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OpenPipe/ART

Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!

Latest metrics (snapshot 2026-10-14)

stars10801
forks1007
issues167
pushed_at2026-10-12T20:55:34Z

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.

DateGitHub stars
2026-10-1410801

ADC Momentum: 52/100

ADC Momentum 52 Leaderboard

Growth velocity55
Consistency44
Recency91
Agent demand0
Basisbootstrap

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

descriptionAgent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
urlhttps://github.com/OpenPipe/ART
languagePython
licenseApache-2.0
topicsagent
agentic-ai
grpo
llms
lora
qwen
qwen3
reinforcement-learning
rl
created_at2025-03-10T18:25:47Z
homepagehttps://art.openpipe.ai

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