{"repo":"blurryface13/asteria-agent","free":true,"listed":false,"github":"https://github.com/blurryface13/asteria-agent","clone":"git clone https://github.com/blurryface13/asteria-agent.git","description":"Local-first AI research assistant: plans sub-queries, searches the web, and writes fully cited reports. LangChain + LangGraph multi-agent + FastAPI + Next.js, with email-OTP auth and per-user Postgres storage.","language":"Python","stars":43,"topics":["ai-agent","deepseek","fastapi","langchain","langgraph","llm","nextjs","research-assistant"],"license":"Apache-2.0","category":"ai-agents","readme_excerpt":"🐰 Asteria Agent 本地优先的 AI 调研助手:输入一个问题,它会自动拆解子查询、联网检索、阅读来源,产出一份带真实引用的调研报告——生成后还可以继续与报告对话。 ✨ 功能特性 特性 说明 --- --- --- 🔍 可溯源调研 子查询拆解 → 检索抓取 → embedding 相关性过滤 → 带引用写作,引用来自真实访问记录而非 LLM 输出;报告导出 Markdown / Word / PDF 🤖 多智能体模式 LangGraph StateGraph 编排研究角色,支持大纲人工审核与章节级并行 🔐 多用户 邮箱验证码 + JWT 鉴权,调研历史按用户隔离存储于 PostgreSQL 📡 实时交互 WebSocket 流式推送调研进度;可对已生成报告继续追问 🔌 模型无关 LLM 与 embedding 均走 OpenAI 兼容规范,任意 provider(含本地 Ollama)一行配置切换 🏗️ 架构 代码库沿一条清晰的边界拆分: - asteria researcher/ — 自包含的调研引擎(检索、抓取、prompt、LLM 抽象、报告写作),完全不感知 Web 层,可作为纯 Python 库独立使用 - backend/ — 包在引擎外面的 FastAPI 服务层:路由、鉴权、WebSocket 推送、PostgreSQL 持久化 - frontend/nextjs/ — Web 界面:调研控制台、实时日志、报告阅读、对话 - multi agents/ — LangGraph 多智能体工作流(planner → 人工审核 → 并行 researcher → writer → fact-checker) 🗺️ Roadmap - [ ] 实验室知识库(RAG) — 基于 pgvector 对内部文献与笔记做混合检索(BM25 + dense + RRF 融合),cross-encoder 重排序,封装为 MCP server 供本应用与其他 agent 共同调用 - [ ] 检索评估集与指标对比(hybrid vs. dense-only) - [ ] 生产部署(Docker、 next build 、云主机) - [ ] 成本看板(单次调研的 token / 费用明细) 🙏 致谢 基于优秀的开源项目 GPT Researcher 的思路与实现模式构建,并围绕本地优先工作流、按用户持久化与不同的鉴权/存储架构进行了重塑。","default_branch":null,"files":null,"tree":[],"storefront":"/r/blurryface13","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/blurryface13/asteria-agent/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}