Comparison
awesome-ai-apps vs deep-research
Verdict
Pick awesome-ai-apps if awesome-ai-apps is a curated list of projects focusing on AI applications and innovations such as RAG technologies, AI agents, and workflows, emphasizing large language models using Python; pick deep-research if deep Research is a JavaScript-based framework enabling integration of various Large Language Models for deep research projects using SSE and MCP.
Markdown twin · awesome-ai-apps alternatives · deep-research alternatives
GraphCanon updated Sep 20, 2026
5views this month
vs
Trust & integrity
| Signal | awesome-ai-apps | deep-research |
|---|---|---|
| Maintenance | Very active (1d since push) As of Sep 20, 2026 · github_public_v1 | Slowing (93d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 20, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 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 Aug 30, 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
- awesome-ai-apps
- A curated list of AI applications showcasing RAG, agents, and workflows.
- deep-research
- Use any LLMs for Deep Research with SSE API and MCP server
Stars
- awesome-ai-apps
- 16k
- deep-research
- 4.7k
Forks
- awesome-ai-apps
- 1.8k
- deep-research
- 1.1k
Open issues
- awesome-ai-apps
- 65
- deep-research
- 39
Language
- awesome-ai-apps
- Python
- deep-research
- JavaScript
Adopt for
- awesome-ai-apps
- awesome-ai-apps is a curated list of projects focusing on AI applications and innovations such as RAG technologies, AI agents, and workflows, emphasizing large language models using Python.
- deep-research
- Deep Research is a JavaScript-based framework enabling integration of various Large Language Models for deep research projects using SSE and MCP.
Persona
- awesome-ai-apps
- -
- deep-research
- -
Runtime
- awesome-ai-apps
- -
- deep-research
- -
License
- awesome-ai-apps
- MIT License ensures easy integration into both open source and proprietary projects without restrictions.
- deep-research
- MIT
Last pushed
- awesome-ai-apps
- Sep 18, 2026
- deep-research
- Jun 18, 2026
Categories
- awesome-ai-apps
- AI Agents, LLM Frameworks
- deep-research
- Inference & Serving, LLM Frameworks
Trust and health
Maintenance
- awesome-ai-apps
- Very active (96%)
- deep-research
- Slowing (36%)
Days since push
- awesome-ai-apps
- 1d
- deep-research
- 93d
Open issues (now)
- awesome-ai-apps
- 65
- deep-research
- 39
Stars delta
- awesome-ai-apps
- +2.4k (30d)
- deep-research
- +2 (30d)
Open issues delta
- awesome-ai-apps
- -24 (30d)
- deep-research
- +3 (30d)
Owner type
- awesome-ai-apps
- User
- deep-research
- Organization
Full report
- awesome-ai-apps
- Trust report
- deep-research
- Trust report
Choose awesome-ai-apps if…
- awesome-ai-apps is primarily Python; deep-research is JavaScript.
- Pricing: As an open-source project under the MIT License, awesome-ai-apps is free to use. There are no paid plans beyond potential third-party service integrations or support contracts..
- Requirements: Requires understanding of Python and familiarity with large language models and RAG technologies to benefit fully from the projects listed..
- Tags unique to awesome-ai-apps: agents, ai, hacktoberfest, llm.
- Also covers AI Agents.
- Use awesome-ai-apps when looking to explore or implement Retrieval-Augmented Generation (RAG) in Python projects focused on enhancing search-based question answering.
When NOT to use awesome-ai-apps
- Avoid awesome-ai-apps if your project requires non-Python support, as all the included applications are built using Python.
- Do not use this repository if your focus is on backend-only AI services that do not involve RAG technologies or AI agents.
Choose deep-research if…
- deep-research is primarily JavaScript; awesome-ai-apps is Python.
- Tags unique to deep-research: anthropic, deep-research-api, gemini, grok.
- Also covers Inference & Serving.
- deep-research ships Docker support for self-hosted deployment.
- - When requiring an API interface that supports Server-Sent Events (SSE) and Model Control Protocol (MCP) for integrating large language models
When NOT to use deep-research
- - When working with environments that do not support JavaScript, as Deep Research is primarily built on this language
- - For projects that require real-time bidirectional communication with models, as Deep Research might only provide unidirectional data flow through SSE
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Arindam200/awesome-ai-apps) · observed Sep 20, 2026
- GitHub forks (Arindam200/awesome-ai-apps) · observed Sep 20, 2026
- Last push (Arindam200/awesome-ai-apps) · observed Sep 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (u14app/deep-research) · observed Sep 20, 2026
- GitHub forks (u14app/deep-research) · observed Sep 20, 2026
- Last push (u14app/deep-research) · observed Jun 18, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Aug 30, 2026
GitHub stars on cards: awesome-ai-apps 16k · deep-research 4.7k (synced Sep 20, 2026).
Common questions
- What is the difference between awesome-ai-apps and deep-research?
- awesome-ai-apps: A curated list of AI applications showcasing RAG, agents, and workflows.. deep-research: Use any LLMs for Deep Research with SSE API and MCP server. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-ai-apps over deep-research?
- Choose awesome-ai-apps over deep-research when awesome-ai-apps is primarily Python; deep-research is JavaScript; Pricing: As an open-source project under the MIT License, awesome-ai-apps is free to use. There are no paid plans beyond potential third-party service integrations or support contracts.; Requirements: Requires understanding of Python and familiarity with large language models and RAG technologies to benefit fully from the projects listed.; Tags unique to awesome-ai-apps: agents, ai, hacktoberfest, llm; Also covers AI Agents; Use awesome-ai-apps when looking to explore or implement Retrieval-Augmented Generation (RAG) in Python projects focused on enhancing search-based question answering.
- When should I choose deep-research over awesome-ai-apps?
- Choose deep-research over awesome-ai-apps when deep-research is primarily JavaScript; awesome-ai-apps is Python; Tags unique to deep-research: anthropic, deep-research-api, gemini, grok; Also covers Inference & Serving; deep-research ships Docker support for self-hosted deployment; - When requiring an API interface that supports Server-Sent Events (SSE) and Model Control Protocol (MCP) for integrating large language models.
- When should I avoid awesome-ai-apps?
- Avoid awesome-ai-apps if your project requires non-Python support, as all the included applications are built using Python. Do not use this repository if your focus is on backend-only AI services that do not involve RAG technologies or AI agents.
- When should I avoid deep-research?
- - When working with environments that do not support JavaScript, as Deep Research is primarily built on this language - For projects that require real-time bidirectional communication with models, as Deep Research might only provide unidirectional data flow through SSE
- Is awesome-ai-apps or deep-research more popular on GitHub?
- awesome-ai-apps has more GitHub stars (15,671 vs 4,688). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-ai-apps and deep-research open source?
- Yes - both are open-source projects on GitHub (awesome-ai-apps: MIT, deep-research: MIT).
- Where can I find alternatives to awesome-ai-apps or deep-research?
- GraphCanon lists graph-backed alternatives at awesome-ai-apps alternatives and deep-research alternatives (awesome-ai-apps markdown twin, deep-research 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, awesome-ai-apps or deep-research?
- awesome-ai-apps: Very active. deep-research: 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 awesome-ai-apps and deep-research?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-ai-apps trust report; deep-research trust report.