Comparison
qdrant vs deep-searcher
Verdict
Pick qdrant if high-performance vector database with support for distributed deployment; pick deep-searcher if deepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license.
Markdown twin · qdrant alternatives · deep-searcher alternatives
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Trust & integrity
| Signal | qdrant | deep-searcher |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Slowing (272d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- qdrant
- High-performance, massive-scale Vector Database and Vector Search Engine
- deep-searcher
- Open Source Deep Research Alternative to Reason and Search on Private Data.
Stars
- qdrant
- 34k
- deep-searcher
- 8.1k
Forks
- qdrant
- 2.5k
- deep-searcher
- 775
Open issues
- qdrant
- 652
- deep-searcher
- 53
Language
- qdrant
- Rust
- deep-searcher
- Python
Adopt for
- qdrant
- High-performance vector database with support for distributed deployment.
- deep-searcher
- DeepSearcher is an open-source tool for reasoning and searching on private data, using vector databases and LLM integrations in Python under Apache-2.0 license.
Persona
- qdrant
- -
- deep-searcher
- -
Runtime
- qdrant
- -
- deep-searcher
- -
License
- qdrant
- Qdrant is available under the Apache License 2.0.
- deep-searcher
- Apache-2.0
Last pushed
- qdrant
- Jul 28, 2026
- deep-searcher
- Nov 19, 2025
Categories
- qdrant
- Data & Retrieval, Vector Databases
- deep-searcher
- AI Agents, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- qdrant
- Very active (96%)
- deep-searcher
- Slowing (36%)
Days since push
- qdrant
- 0d
- deep-searcher
- 272d
Open issues (now)
- qdrant
- 652
- deep-searcher
- 53
Stars delta
- qdrant
- Unknown
- deep-searcher
- +59 (30d)
Open issues delta
- qdrant
- Unknown
- deep-searcher
- 0 (30d)
Full report
- qdrant
- Trust report
- deep-searcher
- Trust report
Typed relationship
qdrant alternative deep-searcherBoth Qdrant and Deep Searcher offer solutions for AI-powered research where private data is concerned, allowing users to perform reasoning and search tasks on their datasets.
Choose qdrant if…
- qdrant is primarily Rust; deep-searcher is Python.
- Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/.
- Requirements: - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections..
- Both Qdrant and Deep Searcher offer solutions for AI-powered research where private data is concerned, allowing users to perform reasoning and search tasks on their datasets.
- Tags unique to qdrant: ai-search, embeddings-similarity, hnsw, knn-algorithm.
- Also covers Data & Retrieval.
- - When scalability and performance are paramount in handling large-scale embeddings.
When NOT to use qdrant
- - Avoid if your project requires more traditional relational database features as Qdrant focuses exclusively on vectors.
- - If minimalistic setup is crucial, since Qdrant's capability for distributed deployment may introduce complexity that is not necessary for smaller-scale applications.
- - For use cases where non-Rust environments significantly limit the feasibility of integrating external tools.
Choose deep-searcher if…
- deep-searcher is primarily Python; qdrant is Rust.
- Both Qdrant and Deep Searcher offer solutions for AI-powered research where private data is concerned, allowing users to perform reasoning and search tasks on their datasets.
- Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm.
- Also covers AI Agents, LLM Frameworks.
- When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3.
When NOT to use deep-searcher
- Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems.
- Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (qdrant/qdrant) · observed Jul 28, 2026
- GitHub forks (qdrant/qdrant) · observed Jul 28, 2026
- Last push (qdrant/qdrant) · observed Jul 28, 2026
- License file (Apache-2.0) · observed Jul 28, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zilliztech/deep-searcher) · observed Aug 18, 2026
- GitHub forks (zilliztech/deep-searcher) · observed Aug 18, 2026
- Last push (zilliztech/deep-searcher) · observed Nov 19, 2025
- License file (Apache-2.0) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: qdrant 34k · deep-searcher 8.1k (synced Jul 28, 2026).
Common questions
- What is the difference between qdrant and deep-searcher?
- qdrant: High-performance, massive-scale Vector Database and Vector Search Engine. deep-searcher: Open Source Deep Research Alternative to Reason and Search on Private Data.. See the comparison table for live GitHub stats and shared categories.
- When should I choose qdrant over deep-searcher?
- Choose qdrant over deep-searcher when qdrant is primarily Rust; deep-searcher is Python; Qdrant supports self-hosted deployment along with a cloud option at https://cloud.qdrant.io/; Requirements: - Distributed deployment with sharding and replication is supported.; - No specific minimum RAM requirement provided. Performance and resource use will depend on the scale of embedding collections.; Both Qdrant and Deep Searcher offer solutions for AI-powered research where private data is concerned, allowing users to perform reasoning and search tasks on their datasets; Tags unique to qdrant: ai-search, embeddings-similarity, hnsw, knn-algorithm; Also covers Data & Retrieval; - When scalability and performance are paramount in handling large-scale embeddings.
- When should I choose deep-searcher over qdrant?
- Choose deep-searcher over qdrant when deep-searcher is primarily Python; qdrant is Rust; Both Qdrant and Deep Searcher offer solutions for AI-powered research where private data is concerned, allowing users to perform reasoning and search tasks on their datasets; Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm; Also covers AI Agents, LLM Frameworks; When you require custom search and reasoning capabilities on your private datasets with integration of multiple LLMs like Claude or Qwen3.
- When should I avoid qdrant?
- - Avoid if your project requires more traditional relational database features as Qdrant focuses exclusively on vectors. - If minimalistic setup is crucial, since Qdrant's capability for distributed deployment may introduce complexity that is not necessary for smaller-scale applications. - For use cases where non-Rust environments significantly limit the feasibility of integrating external tools.
- When should I avoid deep-searcher?
- Avoid if your project demands proprietary solutions, as DeepSearcher is open-source and may not be suitable for closed systems. Not ideal when a single vector database suffices; DeepSearcher supports multiple databases which might be overkill and complicate setup unnecessarily.
- Is qdrant or deep-searcher more popular on GitHub?
- qdrant has more GitHub stars (33,629 vs 8,060). Stars measure visibility, not whether either tool fits your constraints.
- Are qdrant and deep-searcher open source?
- Yes - both are open-source projects on GitHub (qdrant: Apache-2.0, deep-searcher: Apache-2.0).
- Where can I find alternatives to qdrant or deep-searcher?
- GraphCanon lists graph-backed alternatives at qdrant alternatives and deep-searcher alternatives (qdrant markdown twin, deep-searcher markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, qdrant or deep-searcher?
- qdrant: Very active. deep-searcher: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for qdrant and deep-searcher?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: qdrant trust report; deep-searcher trust report.