RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context layer for LLMs
The week focused on porting agentic RAG capabilities to Go and exposing them as a chat mode, alongside per-dialog failover support. Beyond that feature work, the team strengthened security posture across agents, MCP, and HTTP handling by rejecting unvalidated redirects and unsafe retries, while…
Get this in your inbox every Monday →A deterministic 0–100 hygiene score — README, license, CI, tests, docs, and freshness.
Who ships this repo — author concentration and the bus factor across the last 300 mainline commits.
How welcoming this repo is to contributors — issue throughput, close time, responsiveness, and good-first-issue count.
What this project is built on — dependency count by ecosystem, the license mix, and anything worth a legal look before you adopt it.
Whether this project's CI can be trusted — pass rate, run times, flaky runs, and which workflow is the weak link.
Grounded in ragflow's README, structure, and recent commits — answers won't invent code they haven't seen.
A Monday email with what shipped, in plain English — no account needed.
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feat(agent,ingestion): carry the grep/bm25 contracts and index declared titles (#20542)
Port agentic RAG to Go, expose it as a chat mode, and add per-dialog failover (#20503)
Fix: The separator line causes the font size of the agent message error text to be too large. (#20462)
A floor, not a guess: counts only commits whose author, co-author trailer, or message explicitly credits an AI tool (Claude, Copilot, Cursor, aider, Codex…). Based on 30 mainline commits. Unattributed AI code isn't counted here — the full audit estimates that separately.
