SHELL", "curl -f http://${HOST_IP}:${VLLM_PORT:-8000}/health || exit 1"]
       interval: 10s
       timeout: 10s
       retries: 100
     runtime: habana
     cap_add:
       - SYS_NICE
     ipc: host
-    command: --model ${LLM_MODEL_ID} --tensor-parallel-size ${NUM_CARDS} --host 0.0.0.0 --port 8000 --max-seq-len-to-capture $MAX_LEN
+    command: --model ${LLM_MODEL_ID} --tensor-parallel-size ${NUM_CARDS} --enable-auto-tool-choice --tool-call-parser ${TOOL_CALL_PARSER} --host 0.0.0.0 --port 8000 --max-seq-len-to-capture ${MAX_LEN}
 
   deep-research-agent-server:
     image: ${REGISTRY:-opea}/deep-research-agent:${TAG:-latest}
@@ -56,4 +46,14 @@ services:
       - "8022:8022"
     ipc: host
     environment:
-      <<: *common-agent-env
+      <<: *common-env
+      HF_TOKEN: ${HF_TOKEN}
+      model: ${LLM_MODEL_ID}
+      TAVILY_API_KEY: ${TAVILY_API_KEY}
+      OPENAI_API_KEY: ${OPENAI_API_KEY}
+      OPENAI_BASE_URL: ${OPENAI_BASE_URL}
+      MAX_CONCURRENT_RESEARCH_UNITS: ${MAX_CONCURRENT_RESEARCH_UNITS:-3}
+      MAX_RESEARCHER_ITERATIONS: ${MAX_RESEARCHER_ITERATIONS:-3}
+      RESEARCHER_INSTRUCTIONS: ${RESEARCHER_INSTRUCTIONS:-""}
+      RESEARCH_WORKFLOW_INSTRUCTIONS: ${RESEARCH_WORKFLOW_INSTRUCTIONS:-""}
+      SUBAGENT_DELEGATION_INSTRUCTIONS: ${SUBAGENT_DELEGATION_INSTRUCTIONS:-""}

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

@@ -3,45 +3,102 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
-# Navigate to the parent directory and source the environment
+# ==============================================================================
+# Environment Configuration for DeepResearchAgent on Intel Gaudi HPU
+# ==============================================================================
+
+# Get the directory where this script is located
 SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &> /dev/null && pwd)"
 
+# Source the parent environment configuration file
 pushd "$SCRIPT_DIR/../../../../../" > /dev/null
 source .set_env.sh
 popd > /dev/null
 
-# Function to check if a variable is set
+# ------------------------------------------------------------------------------
+# Helper Functions
+# ------------------------------------------------------------------------------
+
+# Validates that a required environment variable is set
 check_var() {
     local var_name="$1"
     local var_value="${!var_name}"
     if [ -z "${var_value}" ]; then
         echo "Error: ${var_name} is not set. Please set ${var_name}."
-        return 1  # Return an error code but do not exit the script
+        return 1  # Return error but don't exit to allow other checks to run
     fi
 }
 
-# Check critical variables
-check_var "HF_TOKEN"
+# ------------------------------------------------------------------------------
+# Validate Required API Keys
+# ------------------------------------------------------------------------------
+
+check_var "HF_TOKEN"        # HuggingFace token for model access
+check_var "TAVILY_API_KEY"  # Tavily API key for web search functionality
+
+# ------------------------------------------------------------------------------
+# Network Configuration
+# ------------------------------------------------------------------------------
+
+# Detect the primary IP address of the host machine
 export ip_address=$(hostname -I | awk '{print $1}')
+export HOST_IP=${ip_address}
 
-# VLLM configuration
+# Update proxy settings to include the host IP
+export no_proxy=${no_proxy},${ip_address}
+export http_proxy=${http_proxy}
+export https_proxy=${https_proxy}
+
+# ------------------------------------------------------------------------------
+# vLLM Service Configuration
+# ------------------------------------------------------------------------------
+
+# Port where vLLM service will be accessible
 export VLLM_PORT="${VLLM_PORT:-8000}"
-export VLLM_VOLUME="${VLLM_VOLUME:-/data2/huggingface}"
-export VLLM_IMAGE="${VLLM_IMAGE:-opea/vllm-gaudi:latest}"
+
+# ------------------------------------------------------------------------------
+# Language Model Configuration
+# ------------------------------------------------------------------------------
+
+# LLM model to use for the Deep Research Agent
+# See supported models and tool call parsers at:
+# https://docs.vllm.ai/en/stable/features/tool_calling/#automatic-function-calling
 export LLM_MODEL_ID="${LLM_MODEL_ID:-meta-llama/Llama-3.3-70B-Instruct}"
+
+# Parser for handling function/tool calls (must match the model)
+export TOOL_CALL_PARSER="${TOOL_CALL_PARSER:-llama3_json}"
+
+# Maximum sequence length for model context (131072 = ~128K tokens)
 export MAX_LEN="${MAX_LEN:-131072}"
+
+# Number of Gaudi accelerator cards to use
 export NUM_CARDS="${NUM_CARDS:-4}"
+
+# Directory for caching HuggingFace models
 export HF_CACHE_DIR="${HF_CACHE_DIR:-"./data"}"
-export OPENAI_BASE_URL="http://${ip_address}:8000/v1"
-export OPENAI_API_KEY="empty"
-export no_proxy=${no_proxy}
-export http_proxy=${http_proxy}
-export https_proxy=${https_proxy}
 
+# OpenAI-compatible API endpoint URL for vLLM
+export OPENAI_BASE_URL="http://${ip_address}:${VLLM_PORT}/v1"
+
+# ------------------------------------------------------------------------------
+# API Keys and Authentication
+# ------------------------------------------------------------------------------
+
+export HF_TOKEN="${HF_TOKEN}"              # HuggingFace authentication token
+export OPENAI_API_KEY="empty-api-key"      # Placeholder for vLLM compatibility
+export TAVILY_API_KEY="${TAVILY_API_KEY}"  # Tavily search API key
+
+# ------------------------------------------------------------------------------
+# Deep Research Agent Configuration
+# ------------------------------------------------------------------------------
+
+# Maximum number of research units that can run concurrently
+export MAX_CONCURRENT_RESEARCH_UNITS="${MAX_CONCURRENT_RESEARCH_UNITS:-3}"
 
-# Hugging Face API token
-export HF_TOKEN="${HF_TOKEN}"
+# Maximum iterations per researcher before stopping
+export MAX_RESEARCHER_ITERATIONS="${MAX_RESEARCHER_ITERATIONS:-3}"
 
-# API keys
-check_var "TAVILY_API_KEY"
-export TAVILY_API_KEY="${TAVILY_API_KEY}"
+# 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

DeepResearchAgent/requirements.in

@@ -0,0 +1,8 @@
+deepagents
+httpx
+langchain_openai
+langchain-tavily
+langgraph-cli[inmem]
+markdownify
+rich
+tavily-python