DeepResearchAgent

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Introduction

DeepResearchAgent is a hierarchical multi-agent system designed not only for deep research tasks but also for general-purpose task solving. The framework leverages a top-level planning agent to coordinate multiple specialized lower-level agents, enabling automated task decomposition and efficient execution across diverse and complex domains.

Architecture

The system adopts a two-layer structure:

1. Top-Level Planning Agent

• Responsible for understanding, decomposing, and planning the overall workflow for a given task.
• Breaks down tasks into manageable sub-tasks and assigns them to appropriate lower-level agents.
• Dynamically coordinates the collaboration among agents to ensure smooth task completion.

2. Specialized Lower-Level Agents

• Deep Analyzer

  • Performs in-depth analysis of input information, extracting key insights and potential requirements.
  • Supports analysis of various data types, including text and structured data.

• Deep Researcher

  • Conducts thorough research on specified topics or questions, retrieving and synthesizing high-quality information.
  • Capable of generating research reports or knowledge summaries automatically.

• Browser Use

  • Automates browser operations, supporting web search, information extraction, and data collection tasks.
  • Assists the Deep Researcher in acquiring up-to-date information from the internet.

• General Tool Calling Agent

  • Provides a general-purpose interface for invoking various tools and APIs.
  • Supports function calling, allowing the agent to execute specific tasks or retrieve information from external services.

Features

• Hierarchical agent collaboration for complex and dynamic task scenarios
• Extensible agent system, allowing easy integration of additional specialized agents
• Automated information analysis, research, and web interaction capabilities
• Secure Python code execution environment for tools, featuring configurable import controls, restricted built-ins, attribute access limitations, and resource limits. (See PythonInterpreterTool Sandboxing for details).
• Support for asynchronous operations, enabling efficient handling of multiple tasks and agents
• Support for local and remote model inference, including OpenAI, Anthropic, Google LLMs, and local Qwen models via vLLM
• Support for image and video generation tools based on the Imagen and Veo3 models, respectively

Image and Video Examples:

Updates

• 2025.07.08: Add the video generator tool, which can generate a video based on the input text and/or image. The video generator tool is based on the Veo3 model.
• 2025.07.08: Add the image generator tool, which can generate images based on the input text. The image generator tool is based on the Imagen model.
• 2025.07.07: Due to the limited flexibility of TOML configuration files, we have switched to using the config format supported by mmengine.
• 2025.06.20: Add the support for the mcp (Both the local mcp and remote mcp).
• 2025.06.17: Update technical report https://arxiv.org/pdf/2506.12508.
• 2025.06.01: Update the browser-use to 0.1.48.
• 2025.05.30: Convert the sub agent to a function call. Planning agent can now be gpt-4.1 or gemini-2.5-pro.
• 2025.05.27: Support OpenAI, Anthropic, Google LLMs, and local Qwen models (via vLLM, see details in Usage).

TODO List

• Asynchronous feature completed
• Image Generator Tool completed
• Video Generator Tool completed
• MCP in progress
• AI4Research Agent to be developed
• Novel Writing Agent to be developed

Installation

Prepare Environment

# poetry install environment
conda create -n dra python=3.11
conda activate dra
make install

# (Optional) You can also use requirements.txt
conda create -n dra python=3.11
conda activate dra
make install-requirements

# playwright install if needed
pip install playwright
playwright install chromium --with-deps --no-shell

Set Up .env

Please refer to the .env.template file and create a .env file in the root directory of the project. This file is used to configure API keys and other environment variables.

Refer to the following instructions to obtain the necessary google gemini-2.5-pro API key and set it in the .env file:

• https://aistudio.google.com/app/apikey
• https://cloud.google.com/docs/authentication/application-default-credentials?hl=zh-cn

brew install --cask google-cloud-sdk
gcloud init
gcloud auth application-default login

Usage

Main Example

A simple example to demonstrate the usage of the DeepResearchAgent framework.

python main.py

Run Single Agent Example

A simple example to demonstrate the usage of a single agent, such as a general tool calling agent.

python examples/run_general.py

RUN GAIA Evaluation Example

# Download GAIA
mkdir data && cd data
git clone https://huggingface.co/datasets/gaia-benchmark/GAIA

# Run
python examples/run_gaia.py

Experiments

We evaluated our agent on the GAIA validation set and achieved state-of-the-art performance on May 10th.

Questions

1. About Qwen Models

Our framework now supports:

• qwen2.5-7b-instruct
• qwen2.5-14b-instruct
• qwen2.5-32b-instruct

Update your config:

model_id = "qwen2.5-7b-instruct"

2. Browser Use

If problems occur, reinstall:

pip install "browser-use[memory]"==0.1.48
pip install playwright
playwright install chromium --with-deps --no-shell

3. Sub-Agent Calling

Function-calling is now supported natively by GPT-4.1 / Gemini 2.5 Pro. Claude-3.7-Sonnet is also recommended.

4. Use vllm for local models

We provide huggingface as a shortcut to the local model. Also provide vllm as a way to start services so that parallel acceleration can be provided.

Step 1: Launch the vLLM Inference Service

nohup bash -c 'CUDA_VISIBLE_DEVICES=0,1 python -m vllm.entrypoints.openai.api_server \
  --model /input0/Qwen3-32B \
  --served-model-name Qwen \
  --host 0.0.0.0 \
  --port 8000 \
  --max-num-seqs 16 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes \
  --tensor_parallel_size 2' > vllm_qwen.log 2>&1 &

Update .env:

QWEN_API_BASE=http://localhost:8000/v1
QWEN_API_KEY="abc"

Step 2: Launch the Agent Service

python main.py

Example command:

Use deep_researcher_agent to search the latest papers on the topic of 'AI Agent' and then summarize it.

Acknowledgement

DeepResearchAgent is primarily inspired by the architecture of smolagents. The following improvements have been made:

• The codebase of smolagents has been modularized for better structure and organization.
• The original synchronous framework has been refactored into an asynchronous one.
• The multi-agent setup process has been optimized to make it more user-friendly and efficient.

We would like to express our gratitude to the following open source projects, which have greatly contributed to the development of this work:

• smolagents - A lightweight agent framework.
• OpenManus - An asynchronous agent framework.
• browser-use - An AI-powered browser automation tool.
• crawl4ai - A web crawling library for AI applications.
• markitdown - A tool for converting files to Markdown format.

We sincerely appreciate the efforts of all contributors and maintainers of these projects for their commitment to advancing AI technologies and making them available to the wider community.

Contribution

Contributions and suggestions are welcome! Feel free to open issues or submit pull requests.

Cite

@misc{zhang2025agentorchestrahierarchicalmultiagentframework,
      title={AgentOrchestra: A Hierarchical Multi-Agent Framework for General-Purpose Task Solving}, 
      author={Wentao Zhang, Ce Cui, Yilei Zhao, Rui Hu, Yang Liu, Yahui Zhou, Bo An},
      year={2025},
      eprint={2506.12508},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.12508}, 
}

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🇨🇳 中文版说明文档

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