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ragflow

v1.0.0
infiniflow/ragflow

RAGFlow is an agent-native database: a Retrieval-Augmented Generation (RAG) engine that grew into context infrastructure.

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Maintenance
Active2843 commits in 3 months · bus factor 7
License
Apache-2.0Permissive · OSI-approved
Security
8 advisories5 critical, 3 high
Supported versions
No scheduleendoflife.date does not track this project

94.2k stars · 11.3k forks · 1.3k open issues · last release 9 days ago · data checked 4 hours ago

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Breaking changes

What to check before upgrading, taken from the notes of the last 30 releases.

  • v0.22.0Nov 12, 2025

    Breaking Changes

    • From this release onwards, we ship only the slim edition (without embedding models) Docker image and no longer append the -slim suffix to the image tag.

Release history

All releases on GitHub
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BadgeLatest version of infiniflow/ragflow[![version](/badge/infiniflow/ragflow)](/p/infiniflow/ragflow)

After three release candidates, we are pleased to announce the official release of RAGFlow 1.0, now ready for production. Built on a complete rewrite in Go, this release significantly reduces deployment and runtime overhead and introduces an all-new Agentic Chat experience. We encourage all users to upgrade for improved stability, performance, and question answering.

Highlights

  • Rewritten in Go for lighter deployment: Docker images are approximately 10× smaller, and startup is approximately 10× faster.
  • Approximately 100× greater concurrency, with substantial improvements in query latency, memory usage, and document parsing speed.
  • All-new Agentic Chat: Enables multi-step reasoning and answers to complex questions through iterative retrieval, tool calls, and evidence sufficiency assessment.

Chat and Agents

  • Added Agentic Chat mode, replacing the previous Low, Medium, High, and Ultra thinking levels.
  • Introduced the Jev decision model into the agent tool-calling loop to determine whether the available evidence is sufficient to answer a question.
  • Added support for the Jev decision model in the Categorize agent component.
  • Added storage and display of prompts, token usage, and execution time in chat history.

Document Ingestion and Retrieval

  • Unified the 12 built-in document parsers with the pipeline parsers.
  • Added support for multiple ingestion pipeline DSL versions.

Architecture and Deployment

  • Migrated the message queue from Redis to NATS.
  • Adopted Kvrocks for cache and checkpoint storage.
  • Added Docker images for the arm64 architecture.
  • Reimplemented the RAGFlow CLI in Go.

Stability

  • Fixed multiple critical bugs reported by the community and issues identified during the release candidate phase, further improving runtime stability.

Benchmarks

  • Achieved 91% accuracy on the FRAMES multi-hop question-answering benchmark using RAGFlow Agentic Chat with MiniMax M3.

Upgrading

  • From v1.0.0-rc1, rc2, or rc3: Existing data is fully compatible. You can upgrade directly.
  • From v0.27.2: You can upgrade directly to v1.0.0. Data migration runs automatically when the RAGFlow Docker container starts.
  • From versions earlier than v0.27.2: Follow the migration guide to complete the required intermediate upgrades before upgrading to v1.0.0.
  • Back up all data before upgrading. Data migration from v0.27.2 to v1.0.0 is irreversible. If migration fails, restore the complete pre-upgrade backup before trying again.

Compatibility and Limitations

  • Deprecated API aliases are no longer supported.
  • The Python SDK is unaffected.
  • DeepDoc currently supports CPU environments only.

Relative references

What's Changed

Full release notesCompare with previous