datamllab/HarborRL
An end-to-end framework for Agentic Reinforcement Learning.
Latest metrics (snapshot 2026-11-02)
| stars | 80 |
|---|---|
| forks | 8 |
| issues | 0 |
| pushed_at | 2026-10-30T07:13:21Z |
History
History chart: 1 daily snapshot so far (archive started 2026-10-14). The line fills in as the daily archive grows; full series via the API.
| Date | GitHub stars |
|---|---|
| 2026-11-02 | 80 |
ADC Momentum: 40/100
| Growth velocity | 37 |
|---|---|
| Consistency | 30 |
| Recency | 87 |
| Agent demand | 0 |
| Basis | bootstrap |
ADC Momentum (0-100) = 45% growth velocity (stars/likes gained per day in our daily archive, relative and absolute, percentile-ranked within the category) + 20% consistency (share of recent days with growth) + 20% recency (latest push/update/release, decaying) + 15% agent demand (how often AI agents looked it up via our API/MCP this week). MCP servers use release cadence; LLMs use price cuts, context and recency. Recomputed daily; scores marked "bootstrap" use lifetime rates until the archive has enough days. Method
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Details
| description | An end-to-end framework for Agentic Reinforcement Learning. |
|---|---|
| url | https://github.com/datamllab/HarborRL |
| language | Python |
| license | MIT |
| topics | agent agent-framework agentic agentic-ai agentrl coding-agent coding-agents dapo grpo harbor harbor-framework llm |
| created_at | 2026-10-12T14:40:53Z |
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