Practical overview
When it fits
Use it when you want a repository-based starting point for adapting an agent to web-oriented research and information-seeking tasks.
Who it is for
- Developers evaluating open-source research agents
- Teams exploring self-hosted AI research workflows
- Researchers working with Python and the Alibaba Tongyi ecosystem
Documented uses
- Build a self-hosted question-answering research agent
- Create a web-based literature or market research assistant
- Add information-seeking agent capabilities to internal knowledge workflows
Requirements & limits
The project is available as a public GitHub repository and is identified for Linux, macOS, Windows, CLI, server and self-hosted environments. It uses the Apache-2.0 license.
- Open-source status and Apache-2.0 licensing support inspection and adaptation.
- Python is the documented primary language.
- The project is positioned specifically around deep research and information seeking rather than a general-purpose agent.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Set up the Tongyi DeepResearch inference environment and evaluation data
Prepare a Python environment, install dependencies, configure required services, and create supported evaluation input data.
Before you start
- Python 3.10.0 is recommended.
- An isolated Conda or virtualenv environment is strongly advised.
- Access to the required service keys and endpoints listed in the configuration.
- A model-weights path and an evaluation dataset name.
Steps
Create and activate an isolated Python 3.10.0 environment, for example with Conda.
From the repository, install dependencies with the command below.
pip install -r requirements.txtCopy
.env.exampleto.env, then provide the documented search, page-reading, summarization, file-parsing, sandbox, model, dataset, and output settings.Prepare evaluation data as JSONL records containing
questionandanswer, or as a JSON array of objects with those keys.
Expected outcome
The environment, configuration file, and supported evaluation input data are prepared for the repository's inference scripts.
Guide limitations
- The documentation recommends Python 3.10.0 because other versions may cause dependency issues.
- The
answerfield is a ground-truth or reference answer used for evaluation.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 30 September 2026
- Original website title
- Alibaba-NLP/DeepResearch · Official homepage title · retained in its source language

