Status basis: github_stars momentum fell to 0 per 3d from 13 (<= 40% of the prior window)
- Cooling github_stars momentum fell to 0 per 3d from 13 (<= 40% of the prior window)
- Cooling github_stars momentum fell to 0 per 3d from 26 (<= 40% of the prior window)
- Cooling github_stars momentum fell to 6 per 3d from 192 (<= 40% of the prior window)
- Cooling github_stars momentum fell to 13 per 3d from 185 (<= 40% of the prior window)
- Cooling github_stars momentum fell to 26 per 3d from 172 (<= 40% of the prior window)
- Accelerating github_stars gained 192 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Reactivated github_stars did not move for 7d, then gained 192 in the last 3d
- Cooling github_stars momentum fell to 26 per 3d from 172 (<= 40% of the prior window)
- Accelerating github_stars gained 185 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Reactivated github_stars did not move for 7d, then gained 185 in the last 3d
- Accelerating github_stars gained 173 in the last 3d after a flat 3d baseline (no prior movement to compare a ratio against)
- Reactivated github_stars did not move for 7d, then gained 173 in the last 3d
- Anomaly daily change 11.0 is -3.3 standard deviations from this entity's own prior mean (14.3±0.0, baseline excludes this point) — unusual, not necessarily adoption
- first observed Kanon started keeping permanent history for this agent
| Machine-callable | — | unknown | — |
| Open source | 1 | verified | 1 quote(s) |
| Self-hostable | — | unknown | — |
| Bring your own key | — | unknown | — |
| Autonomy level | 2 | inferred | 3 quote(s) |
| Pricing model | — | unknown | — |
| Integrations | mcp | inferred | 1 quote(s) |
“Unknown” means we have not verified it — it is not a “no”. Hard filters never treat unknown as false.
Its end-to-end agentic RAG architecture provides a reusable blueprint for enterprise AI applications, setting a strong standard for B2B intelligent systems.
Evidence quotesverbatim, from the product’s own materials
“public GitHub repository (README fetched)”— structural
“MCP 工具发现、提参与校验”— readme
“可编排入库 Pipeline、远程刷新、回答溯源”— readme
“会话记忆：最近 N 轮消息结合持久化摘要”— readme
“以及 MCP 工具发现、提参与校验。”— readme
FAQ
What is ragent?
ragent enables enterprises to build intelligent knowledge systems with full automation—from document parsing to tool use—without starting from scratch.
What does ragent do?
Enable enterprises to automate the full lifecycle of intelligent knowledge systems, including document parsing, retrieval, intent recognition, and tool calling.
Why does ragent matter?
Its end-to-end agentic RAG architecture provides a reusable blueprint for enterprise AI applications, setting a strong standard for B2B intelligent systems.
How much does ragent cost?
Custom
Is ragent open source?
Yes — ragent is open source.
What does ragent integrate with?
mcp
How popular is ragent?
As tracked by KanonAgent: 6 upvotes (first indexed 2026-07-15).
What are the best ragent alternatives?
Similar AI agents tracked by KanonAgent: Winninghunter, Stealth Venture, Hidden Business, ragflow, Dropkiller, DataExpert / TechCreator.