Commonstack

Excited to introduce AgenticTrading! 🚀 An open-source experimental playground for LLM-powered trading agents. Build, test, and deploy agents that reason, trade, and perform in realistic market environments. We are thrilled to collaborate with Dr. Xiaoyang Liu's group at Columbia University to push the boundaries of AI in finance. Stay tuned for our findings. Explore our platform: agentic-trading-lab.vercel.app Read our Medium post: @kuailedefcl/agentic-trading-part-1-build-a-simple-trading-agent-with-commonstack-apis-42b413365906" target="_blank" rel="nofollow noopener">medium.com/@kuailedefcl/a… GitHub Repo: github.com/Open-Finance-L… We welcome more collaborators to join us! Let's shape the future of open finance together. 🌟 #AgenticTrading #AIinFinance #LLM #AlgorithmicTrading #OpenSource

Excited to share that TwinRouterBench has been accepted to the #RLEval Workshop at #CAIS2026 🎉 As LLM apps become long-horizon agents, one request can trigger many model calls across planning, tool use, retrieval, coding, and verification. That makes per-step LLM routing a core infrastructure problem: sending each call to the cheapest sufficient model without breaking downstream success. TwinRouterBench introduces: ⚡ Static track: 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench 🚀 Dynamic track: live SWE-bench Verified evaluation with official task resolution + realized API spend Key result: a router trained on static labels achieves comparable SWE-bench resolve rate while cutting API cost by ~53% vs. an unrouted Opus 4.6 baseline. Paper: arxiv.org/html/2605.1885… Code: github.com/CommonstackAI/… Dataset: huggingface.co/datasets/Amorp… Website: commonstackai.github.io/TwinRouterBenc… #LLM #AgenticAI #LLMRouting #Benchmark #SWEBench

Excited to share that TwinRouterBench has been accepted to the #RLEval Workshop at #CAIS2026 🎉 As LLM apps become long-horizon agents, one request can trigger many model calls across planning, tool use, retrieval, coding, and verification. That makes per-step LLM routing a core infrastructure problem: sending each call to the cheapest sufficient model without breaking downstream success. TwinRouterBench introduces: ⚡ Static track: 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench 🚀 Dynamic track: live SWE-bench Verified evaluation with official task resolution + realized API spend Key result: a router trained on static labels achieves comparable SWE-bench resolve rate while cutting API cost by ~53% vs. an unrouted Opus 4.6 baseline. Paper: arxiv.org/html/2605.1885… Code: github.com/CommonstackAI/… Dataset: huggingface.co/datasets/Amorp… Website: commonstackai.github.io/TwinRouterBenc… #LLM #AgenticAI #LLMRouting #Benchmark #SWEBench