Comparison
kotaemon vs ragflow
Verdict
Pick kotaemon if cinnamon's kotaemon is an open-source Retrieval-Augmented Generation-based chat tool that facilitates document interaction through a user-friendly chat interface; pick ragflow if rAGFlow is an open-source RAG engine that integrates agent capabilities to enhance context management for LLMs, built in Go under the Apache-2.0 license.
Markdown twin · kotaemon alternatives · ragflow alternatives
GraphCanon updated Sep 20, 2026
16views this month
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Trust & integrity
| Signal | kotaemon | ragflow |
|---|---|---|
| Maintenance | Steady (34d since push) As of Aug 18, 2026 · github_public_v1 | Very active (0d since push) As of Sep 18, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Aug 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | Published findings As of Sep 18, 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
- kotaemon
- An open-source RAG-based tool for chatting with your documents.
- ragflow
- Retrieval-Augmented Generation (RAG) engine with agent capabilities
Stars
- kotaemon
- 26k
- ragflow
- 91k
Forks
- kotaemon
- 2.1k
- ragflow
- 11k
Open issues
- kotaemon
- 241
- ragflow
- 1.4k
Language
- kotaemon
- Python
- ragflow
- Go
Adopt for
- kotaemon
- Cinnamon's kotaemon is an open-source Retrieval-Augmented Generation-based chat tool that facilitates document interaction through a user-friendly chat interface.
- ragflow
- RAGFlow is an open-source RAG engine that integrates agent capabilities to enhance context management for LLMs, built in Go under the Apache-2.0 license.
Persona
- kotaemon
- -
- ragflow
- -
Runtime
- kotaemon
- -
- ragflow
- -
License
- kotaemon
- Apache-2.0
- ragflow
- Apache-2.0
Last pushed
- kotaemon
- Jul 14, 2026
- ragflow
- Sep 18, 2026
Categories
- kotaemon
- Data & Retrieval, Developer Tools
- ragflow
- AI Agents, Data & Retrieval
Trust and health
Maintenance
- kotaemon
- Steady (60%)
- ragflow
- Very active (96%)
Days since push
- kotaemon
- 34d
- ragflow
- 0d
Open issues (now)
- kotaemon
- 241
- ragflow
- 1.4k
Stars delta
- kotaemon
- +138 (30d)
- ragflow
- +4.4k (30d)
Open issues delta
- kotaemon
- +4 (30d)
- ragflow
- -553 (30d)
OSV dependency advisories
- kotaemon
- No lockfile (source not queried)
- ragflow
- Published findings
Full report
- kotaemon
- Trust report
- ragflow
- Trust report
Typed relationship
kotaemon alternative ragflowBoth kotaemon and ragflow are RAG-based tools focused on interacting with documents using AI, making them alternatives in the chatbot/document interaction space.
Choose kotaemon if…
- kotaemon is primarily Python; ragflow is Go.
- Both kotaemon and ragflow are RAG-based tools focused on interacting with documents using AI, making them alternatives in the chatbot/document interaction space.
- Tags unique to kotaemon: chatbot, llms, open-source.
- Also covers Developer Tools.
- When you need to customize the file types you can process beyond PDFs, HTML, MHTML, and XLSX by opting for the `full` Docker version which includes additional packages from 'unstructured'.
When NOT to use kotaemon
- If you only need to process a limited set of file types (PDF, HTML, MHTML, XLSX) and prefer the smaller Docker image size, as the `lite` version lacks additional package dependencies.
- In environments with strict constraints on external dependencies where installing Docker or Python packages is not feasible.
Choose ragflow if…
- ragflow is primarily Go; kotaemon is Python.
- Both kotaemon and ragflow are RAG-based tools focused on interacting with documents using AI, making them alternatives in the chatbot/document interaction space.
- Tags unique to ragflow: agent-harness, agentic-ai, agentic-retrieval, agentic-search.
- Also covers AI Agents.
- When you need an open-source RAG engine that can integrate with external LLM and embedding services to enhance context management.
When NOT to use ragflow
- If your project does not require integration with external LLM and embedding services, as RAGFlow relies on these services.
- If you are looking for a lightweight solution, as RAGFlow's Docker image is approximately 2 GB in size.
- When you prefer tools that do not require building a Docker image, as RAGFlow's setup involves creating a Docker image.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (Cinnamon/kotaemon) · observed Sep 20, 2026
- GitHub forks (Cinnamon/kotaemon) · observed Sep 20, 2026
- Last push (Cinnamon/kotaemon) · observed Jul 14, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (infiniflow/ragflow) · observed Sep 20, 2026
- GitHub forks (infiniflow/ragflow) · observed Sep 20, 2026
- Last push (infiniflow/ragflow) · observed Sep 18, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Sep 18, 2026
- Trust scan (lockfile / OSV) · observed Sep 18, 2026
GitHub stars on cards: kotaemon 26k · ragflow 91k (synced Sep 20, 2026).
Common questions
- What is the difference between kotaemon and ragflow?
- kotaemon: An open-source RAG-based tool for chatting with your documents.. ragflow: Retrieval-Augmented Generation (RAG) engine with agent capabilities. See the comparison table for live GitHub stats and shared categories.
- When should I choose kotaemon over ragflow?
- Choose kotaemon over ragflow when kotaemon is primarily Python; ragflow is Go; Both kotaemon and ragflow are RAG-based tools focused on interacting with documents using AI, making them alternatives in the chatbot/document interaction space; Tags unique to kotaemon: chatbot, llms, open-source; Also covers Developer Tools; When you need to customize the file types you can process beyond PDFs, HTML, MHTML, and XLSX by opting for the
fullDocker version which includes additional packages from 'unstructured'. - When should I choose ragflow over kotaemon?
- Choose ragflow over kotaemon when ragflow is primarily Go; kotaemon is Python; Both kotaemon and ragflow are RAG-based tools focused on interacting with documents using AI, making them alternatives in the chatbot/document interaction space; Tags unique to ragflow: agent-harness, agentic-ai, agentic-retrieval, agentic-search; Also covers AI Agents; When you need an open-source RAG engine that can integrate with external LLM and embedding services to enhance context management.
- When should I avoid kotaemon?
- If you only need to process a limited set of file types (PDF, HTML, MHTML, XLSX) and prefer the smaller Docker image size, as the
liteversion lacks additional package dependencies. In environments with strict constraints on external dependencies where installing Docker or Python packages is not feasible. - When should I avoid ragflow?
- If your project does not require integration with external LLM and embedding services, as RAGFlow relies on these services. If you are looking for a lightweight solution, as RAGFlow's Docker image is approximately 2 GB in size. When you prefer tools that do not require building a Docker image, as RAGFlow's setup involves creating a Docker image.
- Is kotaemon or ragflow more popular on GitHub?
- ragflow has more GitHub stars (90,923 vs 25,701). Stars measure visibility, not whether either tool fits your constraints.
- Are kotaemon and ragflow open source?
- Yes - both are open-source projects on GitHub (kotaemon: Apache-2.0, ragflow: Apache-2.0).
- Where can I find alternatives to kotaemon or ragflow?
- GraphCanon lists graph-backed alternatives at kotaemon alternatives and ragflow alternatives (kotaemon markdown twin, ragflow 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, kotaemon or ragflow?
- kotaemon: Steady. ragflow: 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 kotaemon and ragflow?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: kotaemon trust report; ragflow trust report.