AgenticRAG-Survey Agent工作流
经 AI Skill Hub 精选评估，AgenticRAG-Survey Agent工作流 获评「强烈推荐」。已获得 1.6k 颗 GitHub Star，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 8.2 分，适合有一定技术背景的用户使用。
📚 深度解析
AgenticRAG-Survey Agent工作流 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 8.2 分，是同类 Agent 工作流中的精选推荐。
📋 工具概览
深度探索智能体增强的检索增强生成系统，提供完整的Agentic-RAG框架设计、模式实现和最佳实践。适合AI工程师、LLM应用开发者研究和构建高级RAG系统。
AgenticRAG-Survey Agent工作流 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。
📖 中文文档
深度探索智能体增强的检索增强生成系统，提供完整的Agentic-RAG框架设计、模式实现和最佳实践。适合AI工程师、LLM应用开发者研究和构建高级RAG系统。
AgenticRAG-Survey Agent工作流 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。
- 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
- 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
# 克隆仓库 git clone https://github.com/asinghcsu/AgenticRAG-Survey cd AgenticRAG-Survey # 查看安装说明 cat README.md # 按 README 完成环境依赖安装后即可使用
- 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
# 查看帮助 agenticrag-survey --help # 基本运行 agenticrag-survey [options] <input> # 详细使用说明请查阅文档 # https://github.com/asinghcsu/AgenticRAG-Survey
# agenticrag-survey 配置说明 # 查看配置选项 agenticrag-survey --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量（覆盖配置文件） export AGENTICRAG_SURVEY_CONFIG="/path/to/config.yml"
Agentic Retrieval-Augmented Generation : A Survey On Agentic RAG
<p align="center"> <img src="./assets/overview_agentic_rag.svg" alt="Agentic RAG Overview"> <br> <strong>Overview of Agentic RAG</strong> </p> <br><br> <p align="center"> <img src="https://img.shields.io/github/stars/asinghcsu/AgenticRAG-Survey?style=social" alt="GitHub stars"> <img src="https://img.shields.io/github/forks/asinghcsu/AgenticRAG-Survey?style=social" alt="GitHub forks"> <img src="https://img.shields.io/github/watchers/asinghcsu/AgenticRAG-Survey?style=social" alt="GitHub watchers"> </p>
<p> <img src="./assets/new.png" alt="New Update" width="50" style="vertical-align: middle;"> <strong>Recent Update (2025-02-04):</strong> </p>
Check section 4 in the table of contents in this repo for the new Agentic Workflow Patterns. New images have been added to enhance the Overview of Agentic RAG. The <a href="https://arxiv.org/pdf/2501.09136">paper</a> is also updated.
---
Abstract
Agentic Retrieval-Augmented Generation ( Agentic RAG) represents a transformative leap in artificial intelligence by embedding autonomous agents into the RAG pipeline. This repository complements the survey paper "Agentic Retrieval-Augmented Generation (Agentic RAG): A Survey On Agentic RAG," providing insights into:
- Foundational principles, including Agentic Patterns such as reflection, planning, tool use, and multi-agent collaboration.
- A detailed taxonomy of Agentic RAG systems, showcasing frameworks like single-agent, multi-agent, hierarchical, corrective, adaptive, and graph-based RAG.
- Comparative analysis of traditional RAG, Agentic RAG, and Agentic Document Workflows (ADW) to highlight their strengths, weaknesses, and best-fit scenarios.
- Real-world applications across industries like healthcare, education, finance, and legal analysis.
- Challenges and future directions in scaling, ethical AI, multimodal integration, and human-agent collaboration.
This repository serves as a comprehensive resource for researchers and practitioners to explore, implement, and advance the capabilities of Agentic RAG systems.
---
Introduction
Retrieval-Augmented Generation (RAG) systems combine the capabilities of large language models (LLMs) with retrieval mechanisms to generate contextually relevant and accurate responses. While traditional RAG systems excel in knowledge retrieval and generation, they often fall short in handling dynamic, multi-step reasoning tasks, adaptability, and orchestration for complex workflows.
Agentic Retrieval-Augmented Generation (Agentic RAG) overcomes these limitations by integrating autonomous AI agents. These agents employ core Agentic Patterns, such as reflection, planning, tool use, and multi-agent collaboration, to dynamically adapt to task-specific requirements and provide superior performance in:
- Multi-domain knowledge retrieval.
- Real-time, document-centric workflows.
- Scalable, adaptive, and ethical AI systems.
This repository explores the evolution of RAG to Agentic RAG, presenting: - Agentic Patterns: The core principles driving the system’s adaptability and intelligence. - Taxonomy: A comprehensive classification of Agentic RAG architectures. - Comparative Analysis: Key differences between Traditional RAG, Agentic RAG, and ADW. - Applications: Practical use cases across healthcare, education, finance, and more. - Challenges and Future Directions: Addressing scalability, ethical AI, and multimodal integration.
Whether you’re a researcher, developer, or practitioner, this repository offers valuable insights and resources to understand and advance Agentic RAG systems.
---
Blogs and Tutorials on Agentic RAG
- DeepLearning.AI: How agents can improve LLM performance. DeepLearning.AI
- Weaviate Blog: What is agentic RAG? Weaviate Blog
- LangGraph CRAG Tutorial: LangGraph CRAG: Contextualized retrieval-augmented generation tutorial. LangGraph CRAG
- LangGraph Adaptive RAG Tutorial: LangGraph adaptive RAG: Adaptive retrieval-augmented generation tutorial. LangGraph Adaptive RAG. Accessed: 2025-01-14.
- LlamaIndex Blog: Agentic RAG with LlamaIndex. LlamaIndex Blog
- Hugging Face Cookbook. Agentic RAG: Turbocharge your retrieval-augmented generation with query reformulation and self-query. Hugging Face Cookbook
- Hugging Face Agentic RAG: https://huggingface.co/docs/smolagents/en/examples/rag
- Qdrant Blog. Agentic RAG: Combining RAG with agents for enhanced information retrieval. Qdrant Blog
- Semantic Kernel: Semantic Kernel is an open-source SDK by Microsoft that integrates large language models (LLMs) into applications. It supports agentic patterns, enabling the creation of autonomous AI agents for natural language understanding, task automation, and decision-making. It has been used in scenarios like ServiceNow’s P1 incident management to facilitate real-time collaboration, automate task execution, and retrieve contextual information seamlessly.
---
Practical Implementations and Use Cases of Agentic RAG
- AWS Machine Learning Blog. How Twitch used agentic workflow with RAG on Amazon Bedrock to supercharge ad sales. AWS Machine Learning Blog
- LlamaCloud Demo Repository. Patient case summary workflow using LlamaCloud. GitHub 2025. Accessed: 2025-01-13.
- LlamaCloud Demo Repository. Contract review workflow using LlamaCloud. GitHub
- LlamaCloud Demo Repository. Auto insurance claims workflow using LlamaCloud. GitHub
- LlamaCloud Demo Repository. Research paper report generation workflow using LlamaCloud.GitHub
---
References
Agentic Workflow Patterns: Adaptive Strategies for Dynamic Collaboration
Agentic workflow patterns help structure LLM-based applications to optimize performance, accuracy, and efficiency. Different approaches are suitable depending on task complexity and processing requirements. Source: Anthropic Research and LangGraph Workflows
7. Agentic Document Workflows (ADW)
Agentic Document Workflows (ADW) extend traditional RAG systems by automating document-centric processes with intelligent agents.
#### Workflow 1. Document Parsing and Structuring: - Extracts structured data from documents like invoices or contracts. 2. State Maintenance: - Tracks context across multi-step workflows for consistency. 3. Knowledge Retrieval: - Retrieves relevant references from external sources or domain-specific databases. 4. Agentic Orchestration: - Applies business rules, performs multi-hop reasoning, and orchestrates external APIs. 5. Actionable Output Generation: - Produces structured outputs tailored to specific use cases (e.g., reports or summaries).
#### Key Features and Advantages - State Maintenance: Ensures consistency in multi-step workflows. - Domain-Specific Intelligence: Adapts to specialized domains with tailored rules. - Scalability: Handles large-scale document processing efficiently. - Enhanced Productivity: Reduces manual effort and augments human expertise.
---
6. Graph-Enhanced Applications in Multimodal Workflows
- Problem: Tackling tasks requiring relational understanding and multi-modal data integration.
- Applications:
- Graph-based retrieval systems for connecting structured and unstructured data.
- Enhanced reasoning workflows in domains like scientific research and knowledge management.
- Synthesis of insights across text, images, and structured data for actionable outputs.
7. Document-Centric Workflows
- Problem: Automating complex workflows involving document parsing, data extraction, and multi-step reasoning.
- Applications:
- Invoice Payments Workflow:
- Parses invoices to extract key details (e.g., invoice number, vendor info, payment terms).
- Retrieves related vendor contracts to verify terms and compliance.
- Generates a payment recommendation report, including cost-saving suggestions (e.g., early payment discounts).
- Contract Review:
- Analyzes legal contracts for critical clauses and compliance issues.
- Automatically identifies risks and provides actionable recommendations.
- Insurance Claims Analysis:
- Automates claims review, extracting policy terms and calculating payouts based on predefined rules.
- Key Advantages:
- State Maintenance: Tracks the document’s context across workflow stages.
- Domain-Specific Intelligence: Applies tailored rules for industry-specific needs.
- Scalability: Handles large volumes of enterprise documents efficiently.
- Enhanced Productivity: Reduces manual effort and augments human expertise.
---
该调研项目系统阐述Agentic-RAG前沿方向，汇集实战框架和最佳实践，是RAG领域重要参考资源，社区活跃度良好。
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 跨境业务、多语言内容运营团队
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- embedding 模型与查询模型不一致导致检索失效
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
⚡ 核心功能
- 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
- 构建多智能体协作系统的 Agent 开发者
- 构建企业知识库 / RAG 检索应用的团队
- 跨境业务、多语言内容运营团队
- 分块大小建议 256-512 tokens，向量库优选 pgvector 或 Qdrant
- Agent 任务先做 dry-run 验证工具调用链，再开启自主执行
- API key 直接提交到 git 仓库（请用 .env 并加入 .gitignore）
- embedding 模型与查询模型不一致导致检索失效
👥 适合人群
🎯 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
⚖️ 优点与不足
- +大幅减少重复性人工操作
- +可视化流程，清晰直观
- +可扩展性强，支持复杂场景
- −未明确开源协议，商用场景需谨慎评估
- −初始配置和调试需投入一定时间
- −强依赖外部服务的稳定性
- −复杂场景需具备一定技术基础
该工具未明确声明开源协议，商业使用前请联系原作者确认授权范围，避免侵权风险。
AI Skill Hub 为第三方内容聚合平台，本页面信息基于公开数据整理，不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后，再部署至生产环境，并做好必要的安全评估。
🔗 相关工具推荐
❓ 常见问题 FAQ
AI Skill Hub 点评：AgenticRAG-Survey Agent工作流 的核心功能完整，质量优秀。对于自动化工程师和运维人员来说，这是一个值得纳入个人工具库的选择。建议先在非生产环境试用，再逐步推广。
| 原始名称 | AgenticRAG-Survey |
| 原始描述 | 开源AI工作流：Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced wi。⭐1.6k |
| Topics | RAG系统智能体框架工作流设计LLM应用 |
| GitHub | https://github.com/asinghcsu/AgenticRAG-Survey |
收录时间：2026-05-14 · 更新时间：2026-05-16 · License：未公布 · AI Skill Hub 不对第三方内容的准确性作法律背书。