Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →brycewang-stanford/deep-searcher-guide
Open deep research alternative for private data with vector search
Passed 17 of 23 test cases with the skill, and 6 of 23 without it.
and 13 more in the full report
11 fixed6 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
What pip installation command installs the Python client for embedded Milvus Lite database support required by Deep Searcher?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies pip install pymilvus[model]. | Missed | Passed |
The instructions your model reads when the skill loads.
Deep Searcher is an open-source deep research tool developed by Zilliz with over 8,000 GitHub stars, designed to be an open alternative to proprietary deep research systems like OpenAI's Deep Research and Gemini Deep Research. What distinguishes Deep Searcher is its focus on private data -- it enables researchers to conduct deep, iterative research over their own document collections, databases, and institutional knowledge bases rather than being limited to public web content.
The system combines vector search via Milvus (or other vector databases) with agentic RAG (Retrieval-Augmented Generation) to decompose complex research questions, retrieve relevant passages from your document collection, reason over the retrieved content, and iteratively refine its search until it can produce a comprehensive answer. This makes it particularly valuable for researchers who work with proprietary datasets, unpublished manuscripts, internal reports, or specialized domain corpora that are not available through web search.
Deep Searcher supports multiple LLM providers and embedding models, and can be deployed entirely on-premises for organizations with strict data privacy requirements. It is built on top of Milvus, the high-performance open-source vector database also created by Zilliz, ensuring scalable and efficient similarity search across large document collections.
# Install Deep Searcher
pip install deepsearcher
# Or clone for development
git clone https://github.com/zilliztech/deep-searcher.git
cd deep-searcher
pip install -e .
Deep Searcher requires a vector database and LLM access:
# Option 1: Milvus Lite (embedded, no separate server needed)
pip install pymilvus[model]
# Option 2: Full Milvus via Docker
docker run -d --name milvus \
-p 19530:19530 \
-p 9091:9091 \
milvusio/milvus:latest standalone
# Configure LLM access
export OPENAI_API_KEY=$OPENAI_API_KEY
# Or for local LLMs
export OLLAMA_BASE_URL=http://localhost:11434
test cases passed, vs 6 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.