Comparison
awesome-llm-apps vs docetl
Verdict
Pick awesome-llm-apps when license: awesome-llm-apps is Apache-2.0, docetl is MIT; pick docetl when license: docetl is MIT, awesome-llm-apps is Apache-2.0.
Markdown twin · awesome-llm-apps alternatives · docetl alternatives
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Trust & integrity
| Signal | awesome-llm-apps | docetl |
|---|---|---|
| Maintenance | Very active (3d since push) As of 1d · github_public_v1 | Active (18d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of 1d · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 4d · osv@v1 | No lockfile (source not queried) As of today · 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-llm-apps
- Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.
- docetl
- A system for agentic LLM-powered data processing and ETL
Stars
- awesome-llm-apps
- 120k
- docetl
- 3.9k
Forks
- awesome-llm-apps
- 18k
- docetl
- 414
Open issues
- awesome-llm-apps
- 17
- docetl
- 41
Language
- awesome-llm-apps
- Python
- docetl
- Python
Adopt for
- awesome-llm-apps
- awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python.
- docetl
- -
Persona
- awesome-llm-apps
- -
- docetl
- -
Runtime
- awesome-llm-apps
- -
- docetl
- -
License
- awesome-llm-apps
- The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license.
- docetl
- MIT
Last pushed
- awesome-llm-apps
- Jul 11, 2026
- docetl
- Jun 26, 2026
Categories
- awesome-llm-apps
- AI Agents, Data & Retrieval
- docetl
- AI Agents, Data & Retrieval, LLM Frameworks
Trust and health
Maintenance
- awesome-llm-apps
- Very active (96%)
- docetl
- Active (82%)
Days since push
- awesome-llm-apps
- 3d
- docetl
- 18d
Open issues (now)
- awesome-llm-apps
- 17
- docetl
- 41
Owner type
- awesome-llm-apps
- User
- docetl
- Organization
Full report
- awesome-llm-apps
- Trust report
- docetl
- Trust report
Shared compatibility
- Python · awesome-llm-apps: Python runtime · docetl: Python runtime
Choose awesome-llm-apps if…
- License: awesome-llm-apps is Apache-2.0, docetl is MIT.
- Pricing: Free with open-source licensing, but commercial exploitation is allowed..
- Tags unique to awesome-llm-apps: applications, customizable, deployable, llms.
- When you need quick implementations of various real-world use cases for AI Agents and RAG.
When NOT to use awesome-llm-apps
- If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch.
- When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction.
Choose docetl if…
- License: docetl is MIT, awesome-llm-apps is Apache-2.0.
- Tags unique to docetl: data, data-pipelines, document-analysis, document-processing.
- Also covers LLM Frameworks.
- docetl ships Docker support for self-hosted deployment.
When NOT to use docetl
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Shubhamsaboo/awesome-llm-apps) · observed Jul 14, 2026
- GitHub forks (Shubhamsaboo/awesome-llm-apps) · observed Jul 14, 2026
- Last push (Shubhamsaboo/awesome-llm-apps) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 14, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ucbepic/docetl) · observed Jul 15, 2026
- GitHub forks (ucbepic/docetl) · observed Jul 15, 2026
- Last push (ucbepic/docetl) · observed Jun 26, 2026
- License file (MIT) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: awesome-llm-apps 120k · docetl 3.9k (synced Jul 14, 2026).
Common questions
- What is the difference between awesome-llm-apps and docetl?
- awesome-llm-apps: Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.. docetl: A system for agentic LLM-powered data processing and ETL. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-llm-apps over docetl?
- Choose awesome-llm-apps over docetl when License: awesome-llm-apps is Apache-2.0, docetl is MIT; Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: applications, customizable, deployable, llms; When you need quick implementations of various real-world use cases for AI Agents and RAG.
- When should I choose docetl over awesome-llm-apps?
- Choose docetl over awesome-llm-apps when License: docetl is MIT, awesome-llm-apps is Apache-2.0; Tags unique to docetl: data, data-pipelines, document-analysis, document-processing; Also covers LLM Frameworks; docetl ships Docker support for self-hosted deployment.
- When should I avoid awesome-llm-apps?
- If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction.
- When should I avoid docetl?
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- Is awesome-llm-apps or docetl more popular on GitHub?
- awesome-llm-apps has more GitHub stars (119,936 vs 3,888). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-llm-apps and docetl open source?
- Yes - both are open-source projects on GitHub (awesome-llm-apps: Apache-2.0, docetl: MIT).
- Where can I find alternatives to awesome-llm-apps or docetl?
- GraphCanon lists graph-backed alternatives at awesome-llm-apps alternatives and docetl alternatives (awesome-llm-apps markdown twin, docetl 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-llm-apps or docetl?
- awesome-llm-apps: Very active. docetl: Active. 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-llm-apps and docetl?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-llm-apps trust report; docetl trust report.