与「ResearchAgents」相关的搜索结果

🚀 Excited to introduce BrainPilot — a human-in-the-loop 1-1-N multi-agent framework designed to accelerate brain science research. 🧠 BrainPilot integrates: • 72 expert skills spanning 7 major neuroscience domains • A curated knowledge base of 7,200+ papers • Automatic review & verification • Trace visualization for transparent, reproducible scientific workflows Our system achieves performance comparable to state-of-the-art agentic frameworks on both Agents' Last Exam and our newly proposed BrainPilotBench. Everything is open source! We'd love for you to ⭐ star the repos, join the community, deploy BrainPilot locally, and adding new features, or proposing new benchmarks. (1/8) 🏠 Homepage: 📄 Technical Report: 🌟 BrainPilot: 📈 BrainPilotBench: #AI# #Neuroscience# #MultiAgent# #AgenticAI# #ScientificAI# #OpenSource# #BrainScience# #LLM# #ResearchAgents# #NeuroAI#

Multi-agent workflows have a scaling problem! The agents inside the same workflow rarely need the same amount of capacity at the same time. Say you have a planner, multiple research agents, and a final synthesis agent. The planner might finish quickly while requests start piling up behind one of the research agents. Scaling the entire application would add capacity everywhere, even though only one part of the workflow actually needs it. CanyonOS is an open-source, workflow-aware platform for multi-agent Python applications and the harness between your agents. Each agent runs as a separate service with its own queue and replica limits, so the busy research agent can get additional capacity while the planner and synthesis agents stay where they are. It also handles communication between the agents and gives you visibility into what happens across the workflow after a request comes in. CanyonOS records the path a request took, including the agents it called, parallel branches, model usage, execution time, and estimated model costs. And you don't need to add tracing decorators or a separate instrumentation SDK throughout your agent code. You still integrate the workflow with CanyonOS, but tracing is handled at the platform layer. I also ran a multi-agent portfolio analysis workflow through it with separate agents working across three stocks. A single request produced 10 traceable steps across the workflow, and I could see which agents ran, where the LLM calls happened, how long each stage took, and the estimated model cost. For multi-agent applications, being able to operate each agent independently while still seeing the complete execution path becomes much more useful as the workflow grows. CanyonOS is open source. Link to the GitHub repo in the comments!

Deep Research and Deep Research Max are our latest autonomous research agents powered by Gemini 3.1 Pro. They can safely navigate both the web and your custom data, like internal docs and specialized financial information, to create professional-grade, fully cited reports. 🧵

聊聊我现在的 vibe setup： - core dev agent：与 cc 的长程深度会话，详尽交互与核心产品 plan，直接在 main 上跑。 - research agents：根据我的 github star 和我自己的 x 书签写各种 plan，提交到 github issue - dev agents：只要存在 issue 必须根据对应的 issue 创建可行分支 - test agents：在所有分支不断 24/7 review 并提高测试覆盖率 - marketing agents：根据 commits 寻找宣传点（外部产品化的就是 shipcast）并制作文字图片和视频素材。 - hook agents：根据各种 webhook 实现 agents 之间的信息传递。 我的核心开发仍然是以和 cc 的长程会话为主，因为我觉得自己想不清楚的问题，让许多 agents 自己去研究也肯定想不清楚，这可能是目前唯一深度参与的 human in the loop 部分。 我最近在写 wanman 一人公司这个产品的时候发现，很多 agents matrix 强调产品的通用性，因为解决的工程问题都是相似的（见上一条推）但在用户真正使用产品的时候得到的交付产品，却存在着极大的不确定性，难以评估交付质量。这个问题我还没想到要如何使用 agents 来实现，我们面向的软件工程，从确定性延续到不确定性的架构，本身需要一段时间去适应，我在想，也许作为用户也在不断适应使用软件的心理预期变化。因此在目前，如果为人类用户设计不确定性较强，但同时又是面向大众需求的产品的时候，领域从极端收敛的方向开始实现，或许是个不错的思路。