- Teams building RAG systems that need to handle complex documents with tables, images, and multi-column layouts
- Organizations deploying document Q&A over PDFs, contracts, reports, and technical documentation
- Engineers who want an all-in-one RAG solution with document processing, retrieval, and agent orchestration
Memory Systems
ragflow
Open-source Retrieval-Augmented Generation engine that combines deep document understanding with agent capabilities.
82K Stars
Apache-2.0 License
9.4K Forks
Open source
About
ragflow overview
RAGFlow is an open-source RAG engine that goes beyond simple vector search by combining deep document understanding, layout analysis, and agent-based orchestration. It processes complex documents (PDFs, images, tables) with layout-aware parsing, then uses agent capabilities to route, filter, and augment retrieval results — creating a production-ready context layer for LLM applications.
Rag
ragflow surfaces rag as a core capability in its published project metadata and source links.
This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs.Workflow
ragflow surfaces workflow as a core capability in its published project metadata and source links.
This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs.Memory
ragflow surfaces memory as a core capability in its published project metadata and source links.
This gives readers a starting point for evaluating whether the project fits their workflow before visiting the source repository or docs. Use cases
When to use ragflow
Personal memory
Use it as a candidate for personal memory when the project facts, license, and official links match your deployment requirements.
Compare
How it compares
When to choose ragflow
Compare it with nearby memory systems by looking at hosting model, integration surface, license, and whether the official docs show the workflow you need.
FAQ
Questions
What types of documents can RAGFlow process?
RAGFlow processes PDFs, images, Office documents, and other complex formats with layout-aware parsing that preserves tables, headers, and multi-column structure.
Does RAGFlow include agent capabilities?
Yes, RAGFlow combines RAG with agent orchestration for intelligent routing, filtering, and result augmentation.
Is RAGFlow open source?
Yes, it is open source under the Apache-2.0 license with 81K+ GitHub stars.
Can RAGFlow be self-hosted?
Yes, RAGFlow is designed for self-hosted deployment with Docker support.
Tags
Capabilities
Decision brief
JSON
Should you use ragflow?
- Simple vector search use cases where basic chunking and embedding are sufficient
- Teams that prefer to assemble RAG pipelines from individual components rather than using an integrated platform
- Verified 2026-06-03
- License: Apache-2.0
- Repo: infiniflow/ragflow
- Open-source signal
Deployment
cloud
Permission surface
memory
Decision signals
No extra signals recorded
Agent packet
Structured decision data for ragflow
This packet is the compact machine-readable view agents should use before following source links or taking action.
Capabilities
rag, workflow, memory
Constraints
open source
Deployment
cloud
Permission surface
memory
Recommended workflows
Memory or RAG workflow
Overview
What ragflow does
What it is
RAGFlow is an open-source RAG engine that combines deep document understanding with agent capabilities. It processes complex documents with layout-aware parsing and uses agent orchestration for production-quality retrieval.
Why it matters
RAGFlow is the most popular open-source RAG engine (81K+ stars) specifically because it handles the hardest part of RAG: extracting quality content from complex documents.
How to evaluate it
Evaluate ragflow by starting from the official sources, checking its repo interface surface, and running one narrow workflow before expanding scope. Recorded integrations include memory systems.
Facts
Known metadata and operating surface
These fields are separated from editorial interpretation so agents can reason over facts and missing checks.
Resource type memory system
Category Memory Systems
Maturity active
Difficulty Unknown
License Apache-2.0
Pricing open source
Verified 2026-06-03
Source confidence high
Risk level low
Fit matrix
Where ragflow fits in an agent stack
strong
Memory or RAG workflow
ragflow has multiple signals for memory or rag workflow, including matching tags, capabilities, category, or positioning.
- Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Browser automation
ragflow has at least one signal for browser automation, but should be checked against a real task before adoption.
- Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Coding agent workflow
ragflow has at least one signal for coding agent workflow, but should be checked against a real task before adoption.
- Run a small repository change and inspect the diff, tests, and rollback path.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Evaluation and observability
ragflow has at least one signal for evaluation and observability, but should be checked against a real task before adoption.
- Add one repeatable test case and confirm results can run again in review or CI.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial
Reusable skill workflow
ragflow has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.
- Run one skill end to end and check whether it produces evidence or structured output.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
weak
Connector or protocol layer
ragflow is not primarily positioned for connector or protocol layer in the current metadata.
- Connect one low-risk service, then inspect schemas, auth scope, errors, and logs.
- Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs
What an agent should inspect
Likely inputs
- Repositories, files, issues, terminal output, and test results
- Documents, user facts, entities, context, or retrieval queries
- Official setup instructions and a small real workflow
Likely outputs
- Diffs, commits, explanations, test results, or review notes
- Retrieved context, memory updates, graph relations, or citations
- Scores, traces, regression results, dashboards, or failure cases
- A decision on whether this resource fits the target workflow
Evidence
Sources, claims, and missing checks
Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.
GitHub github
Repository source for code, license, issues, releases, and implementation details.
Homepage homepageOfficial or project-controlled source for this resource profile.
Source githubRepository source for code, license, issues, releases, and implementation details.
ragflow is listed as open source.
License metadata: Apache-2.0ragflow has a recorded GitHub repository: infiniflow/ragflow.
Resource facts and GitHub source link.ragflow supports these recorded deployment modes: cloud.
OpenAgent decision signal metadata.ragflow is tagged with rag, workflow, memory capabilities.
OpenAgent capability taxonomy.- Dedicated docs link is missing.
- Repository freshness has not been recorded.
Next action
How to start evaluating ragflow
Inspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open sourceOpen Homepage
Start from the official source before adopting third-party instructions.
Open sourceInspect repository
Check license, recent activity, issues, examples, and security-sensitive code paths.
Open source Compare
Alternatives and nearby resources
Use related resources to compare category fit, license, deployment model, and first-workflow behavior.
FAQ
Common questions about ragflow
What types of documents can RAGFlow process?
RAGFlow processes PDFs, images, Office documents, and other complex formats with layout-aware parsing that preserves tables, headers, and multi-column structure.
Does RAGFlow include agent capabilities?
Yes, RAGFlow combines RAG with agent orchestration for intelligent routing, filtering, and result augmentation.
Is RAGFlow open source?
Yes, it is open source under the Apache-2.0 license with 81K+ GitHub stars.
Can RAGFlow be self-hosted?
Yes, RAGFlow is designed for self-hosted deployment with Docker support.