Comparison
dify vs AdalFlow
Verdict
Pick dify if dify is a platform for creating agentic workflows and RAG pipelines, supporting various AI models and tools, deployable on cloud, VPC, or self-hosted environments; pick AdalFlow if adalFlow is designed to streamline the development and automatic optimization of LLM applications.
Markdown twin · dify alternatives · AdalFlow alternatives
GraphCanon updated Oct 4, 2026
7views this month
Trust & integrity
| Signal | dify | AdalFlow |
|---|---|---|
| Maintenance | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (101d since push) As of Sep 7, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 7, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Oct 4, 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
- dify
- Build Agentic workflows and RAG pipelines with rich AI model and tool support on one collaborative workspace.
- AdalFlow
- The library to build & auto-optimize LLM applications.
Stars
- dify
- 156k
- AdalFlow
- 4.2k
Forks
- dify
- 25k
- AdalFlow
- 387
Open issues
- dify
- 1.1k
- AdalFlow
- 68
Language
- dify
- TypeScript
- AdalFlow
- Python
Adopt for
- dify
- Dify is a platform for creating agentic workflows and RAG pipelines, supporting various AI models and tools, deployable on cloud, VPC, or self-hosted environments.
- AdalFlow
- AdalFlow is designed to streamline the development and automatic optimization of LLM applications.
Persona
- dify
- -
- AdalFlow
- -
Runtime
- dify
- -
- AdalFlow
- -
License
- dify
- Other
- AdalFlow
- MIT
Last pushed
- dify
- Sep 18, 2026
- AdalFlow
- May 29, 2026
Categories
- dify
- AI Agents, Data & Retrieval, Developer Tools
- AdalFlow
- AI Agents, Data & Retrieval, LLM Frameworks, Model Training
Trust and health
Maintenance
- dify
- Very active (96%)
- AdalFlow
- Slowing (36%)
Days since push
- dify
- 0d
- AdalFlow
- 101d
Open issues (now)
- dify
- 1.1k
- AdalFlow
- 68
Stars delta
- dify
- +4.5k (30d)
- AdalFlow
- +17 (30d)
Open issues delta
- dify
- +172 (30d)
- AdalFlow
- 0 (30d)
Full report
- dify
- Trust report
- AdalFlow
- Trust report
Choose dify if…
- dify is primarily TypeScript; AdalFlow is Python.
- License: dify is Other, AdalFlow is MIT.
- Tags unique to dify: agentic-ai, agentic-framework, agentic-workflow, automation.
- Also covers Developer Tools.
- When you need a collaborative workspace that supports rich AI model and tool integration for agentic workflows and RAG pipelines.
When NOT to use dify
- If your project does not require agentic workflows or RAG pipelines, and you are looking for a more general-purpose AI development tool.
- When you need a platform that does not require Docker and Docker Compose for setup, as Dify's quick start guide assumes these prerequisites.
- If your team prefers a platform with a different licensing model, as Dify uses a modified Apache 2.0 license with additional conditions.
Choose AdalFlow if…
- AdalFlow is primarily Python; dify is TypeScript.
- License: AdalFlow is MIT, dify is Other.
- Tags unique to AdalFlow: auto-prompting, bm25, chatbot, faiss.
- Also covers LLM Frameworks, Model Training.
- When you are working on projects that require advanced AI agents or chatbots with auto-prompting features, as AdalFlow can handle these needs comprehensively.
When NOT to use AdalFlow
- Avoid using AdalFlow if your project does not benefit from auto-optimization features or does not involve LLM applications, as its specialized capabilities might introduce unnecessary complexity.
- AdalFlow may not be the best choice for projects where custom or low-level control over all aspects of the AI model training and optimization is required, given it's designed to streamline processes.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (langgenius/dify) · observed Sep 20, 2026
- GitHub forks (langgenius/dify) · observed Sep 20, 2026
- Last push (langgenius/dify) · observed Sep 18, 2026
- License file (Other) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Oct 4, 2026
- GitHub stars (SylphAI-Inc/AdalFlow) · observed Sep 20, 2026
- GitHub forks (SylphAI-Inc/AdalFlow) · observed Sep 20, 2026
- Last push (SylphAI-Inc/AdalFlow) · observed May 29, 2026
- License file (MIT) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: dify 156k · AdalFlow 4.2k (synced Sep 20, 2026).
Common questions
- What is the difference between dify and AdalFlow?
- dify: Build Agentic workflows and RAG pipelines with rich AI model and tool support on one collaborative workspace.. AdalFlow: The library to build & auto-optimize LLM applications.. See the comparison table for live GitHub stats and shared categories.
- When should I choose dify over AdalFlow?
- Choose dify over AdalFlow when dify is primarily TypeScript; AdalFlow is Python; License: dify is Other, AdalFlow is MIT; Tags unique to dify: agentic-ai, agentic-framework, agentic-workflow, automation; Also covers Developer Tools; When you need a collaborative workspace that supports rich AI model and tool integration for agentic workflows and RAG pipelines.
- When should I choose AdalFlow over dify?
- Choose AdalFlow over dify when AdalFlow is primarily Python; dify is TypeScript; License: AdalFlow is MIT, dify is Other; Tags unique to AdalFlow: auto-prompting, bm25, chatbot, faiss; Also covers LLM Frameworks, Model Training; When you are working on projects that require advanced AI agents or chatbots with auto-prompting features, as AdalFlow can handle these needs comprehensively.
- When should I avoid dify?
- If your project does not require agentic workflows or RAG pipelines, and you are looking for a more general-purpose AI development tool. When you need a platform that does not require Docker and Docker Compose for setup, as Dify's quick start guide assumes these prerequisites. If your team prefers a platform with a different licensing model, as Dify uses a modified Apache 2.0 license with additional conditions.
- When should I avoid AdalFlow?
- Avoid using AdalFlow if your project does not benefit from auto-optimization features or does not involve LLM applications, as its specialized capabilities might introduce unnecessary complexity. AdalFlow may not be the best choice for projects where custom or low-level control over all aspects of the AI model training and optimization is required, given it's designed to streamline processes.
- Is dify or AdalFlow more popular on GitHub?
- dify has more GitHub stars (156,243 vs 4,213). Stars measure visibility, not whether either tool fits your constraints.
- Are dify and AdalFlow open source?
- Yes - both are open-source projects on GitHub (dify: Other, AdalFlow: MIT).
- Where can I find alternatives to dify or AdalFlow?
- GraphCanon lists graph-backed alternatives at dify alternatives and AdalFlow alternatives (dify markdown twin, AdalFlow 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, dify or AdalFlow?
- dify: Very active. AdalFlow: 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 dify and AdalFlow?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dify trust report; AdalFlow trust report.