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Agent工作流

AIR-Agent

基于 Python · 无代码搭建完整 AI 自动化流程

⭐ 6 Stars 💻 Python 📄 MIT 🏷 AI 8.0分

8.0AI 综合评分

ai-agentapicrawlerdatabasepython

⬇ 下载源码 ZIP ⚙️ 配置说明 📺 TG 频道

✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估，AIR-Agent 获评「强烈推荐」。这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色，AI 评分 8.0 分，适合有一定技术背景的用户使用。

📚 深度解析

AIR-Agent 是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升，基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化（模拟鼠标键盘操作），AI Agent 工作流通过理解任务意图、动态规划执行路径，能够处理更复杂的非结构化任务。

AIR-Agent 工作流的设计遵循"最小配置，最大复用"原则：核心逻辑已经封装好，用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制，在网络波动或 API 限速等情况下仍能稳定运行，适合作为生产环境的自动化基础设施。

在实际部署时，建议先在测试环境中运行 3-5 次，验证各个环节的输出结果符合预期，再部署到生产环境。AI Skill Hub 评分 8.0 分，是同类 Agent 工作流中的精选推荐。

📋 工具概览

AIR-Agent 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排，将复杂的多步骤任务拆解为清晰的自动化流程，实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成，适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

GitHub Stars

⭐ 6

开发语言

Python

支持平台

Windows / macOS / Linux

维护状态

轻量级项目，按需更新

开源协议

MIT

AI 综合评分

8.0 分

工具类型

Agent工作流

Forks

—

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理，如需查看完整原始文档请访问底部「原始来源」。

📌 核心特色

可视化 Agent 工作流编排，无需编写复杂代码
支持多步骤自动化任务链，实现全流程无人值守
与外部 API、数据库和第三方服务无缝集成
内置错误处理与自动重试机制，保障稳定运行
提供可复用的自动化模板，快速在同类场景部署

🎯 主要使用场景

自动化日常重复性工作，将精力集中于创造性任务
构建数据采集 → 处理 → 输出的完整自动化管线
实现跨平台、跨系统的数据流转和业务协同

以下安装命令基于项目开发语言和类型自动生成，实际以官方 README 为准。

安装命令

# 方式一：pip 安装（推荐）
pip install air-agent

# 方式二：虚拟环境安装（推荐生产环境）
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install air-agent

# 方式三：从源码安装（获取最新功能）
git clone https://github.com/ShiYu0318/AIR-Agent
cd AIR-Agent
pip install -e .

# 验证安装
python -c "import air_agent; print('安装成功')"

📋 安装步骤说明

访问 GitHub 仓库获取工作流文件
在对应平台（Dify / Flowise / Make 等）中找到「导入工作流」功能
上传工作流文件
按照提示配置必要的环境变量和 API Key
运行测试确认流程正常后投入使用

以下用法示例由 AI Skill Hub 整理，涵盖最常见的使用场景。

常用命令 / 代码示例

# 命令行使用
air-agent --help

# 基本用法
air-agent input_file -o output_file

# Python 代码中调用
import air_agent

# 示例
result = air_agent.process("input")
print(result)

以下配置示例基于典型使用场景生成，具体参数请参照官方文档调整。

配置示例

# air-agent 配置文件示例（config.yml）
app:
  name: "air-agent"
  debug: false
  log_level: "INFO"

# 运行时指定配置文件
air-agent --config config.yml

# 或通过环境变量配置
export AIR_AGENT_API_KEY="your-key"
export AIR_AGENT_OUTPUT_DIR="./output"

📑 README 深度解析真实文档完整度 90/100 查看 GitHub 原文 →

以下内容由系统直接从 GitHub README 解析整理，保留代码块、表格与列表结构。

简介

Overview

RAGency keeps you on top of fast-moving AI research without the daily manual grind. It gathers the latest papers and community discussion from many sources, distills each item into a concise summary, and builds a searchable knowledge base you can query in natural language. Retrieval spans both a dense/sparse vector index and a citation/concept graph, so the system answers specific questions and reasons about a field as a whole.

The primary interface is an interactive web dashboard (React + FastAPI, GitHub-dark aesthetic, EN/ZH bilingual): streaming Q&A with citations, shareable conversations, interactive D3 citation/concept graphs, deep-research and writing tools, a reading kanban with RSS feeds and exports, trend analytics, learning paths, and per-user notification scheduling. A Discord bot remains as a secondary interface sharing the same core, with a scheduled daily digest and slash commands. User interactions feed a preference reward model that continuously tunes recommendation ranking.

The project runs on free, local components where possible: Groq (OpenAI-compatible) for generation, sentence-transformers for local embeddings, and FAISS for vector search. Heavier options (BGE embeddings and rerankers, HNSW indexing, Postgres + pgvector, OpenAlex citation data, PDF full-text ingestion) are available behind configuration switches and degrade gracefully when their services or models are unavailable.

Features

Prerequisites

Groq API key — https://console.groq.com (free tier works)
Python 3.13 + uv, or Docker
Discord bot token (optional, only for the bot) — https://discord.com/developers/applications
Node.js 22+ (optional, only for frontend development)

Required — dashboard

GROQ_API_KEY=your-groq-api-key

Required only for the Discord bot

DISCORD_BOT_TOKEN=your-bot-token DISCORD_CHANNEL_ID=your-channel-id

Option 1: Docker Compose (recommended)

Single container — the image bakes the frontend build and FastAPI serves it:

```bash

Deployment switches

SCHEDULER_ENABLED=1 # per-user digests and reminders (compose sets this) STORE_BACKEND=sqlite # or postgres (+ DATABASE_URL) ```

Everything else is optional and safely skipped when unset — the full reference:

Variable	Required	Description
`GROQ_API_KEY`	yes	Groq API key ([console.groq.com](https://console.groq.com))
`GROQ_MODEL`		Model id (default `llama-3.3-70b-versatile`)
`DISCORD_BOT_TOKEN`	bot	Discord bot token (required only for the bot)
`DISCORD_CHANNEL_ID`	bot	Channel id for the daily push
`DISCORD_GUILD_ID`		Guild id for instant slash-command sync (else global sync)
`ARXIV_QUERY`		arXiv query (default `cat:cs.AI`)
`DAILY_COUNT` / `REPORT_COUNT`		Papers fetched per daily push / per report
`PUSH_HOUR` / `PUSH_MINUTE` / `PUSH_TZ_OFFSET`		Default daily push time and timezone offset
`EMBED_MODEL`		Embedding model (default `all-MiniLM-L6-v2`, or `BAAI/bge-m3`)
`INDEX_TYPE` / `HNSW_M`		Vector index: `flat` (exact) or `hnsw` (approximate)
`RERANK_ENABLED` / `RERANK_MODEL`		Enable BGE cross-encoder reranking
`TELEGRAM_BOT_TOKEN` / `TELEGRAM_CHAT_ID`		Enable Telegram delivery
`SMTP_HOST` / `SMTP_PORT` / `SMTP_USER` / `SMTP_PASSWORD` / `SMTP_FROM` / `EMAIL_TO`		Enable Email delivery
`LINE_CHANNEL_TOKEN` / `LINE_TO`		Enable LINE delivery (Messaging API)
`GITHUB_TOKEN`		Optional, raises GitHub API rate limits
`X_BEARER_TOKEN`		Enables the X/Twitter crawler (X API v2 requires a paid plan)
`JWT_SECRET`		Dashboard auth secret (ephemeral if unset; set it in production)
`JWT_EXPIRE_MINUTES`		Token lifetime (default 7 days)
`CORS_ORIGINS` / `API_PUBLIC_URL` / `FRONTEND_URL`		Dashboard URLs (defaults fit local dev)
`GOOGLE/GITHUB/DISCORD_CLIENT_ID/SECRET`		OAuth sign-in and Discord account linking
`STORE_BACKEND` / `DATABASE_URL`		`sqlite` (default) or `postgres` with pgvector
`SCHEDULER_ENABLED`		Per-user digest/reminder scheduler (compose sets it to 1)

The arXiv, news, Hacker News, Reddit, and GitHub crawlers work without credentials. Telegram/Email/LINE delivery, OAuth providers, and the X/Twitter crawler activate only once their keys are set. Changing EMBED_MODEL changes vector dimension; the store detects this and rebuilds the index automatically.

Quick start

Usage

The dashboard is self-explanatory after First steps; every page is described in Web dashboard, and the full REST surface in API reference.

2. Set up environment variables

cp backend/.env.example backend/.env

Edit backend/.env with your keys (GROQ_API_KEY at minimum)

Option 2: Local development

```bash

Configuration

Settings live in backend/.env (never committed). The minimum working configuration:

```bash

Setting up the Discord bot

1. Create an application at the Discord Developer Portal. 2. Under Bot -> Reset Token, copy the token into DISCORD_BOT_TOKEN. 3. Under OAuth2 -> URL Generator, select scopes bot and applications.commands, grant Send Messages, Read Message History, and Embed Links, and use the generated URL to invite the bot. 4. Enable Developer Mode in Discord to copy the channel and guild IDs.

Web + API: http://localhost:8000

API docs: http://localhost:8000/docs

```

For Postgres, also set STORE_BACKEND=postgres and DATABASE_URL in backend/.env.

Backend (dashboard API)

cd backend uv sync # install dependencies (includes PyTorch; first run is slow) cp .env.example .env # then fill in your keys uv run python main.py api # dashboard API at :8000 (serves frontend/dist if built) uv run python main.py bot # or: the Discord bot uv run python main.py all # or: both at once

Frontend (in another terminal, hot reload; Vite proxies /api to :8000)

cd frontend npm install npm run dev # UI at http://localhost:5173


`uv run` uses the project virtualenv automatically. If you prefer an activated shell:

bash source backend/.venv/bin/activate # then run: python main.py api ```

API reference

All endpoints are also browsable live at /docs (Swagger UI) and /redoc. Unless marked public, endpoints require Authorization: Bearer <JWT> obtained from register/login or OAuth. Streaming endpoints return Server-Sent Events (data: {json}\n\n frames).

Papers and library (`/api`)

Method	Path	Description
GET	`/api/papers`	List papers (`?limit=&source=&query=`) with reproducibility signals
GET	`/api/paper/{id}`	Paper detail with credibility (OpenAlex) and reproducibility
POST	`/api/daily`	Fetch today's arXiv papers, store and index them
GET	`/api/daily/personalized`	Papers ranked against your interaction profile
POST	`/api/interactions`	Log an interaction (`like`, `click`, ...) for recommendations (201)
GET	`/api/reading`	Reading kanban items (`?state=to-read\\|reading\\|done`)
POST	`/api/reading`	Add a paper to the kanban (201)
PATCH	`/api/reading/{paper_id}`	Move between states
DELETE	`/api/reading/{paper_id}`	Remove from the kanban (204)
GET	`/api/export/csv`	Export the library as CSV
GET	`/api/export/bibtex`	Export as BibTeX
GET	`/api/export/obsidian`	Export as an Obsidian-ready Markdown zip

Feeds and subscriptions (`/api`)

Method	Path	Description
GET	`/api/feeds`	Your RSS feeds
POST	`/api/feeds`	Add a feed (201; 409 on duplicate)
PATCH	`/api/feeds/{id}`	Update title/category/enabled
DELETE	`/api/feeds/{id}`	Remove a feed (204)
POST	`/api/feeds/refresh`	Fetch all enabled feeds into the library
GET	`/api/subscriptions`	Your keyword subscriptions
POST	`/api/subscriptions`	Add a keyword subscription (201)
DELETE	`/api/subscriptions/{name}`	Remove one (204)

Graph (`/api/graph`)

Method	Path	Description
GET	`/api/graph/citation?seed=`	Citation network around a seed (arXiv id or title), D3 nodes/edges + PageRank + communities
GET	`/api/graph/concept`	Concept graph over the library (`?refresh=1` rebuilds)
GET	`/api/graph/global?query=`	Community-summary map-reduce answer for corpus-level questions

Research and writing (`/api`)

Method	Path	Description
POST	`/api/deepresearch`	SSE — decompose → per-question research → synthesis; events: `decompose`, `section`, `synthesis`, `citations`, `done`
POST	`/api/litreview`	Literature review over retrieved papers
POST	`/api/compare`	Multi-paper method comparison table
POST	`/api/report`	Structured topic report with citations
POST	`/api/bibtex`	BibTeX for retrieved papers
POST	`/api/explain`	Guided plain-language deep-read of one paper
POST	`/api/write/{tool}`	Writing tools: `polish`, `contributions`, `review`, `checklist`, `latex`, `slides`

Insights (`/api`)

Method	Path	Description
GET	`/api/trends`	Rising keywords (slope-ranked) + top keywords (`?granularity=month\\|year&top=`)
GET	`/api/trends/{keyword}`	One keyword's time series and next-period forecast
GET	`/api/digest/weekly`	Weekly digest: top recent papers, keywords, LLM overview
GET	`/api/analytics`	Your activity, action totals, reading pipeline, top topics (`?days=`)

Notifications, reminders, learning (`/api`)

Method	Path	Description
GET	`/api/notifications/preferences`	Your notification preferences
PUT	`/api/notifications/preferences`	Update them; the scheduler reschedules immediately
GET	`/api/reminders`	Open reminders (`?include_done=true` for all)
POST	`/api/reminders`	Create a reminder (201)
POST	`/api/reminders/{id}/complete`	Mark done
DELETE	`/api/reminders/{id}`	Delete (204)
GET	`/api/learning-paths`	Your learning paths
POST	`/api/learning-paths`	Generate a path for a topic (LLM, retrieval fallback) (201)
PATCH	`/api/learning-paths/{id}`	Update items/progress/topic
DELETE	`/api/learning-paths/{id}`	Delete (204)
GET	`/api/skills`	Your skill levels
PUT	`/api/skills`	Set a skill level (0-100)

System and extras (`/api`)

Method	Path	Description
GET	`/api/health`	Store stats, scheduler status, provider-key readiness (booleans only) — public
GET	`/api/sources`	Data-source configuration status
GET	`/api/memory`	Your agent memory items (`?kind=&contains=&limit=`)
POST	`/api/memory`	Add a memory item (201)
POST	`/api/eval`	RAG evaluation — `engine=offline` (default): precision@k, recall, MRR, lexical faithfulness; `engine=judge`: RAGAS-style LLM-judged faithfulness, answer relevancy, context precision/recall (503 without `GROQ_API_KEY`)
POST	`/api/agent`	Tool-calling agent (503 without `GROQ_API_KEY`)

Research workflow tools

- Literature review generation with identified research gaps. - BibTeX export with generated citation keys. - Method comparison tables across papers. - Guided deep-read explanations of dense papers. - Credibility and impact signals from citation counts. - Reproducibility signals from linked code repositories. - Reading kanban (to-read / reading / done) with drag-and-drop, and topic subscriptions. - Obsidian export — render papers and links as Markdown notes with frontmatter and wikilinks for Obsidian, Juggl, and Dataview; CSV and BibTeX exports alongside. - Writing assistance — LaTeX drafts, slide outlines, polishing, contribution extraction, review suggestions, and submission checklists.

Retrieval and question-answering

- Hybrid retrieval — dense vector search (FAISS, optional HNSW) fused with BM25 lexical search via Reciprocal Rank Fusion. - Query transformation — HyDE, multi-query rewriting, and question decomposition to widen recall. - Cross-encoder reranking — optional BGE reranker for second-stage precision. - Parent-document retrieval — retrieve focused child chunks, return their parent papers. - Contextual chunk embedding — situate each chunk within its document before embedding. - Semantic answer cache — short-circuit near-duplicate questions. - Traceable citations — chunk-level source markers resolve back to paper, section, and link, with a citation-accuracy metric to score grounding.

Q&A and conversations (`/api`)

Method	Path	Description
POST	`/api/ask`	SSE — streamed answer over the library; events: `conversation`, `token`, `citations`, `done`
GET	`/api/conversations`	List conversations (`?query=` searches titles and messages)
GET	`/api/conversations/{id}`	One conversation with messages and citations
PATCH	`/api/conversations/{id}`	Rename
DELETE	`/api/conversations/{id}`	Delete (204)
POST	`/api/conversations/{id}/share`	Create a public share link, returns `{token, url}`
GET	`/api/shared/{token}`	Read a shared conversation — public