Alibaba-NLP/DeepResearch
DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
Usage guide
DeepResearch is an open-source project around agent, alibaba, artificial-intelligence with 19,567 GitHub stars. This guide focuses on when to use it, how to install it, how to run the first example, and what to verify before adopting it.
Key features
- Implemented mainly in Python, useful for judging integration effort in a similar stack.
- GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
- The project has a homepage, so cross-check docs, examples, and release information beyond GitHub.
Best for
- Evaluating DeepResearch for Python AI workflows.
- Comparing a GitHub project with 19,567 stars and current repository activity.
Pros
- DeepResearch has visible GitHub traction with 19,567 stars. Topics: agent, alibaba, artificial-intelligence.
- The project provides an external homepage for deeper evaluation.
Cons
- Production fit still depends on documentation depth, issue activity, and release cadence.
- License review should confirm the Apache-2.0 terms fit your use case.
Production readiness
DeepResearch should be validated with its README, release history, open issues, and integration requirements before production use.
License risk
Apache-2.0 is reported by GitHub; review the repository license before redistribution or commercial use.
DeepResearch architecture preview
DeepResearch's main path starts at the entry surface, runs through Agent orchestration runtime, combines LLM / model client, Files / repository context, GitHub / WeChat, and returns Assistant response / action result.
Entry
Web / product entry
Users start from a web UI, hosted product surface, or browser-based workflow.
https://tongyi-agent.github.io/blog/introducing-tongyi-deep-research/
Runtime
Agent orchestration runtime
The orchestration layer plans tasks, calls tools, manages context, and decides the next action.
agent workflow
Model
LLM / model client
The project connects its core runtime to local models or hosted AI APIs when model inference is required.
model signal
Context
Files / repository context
Context comes from Files / repository context, which constrains what the model or runtime can use.
Files / repository context
Tools
GitHub / WeChat
Tool adapters let the runtime act outside the model through GitHub / WeChat.
GitHub, WeChat
Output
Assistant response / action result
The final result is a response, action, or task completion returned through the active channel.
assistant output
Install tutorial
Before you install
- Python runtime and an isolated virtual environment
- A clean working directory for the first test run
Check the runtime environment
DeepResearch depends on a Python-style environment. Use venv, conda, or a container to keep dependencies isolated.
Get the project files
Start from the official repository or package so the first run matches the documented behavior.
$ git clone https://github.com/Alibaba-NLP/DeepResearch.gitInstall or build dependencies
Run the next setup command detected from the project documentation.
$ conda create -n react_infer_env python=3.10.0Adoption guidance and sources
Practical use cases
Agent workflow prototype
Use it to validate task decomposition, tool calling, memory, tool permissions, and result review loops.
Tongyi Deep Research, the Leading Open-source Deep Research Agent
This is one of the documented reasons to evaluate DeepResearch before choosing a stack.
Focus area: agent
This is one of the documented reasons to evaluate DeepResearch before choosing a stack.
AI Agents project comparison
Compare DeepResearch with similar projects before committing to a stack.
Before adopting
- Complete one clean-environment verification using the official DeepResearch setup path.
- Review repository license, model weights, external services, and dependency terms for your use case.
- Check recent commits, release cadence, issue response, and documentation depth.
- Evaluate output quality, latency, resource usage, and recovery behavior with a small dataset.
Configuration notes
- Review README configuration notes before using production data.
Sources checked
These links are used to verify repository, documentation, or tutorial details. Review the source pages before adopting the project.
Troubleshooting
- If installation fails, first confirm the command is being run from the README-specified directory.
- If dependencies conflict, retry in a fresh virtual environment, container, or working directory.
- If output looks wrong, return to the smallest documented DeepResearch example before adding complex data.
- For keys, model files, or external services, verify environment variables, local paths, and permissions one by one.
- Before production use, review recent updates, open issues, license terms, and safety boundaries.
What is DeepResearch?
DeepResearch is an open-source ai agents project. Tongyi Deep Research, the Leading Open-source Deep Research Agent
How do I install DeepResearch?
Start with the official README. The first detected setup step is: git clone https://github.com/Alibaba-NLP/DeepResearch.git.
Is DeepResearch beginner-friendly?
If you already know the Python ecosystem, start with the smallest example. Otherwise test it in an isolated environment first.
Can DeepResearch be used commercially?
GitHub detected the Apache-2.0 repository license, which generally permits commercial use. This signal only covers the repository license; review its obligations and any model weights, datasets, dependencies, or external services before commercial adoption.
Does DeepResearch need a GPU?
GPU requirements depend on the workload, model, and dataset size. Start with the smallest README example before scaling up.
How should I decide whether to adopt DeepResearch?
Evaluate setup cost, maintenance activity, issue health, license terms, and fit with your real workflow.