Category: provider

Model Studio Qwen Deep Research

Validation

mkdir -p output/aliyun-qwen-deep-research
python -m py_compile skills/ai/research/aliyun-qwen-deep-research/scripts/prepare_deep_research_request.py && echo "py_compile_ok" > output/aliyun-qwen-deep-research/validate.txt

Pass criteria: command exits 0 and output/aliyun-qwen-deep-research/validate.txt is generated.

Output And Evidence

• Save research goals, confirmation answers, normalized request payloads, and final report snapshots under output/aliyun-qwen-deep-research/.
• Keep the exact model, region, and enable_feedback setting with each saved run.

Use this skill when the user wants a deep, multi-stage research workflow rather than a single chat completion.

Critical model names

Use one of these exact model strings:

• qwen-deep-research
• qwen-deep-research-2025-12-15

Selection guidance:

• Use qwen-deep-research for the current mainline model.
• Use qwen-deep-research-2025-12-15 when you need the snapshot with MCP tool-calling support and stronger reproducibility.

Prerequisites

• Install SDK in a virtual environment:

python3 -m venv .venv
. .venv/bin/activate
python -m pip install dashscope

• Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.
• This model currently applies to the China mainland (Beijing) region and uses its own API shape rather than OpenAI-compatible mode.

Normalized interface (research.run)

Request

• topic (string, required)
• model (string, optional): default qwen-deep-research
• messages (array, optional)
• enable_feedback (bool, optional): default true
• stream (bool, optional): must be true
• attachments (array, optional): image URLs and related context

Response

• status (string): stage status such as thinking, researching, or finished
• text (string, optional): streamed content chunk
• report (string, optional): final structured research report
• raw (object, optional)

Quick start

python skills/ai/research/aliyun-qwen-deep-research/scripts/prepare_deep_research_request.py \
  --topic "Compare cloud video generation model trade-offs for marketing automation." \
  --disable-feedback

Operational guidance

• Expect streaming output only.
• Keep the initial topic concrete and bounded; broad topics can trigger long iterative search plans.
• If the model asks follow-up questions and you already know the constraints, answer them explicitly to avoid wasted rounds.
• Use the snapshot model when you need stable evaluation runs or MCP tool-calling support.

Output location

• Default output: output/aliyun-qwen-deep-research/requests/
• Override base dir with OUTPUT_DIR.

References

• references/sources.md