Comparison
incognide vs deep-searcher
Verdict
Pick incognide if incognide is a TypeScript library for developing AI agents with integrated capabilities for llm-inference; 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 · incognide alternatives · deep-searcher alternatives
GraphCanon updated Sep 20, 2026
11views this month
vs
Trust & integrity
| Signal | incognide | deep-searcher |
|---|---|---|
| Maintenance | Very active (4d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (272d since push) As of Aug 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Aug 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- incognide
- Develops AI agents and integrates llms for inference
- deep-searcher
- Open Source Deep Research Alternative to Reason and Search on Private Data.
Stars
- incognide
- 471
- deep-searcher
- 8.1k
Forks
- incognide
- 50
- deep-searcher
- 775
Open issues
- incognide
- 12
- deep-searcher
- 53
Language
- incognide
- TypeScript
- deep-searcher
- Python
Adopt for
- incognide
- Incognide is a TypeScript library for developing AI agents with integrated capabilities for llm-inference.
- 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
- incognide
- -
- deep-searcher
- -
Runtime
- incognide
- -
- deep-searcher
- -
License
- incognide
- MIT
- deep-searcher
- Apache-2.0
Last pushed
- incognide
- Sep 16, 2026
- deep-searcher
- Nov 19, 2025
Categories
- incognide
- AI Agents, Inference & Serving, LLM Frameworks
- deep-searcher
- AI Agents, LLM Frameworks, Vector Databases
Trust and health
Maintenance
- incognide
- Very active (96%)
- deep-searcher
- Slowing (36%)
Days since push
- incognide
- 4d
- deep-searcher
- 272d
Open issues (now)
- incognide
- 12
- deep-searcher
- 53
Stars delta
- incognide
- +12 (30d)
- deep-searcher
- +59 (30d)
Open issues delta
- incognide
- -6 (30d)
- deep-searcher
- 0 (30d)
Full report
- incognide
- Trust report
- deep-searcher
- Trust report
Choose incognide if…
- incognide is primarily TypeScript; deep-searcher is Python.
- License: incognide is MIT, deep-searcher is Apache-2.0.
- Tags unique to incognide: agents, ai-agents, artificial-intelligence, llm-inference.
- Also covers Inference & Serving.
- When you need a TypeScript-based solution to develop AI agents that integrate llm-inference features
When NOT to use incognide
- If your project requires languages other than TypeScript as the primary language
- Not suitable if you are looking for ready-to-deploy production tools, given its focus on research and development phase
Choose deep-searcher if…
- deep-searcher is primarily Python; incognide is TypeScript.
- License: deep-searcher is Apache-2.0, incognide is MIT.
- Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm.
- Also covers Vector Databases.
- 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 (NPC-Worldwide/incognide) · observed Sep 20, 2026
- GitHub forks (NPC-Worldwide/incognide) · observed Sep 20, 2026
- Last push (NPC-Worldwide/incognide) · observed Sep 16, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zilliztech/deep-searcher) · observed Sep 20, 2026
- GitHub forks (zilliztech/deep-searcher) · observed Sep 20, 2026
- Last push (zilliztech/deep-searcher) · observed Nov 19, 2025
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: incognide 471 · deep-searcher 8.1k (synced Sep 20, 2026).
Common questions
- What is the difference between incognide and deep-searcher?
- incognide: Develops AI agents and integrates llms for inference. 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 incognide over deep-searcher?
- Choose incognide over deep-searcher when incognide is primarily TypeScript; deep-searcher is Python; License: incognide is MIT, deep-searcher is Apache-2.0; Tags unique to incognide: agents, ai-agents, artificial-intelligence, llm-inference; Also covers Inference & Serving; When you need a TypeScript-based solution to develop AI agents that integrate llm-inference features.
- When should I choose deep-searcher over incognide?
- Choose deep-searcher over incognide when deep-searcher is primarily Python; incognide is TypeScript; License: deep-searcher is Apache-2.0, incognide is MIT; Tags unique to deep-searcher: agent, agentic-rag, deep-research, llm; Also covers Vector Databases; 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 incognide?
- If your project requires languages other than TypeScript as the primary language Not suitable if you are looking for ready-to-deploy production tools, given its focus on research and development phase
- 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 incognide or deep-searcher more popular on GitHub?
- deep-searcher has more GitHub stars (8,060 vs 471). Stars measure visibility, not whether either tool fits your constraints.
- Are incognide and deep-searcher open source?
- Yes - both are open-source projects on GitHub (incognide: MIT, deep-searcher: Apache-2.0).
- Where can I find alternatives to incognide or deep-searcher?
- GraphCanon lists graph-backed alternatives at incognide alternatives and deep-searcher alternatives (incognide 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, incognide or deep-searcher?
- incognide: 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 incognide and deep-searcher?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: incognide trust report; deep-searcher trust report.