Comparison
dify vs agents-towards-production
Verdict
Pick dify if dify is a comprehensive low-code/no-code AI agentic framework designed for workflow and process automation, offering deployment via Docker Compose and multiple cloud platforms; pick agents-towards-production if agents-towards-production is an open-source project focused on providing comprehensive, step-by-step tutorials for developing AI agents from the prototype stage to enterprise-ready deployment. This guide includes best-pr.
Markdown twin · dify alternatives · agents-towards-production alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | dify | agents-towards-production |
|---|---|---|
| Maintenance | Very active (0d since push) As of 1w · github_public_v1 | Very active (3d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Personal account As of 3d · 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
- dify
- Production-ready platform for agentic workflow development
- agents-towards-production
- End-to-end, code-first tutorials for building production-grade GenAI agents
Stars
- dify
- 152k
- agents-towards-production
- 21k
Forks
- dify
- 24k
- agents-towards-production
- 2.8k
Open issues
- dify
- 931
- agents-towards-production
- 15
Language
- dify
- TypeScript
- agents-towards-production
- Jupyter Notebook
Adopt for
- dify
- Dify is a comprehensive low-code/no-code AI agentic framework designed for workflow and process automation, offering deployment via Docker Compose and multiple cloud platforms.
- agents-towards-production
- agents-towards-production is an open-source project focused on providing comprehensive, step-by-step tutorials for developing AI agents from the prototype stage to enterprise-ready deployment. This guide includes best-pr
Persona
- dify
- -
- agents-towards-production
- -
Runtime
- dify
- -
- agents-towards-production
- -
License
- dify
- Other
- agents-towards-production
- Other
Last pushed
- dify
- Aug 7, 2026
- agents-towards-production
- Aug 15, 2026
Categories
- dify
- AI Agents
- agents-towards-production
- AI Agents
Trust and health
Days since push
- dify
- 0d
- agents-towards-production
- 3d
Open issues (now)
- dify
- 931
- agents-towards-production
- 15
Stars delta
- dify
- +3.6k (30d)
- agents-towards-production
- +191 (30d)
Open issues delta
- dify
- +118 (30d)
- agents-towards-production
- +4 (30d)
Owner type
- dify
- Organization
- agents-towards-production
- User
Full report
- dify
- Trust report
- agents-towards-production
- Trust report
Typed relationship
dify alternative agents-towards-productionagents-towards-production and dify both focus on agentic workflow development for production settings.
Choose dify if…
- dify is primarily TypeScript; agents-towards-production is Jupyter Notebook.
- Requirements: Min 4 GB RAM; Requires Docker.
- agents-towards-production and dify both focus on agentic workflow development for production settings.
- Tags unique to dify: agent, agentic-framework, automation, gemini.
- When you need a production-grade platform with support for various deployment methods such as Docker Compose, Kubernetes Helm Charts, Terraform, AWS CDK, and Alibaba Cloud services.
When NOT to use dify
- If your project strictly requires a specific proprietary licensing scheme not aligned with Dify’s customized open-source license that is based on Apache 2.0.
- In cases where the desired workflow development tool should be written in a language other than TypeScript or Python, as Dify centers primarily around these languages.
Choose agents-towards-production if…
- agents-towards-production is primarily Jupyter Notebook; dify is TypeScript.
- agents-towards-production and dify both focus on agentic workflow development for production settings.
- Tags unique to agents-towards-production: agent-framework, deployment, langgraph, llms.
- * When you aim to deploy AI agents using cloud services such as AWS Bedrock AgentCore Runtime, where automatic infrastructure management and standardized communication patterns are key.
When NOT to use agents-towards-production
- * If your enterprise strictly forbids using cloud services; this tool emphasizes both cloud and on-prem deployment strategies but may not fit entirely on-prem infrastructures.
- * When you are looking for a fully managed service without code-first or tutorial-guided approaches, as 'agents-towards-production' focuses heavily on hands-on tutorials and end-to-end guide creation.
- * If your specific AI agent workload does not align with the foundational deployment patterns covered (containerization, AWS Bedrock, Ollama on-prem solutions, Runpod GPU deployment), other tools may,
- other_remarks_and_conditions_of_use_or_nonuse_examples_with_links_or_code_snippets_e.g_github_issues__pull_requests__branch_names_etc_that_affect_anyoftheabove_can_be_cited_if_pertinent.
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 Aug 8, 2026
- GitHub forks (langgenius/dify) · observed Aug 8, 2026
- Last push (langgenius/dify) · observed Aug 7, 2026
- License file (Other) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (NirDiamant/agents-towards-production) · observed Aug 18, 2026
- GitHub forks (NirDiamant/agents-towards-production) · observed Aug 18, 2026
- Last push (NirDiamant/agents-towards-production) · observed Aug 15, 2026
- License file (Other) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: dify 152k · agents-towards-production 21k (synced Aug 8, 2026).
Common questions
- What is the difference between dify and agents-towards-production?
- dify: Production-ready platform for agentic workflow development. agents-towards-production: End-to-end, code-first tutorials for building production-grade GenAI agents. See the comparison table for live GitHub stats and shared categories.
- When should I choose dify over agents-towards-production?
- Choose dify over agents-towards-production when dify is primarily TypeScript; agents-towards-production is Jupyter Notebook; Requirements: Min 4 GB RAM; Requires Docker; agents-towards-production and dify both focus on agentic workflow development for production settings; Tags unique to dify: agent, agentic-framework, automation, gemini; When you need a production-grade platform with support for various deployment methods such as Docker Compose, Kubernetes Helm Charts, Terraform, AWS CDK, and Alibaba Cloud services.
- When should I choose agents-towards-production over dify?
- Choose agents-towards-production over dify when agents-towards-production is primarily Jupyter Notebook; dify is TypeScript; agents-towards-production and dify both focus on agentic workflow development for production settings; Tags unique to agents-towards-production: agent-framework, deployment, langgraph, llms; * When you aim to deploy AI agents using cloud services such as AWS Bedrock AgentCore Runtime, where automatic infrastructure management and standardized communication patterns are key.
- When should I avoid dify?
- If your project strictly requires a specific proprietary licensing scheme not aligned with Dify’s customized open-source license that is based on Apache 2.0. In cases where the desired workflow development tool should be written in a language other than TypeScript or Python, as Dify centers primarily around these languages.
- When should I avoid agents-towards-production?
- * If your enterprise strictly forbids using cloud services; this tool emphasizes both cloud and on-prem deployment strategies but may not fit entirely on-prem infrastructures. * When you are looking for a fully managed service without code-first or tutorial-guided approaches, as 'agents-towards-production' focuses heavily on hands-on tutorials and end-to-end guide creation. * If your specific AI agent workload does not align with the foundational deployment patterns covered (containerization, AWS Bedrock, Ollama on-prem solutions, Runpod GPU deployment), other tools may, other_remarks_and_conditions_of_use_or_nonuse_examples_with_links_or_code_snippets_e.g_github_issues__pull_requests__branch_names_etc_that_affect_anyoftheabove_can_be_cited_if_pertinent.
- Is dify or agents-towards-production more popular on GitHub?
- dify has more GitHub stars (151,725 vs 21,298). Stars measure visibility, not whether either tool fits your constraints.
- Are dify and agents-towards-production open source?
- Yes - both are open-source projects on GitHub (dify: Other, agents-towards-production: Other).
- Where can I find alternatives to dify or agents-towards-production?
- GraphCanon lists graph-backed alternatives at dify alternatives and agents-towards-production alternatives (dify markdown twin, agents-towards-production 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 agents-towards-production?
- dify: Very active. agents-towards-production: Very 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 dify and agents-towards-production?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: dify trust report; agents-towards-production trust report.