Comparison
ragflow vs llama_index
Verdict
Pick ragflow if rAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license; pick llama_index if llamaIndex is a Python-based framework enabling the creation of agentic applications with functionalities like OCR, data indexing, and more. The project promotes flexibility via numerous integrations.
Markdown twin · ragflow alternatives · llama_index alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | ragflow | llama_index |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Very active (0d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 1w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- ragflow
- Retrieval-Augmented Generation engine with agent capabilities
- llama_index
- Leading document agent and OCR platform
Stars
- ragflow
- 87k
- llama_index
- 51k
Forks
- ragflow
- 10k
- llama_index
- 7.9k
Open issues
- ragflow
- 2.0k
- llama_index
- 615
Language
- ragflow
- Go
- llama_index
- Python
Adopt for
- ragflow
- RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license.
- llama_index
- LlamaIndex is a Python-based framework enabling the creation of agentic applications with functionalities like OCR, data indexing, and more. The project promotes flexibility via numerous integrations available on LlamaH
Persona
- ragflow
- -
- llama_index
- -
Runtime
- ragflow
- -
- llama_index
- -
License
- ragflow
- Apache-2.0 License
- llama_index
- MIT
Last pushed
- ragflow
- Jul 31, 2026
- llama_index
- Aug 6, 2026
Categories
- ragflow
- AI Agents, Data & Retrieval
- llama_index
- AI Agents, Data & Retrieval
Trust and health
Open issues (now)
- ragflow
- 2.0k
- llama_index
- 615
Stars delta
- ragflow
- Unknown
- llama_index
- +719 (30d)
Open issues delta
- ragflow
- Unknown
- llama_index
- +121 (30d)
OSV dependency advisories
- ragflow
- Published findings
- llama_index
- No lockfile (source not queried)
Full report
- ragflow
- Trust report
- llama_index
- Trust report
Typed relationship
ragflow alternative llama_indexIn both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach.
Choose ragflow if…
- ragflow is primarily Go; llama_index is Python.
- License: ragflow is Apache-2.0, llama_index is MIT.
- Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services..
- In both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach.
- Tags unique to ragflow: agentic-ai, context management, rag, retrieval-augmented-generation.
- ragflow ships Docker support for self-hosted deployment.
- - You need an integrated RAG system with AI agent capabilities for better context management in your applications.
When NOT to use ragflow
- - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go.
- - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB).
- - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function.
Choose llama_index if…
- llama_index is primarily Python; ragflow is Go.
- License: llama_index is MIT, ragflow is Apache-2.0.
- In both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach.
- Tags unique to llama_index: agents, application, data, fine-tuning.
- - When you need to work with document agents or require advanced OCR capabilities involving multiple formats.
When NOT to use llama_index
- - Avoid using if your primary need is a simple, lightweight solution that doesn't require the extensive OCR or agentic capabilities provided by LlamaIndex.
- - If specific features like 'Parse', 'Extract', and 'Index' are not necessary for your project, simpler alternatives might be more suitable.
- - In scenarios where customization beyond integrating existing plugins isn't required; LlamaIndex's strength lies in its integration library, which may not cover all niche needs without modification.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (infiniflow/ragflow) · observed Aug 1, 2026
- GitHub forks (infiniflow/ragflow) · observed Aug 1, 2026
- Last push (infiniflow/ragflow) · observed Jul 31, 2026
- License file (Apache-2.0) · observed Aug 1, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (run-llama/llama_index) · observed Aug 7, 2026
- GitHub forks (run-llama/llama_index) · observed Aug 7, 2026
- Last push (run-llama/llama_index) · observed Aug 6, 2026
- License file (MIT) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ragflow 87k · llama_index 51k (synced Aug 1, 2026).
Common questions
- What is the difference between ragflow and llama_index?
- ragflow: Retrieval-Augmented Generation engine with agent capabilities. llama_index: Leading document agent and OCR platform. See the comparison table for live GitHub stats and shared categories.
- When should I choose ragflow over llama_index?
- Choose ragflow over llama_index when ragflow is primarily Go; llama_index is Python; License: ragflow is Apache-2.0, llama_index is MIT; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; In both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach; Tags unique to ragflow: agentic-ai, context management, rag, retrieval-augmented-generation; ragflow ships Docker support for self-hosted deployment; - You need an integrated RAG system with AI agent capabilities for better context management in your applications.
- When should I choose llama_index over ragflow?
- Choose llama_index over ragflow when llama_index is primarily Python; ragflow is Go; License: llama_index is MIT, ragflow is Apache-2.0; In both cases, they are tools for Retrieval-Augmented Generation with agent capabilities. However, RagFlow integrates directly into existing workflows, making it an alternative approach; Tags unique to llama_index: agents, application, data, fine-tuning; - When you need to work with document agents or require advanced OCR capabilities involving multiple formats.
- When should I avoid ragflow?
- - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go. - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB). - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function.
- When should I avoid llama_index?
- - Avoid using if your primary need is a simple, lightweight solution that doesn't require the extensive OCR or agentic capabilities provided by LlamaIndex. - If specific features like 'Parse', 'Extract', and 'Index' are not necessary for your project, simpler alternatives might be more suitable. - In scenarios where customization beyond integrating existing plugins isn't required; LlamaIndex's strength lies in its integration library, which may not cover all niche needs without modification.
- Is ragflow or llama_index more popular on GitHub?
- ragflow has more GitHub stars (86,541 vs 51,442). Stars measure visibility, not whether either tool fits your constraints.
- Are ragflow and llama_index open source?
- Yes - both are open-source projects on GitHub (ragflow: Apache-2.0, llama_index: MIT).
- Where can I find alternatives to ragflow or llama_index?
- GraphCanon lists graph-backed alternatives at ragflow alternatives and llama_index alternatives (ragflow markdown twin, llama_index 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, ragflow or llama_index?
- ragflow: Very active. llama_index: 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 ragflow and llama_index?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ragflow trust report; llama_index trust report.