promptulate — AI Agent 工作流中文教程
AI Skill Hub 强烈推荐：promptulate — AI Agent 工作流中文教程 是一款优质的Agent工作流。AI 综合评分 8.2 分，在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案，这是一个值得深入了解的选择。
promptulate — AI Agent 工作流中文教程 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。
promptulate — AI Agent 工作流中文教程 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。
promptulate — AI Agent 工作流中文教程 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
# 方式一：pip 安装（推荐）
pip install promptulate
# 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 promptulate
# 方式三：从源码安装（获取最新功能）
git clone https://github.com/Undertone0809/promptulate
cd promptulate
pip install -e .
# 验证安装
python -c "import promptulate; print('安装成功')"
- 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
# 命令行使用
promptulate --help
# 基本用法
promptulate input_file -o output_file
# Python 代码中调用
import promptulate
# 示例
result = promptulate.process("input")
print(result)
# promptulate 配置文件示例（config.yml） app: name: "promptulate" debug: false log_level: "INFO" # 运行时指定配置文件 promptulate --config config.yml # 或通过环境变量配置 export PROMPTULATE_API_KEY="your-key" export PROMPTULATE_OUTPUT_DIR="./output"
简介
<p align="center"> <img src="./docs/public/banner.png" alt="promptulate" style="border-radius: 15px;"/> </p>
<p align="center"> <a target="_blank" href=""> <img src="https://img.shields.io/github/license/Undertone0809/promptulate.svg?style=flat-square" /> </a> <a target="_blank" href=''> <img src="https://img.shields.io/github/release/Undertone0809/promptulate/all.svg?style=flat-square"/> </a> <a href="https://pypi.org/project/promptulate" target="_blank"> <img src="https://img.shields.io/pypi/pyversions/promptulate.svg?color=%2334D058" alt="Supported Python versions"> </a> <a href="https://t.me/zeeland0809" target="_blank"> <img src="https://img.shields.io/badge/Telegram-join%20chat-2CA5E0?logo=telegram&logoColor=white" alt="chat on Telegram"> </a> <a target="_blank" href=''> <img src="https://static.pepy.tech/personalized-badge/promptulate?period=month&units=international_system&left_color=grey&right_color=blue&left_text=Downloads/Week"/> </a> </p>
Overview
Promptulate is an AI Agent application development framework crafted by Cogit Lab, which offers developers an extremely concise and efficient way to build Agent applications through a Pythonic development paradigm. The core philosophy of Promptulate is to borrow and integrate the wisdom of the open-source community, incorporating the highlights of various development frameworks to lower the barrier to entry and unify the consensus among developers. With Promptulate, you can manipulate components like LLM, Agent, Tool, RAG, etc., with the most succinct code, as most tasks can be easily completed with just a few lines of code. 🚀
💡 Features
- 🐍 Pythonic Code Style: Embraces the habits of Python developers, providing a Pythonic SDK calling approach, putting everything within your grasp with just one
pne.chatfunction to encapsulate all essential functionalities. - 🧠 Model Compatibility: Supports nearly all types of large models on the market and allows for easy customization to meet specific needs.
- 🕵️♂️ Diverse Agents: Offers various types of Agents, such as WebAgent, ToolAgent, CodeAgent, etc., capable of planning, reasoning, and acting to handle complex problems. Atomize the Planner and other components to simplify the development process.
- 🔗 Low-Cost Integration: Effortlessly integrates tools from different frameworks like LangChain, significantly reducing integration costs.
- 🔨 Functions as Tools: Converts any Python function directly into a tool usable by Agents, simplifying the tool creation and usage process.
- 🪝 Lifecycle and Hooks: Provides a wealth of Hooks and comprehensive lifecycle management, allowing the insertion of custom code at various stages of Agents, Tools, and LLMs.
- 💻 Terminal Integration: Easily integrates application terminals, with built-in client support, offering rapid debugging capabilities for prompts.
- ⏱️ Prompt Caching: Offers a caching mechanism for LLM Prompts to reduce repetitive work and enhance development efficiency.
- 🤖 Powerful OpenAI Wrapper: With pne, you no longer need to use the openai sdk, the core functions can be replaced with pne.chat, and provides enhanced features to simplify development difficulty.
- 🧰 Streamlit Component Integration: Quickly prototype and provide many out-of-the-box examples and reusable streamlit components.
The following diagram shows the core architecture of promptulate:
The core concept of Promptulate is we hope to provide a simple, pythonic and efficient way to build AI applications, which means you don't need to spend a lot of time learning the framework. We hope to use pne.chat() to do most of the works, and you can easily build any AI application with just a few lines of code.
Below,pnestands for Promptulate, which is the nickname for Promptulate. Thepanderepresent the beginning and end of Promptulate, respectively, andnstands for 9, which is a shorthand for the nine letters betweenpande.
📝 Examples
- Build streamlit chatbot by pne
- Build gradio chatbot by pne
- Build math application with agent
- Groq, llama3, Streamlit to build an application
- Build knowledge map with streamlit and pne
- Build a chatbot using pne+streamlit to chat with GitHub repo
- [Build a math application with agent [Steamlit, ToolAgent, Hooks].](use_cases/build-math-application-with-agent.md)
- [A Mulitmodal Robot Agent framework of ROS2 and Promptulate [Agent]](https://github.com/Undertone0809/Athena)
- [Use streamlit and pne to compare different model a playground. [Streamlit]](https://github.com/Undertone0809/pne-playground-model-comparison)
- gcop:Your git AI copilot, based on promptulate
🛠 Quick Start
- Open the terminal and enter the following command to install the framework:
script
pip install -U pneNote: Your Python version should be 3.8 or higher.
Even though pne provides many modules, in 90% of LLM application development scenarios, you only need to use the pne.chat () function, so you only need to start with chat to understand the use of pne, and when you need to use additional modules, you can learn more about the features and use of other modules.
Replace the OpenAI SDK
Many third party libraries can use OpenAI SDK calls their models, such as Deepseek. In pne, you can directly use pne.chat() function to call these models, It does not need to use the OpenAI SDK, and provides enhanced features to simplify the development difficulty. Use the openai/xxx provider prefix in the model, and you can use the OpenAI model to make calls.
import os
import promptulate as pne
os.environ["DEEPSEEK_API_KEY"] = "your api key"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "How are you?"},
]
response = pne.chat(
messages=messages,
model="openai/deepseek-chat",
)
print(response)- 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
- 本地部署优先选 GGUF 量化模型，节省显存并保持响应速度
- 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
- 显存不足直接 OOM — 优先降低 context 或换更小的量化模型
- Python 依赖冲突：建议用 venv / uv 隔离环境
- CLI：直接 npm install -g / pip install，命令行调用
- 本地部署：CPU 8GB 起，GPU 推荐 16GB+ 显存
- 云端托管：可放在 Vercel / Railway / Fly.io 等 PaaS 平台
- 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
- +Apache-2.0 协议，可免费商用
- +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
- −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
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。
✅ Apache 2.0 — 宽松开源协议，可商用，需保留版权声明和 NOTICE 文件，含专利授权条款。
总体来看，promptulate — AI Agent 工作流中文教程 是一款质量优秀的Agent工作流，在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态，建议收藏备用，结合自身场景选择合适时机引入使用。
| 原始名称 | promptulate |
| 原始描述 | 🚀Lightweight Large language model automation and Autonomous Language Agents development framework. Build your LLM Agent Application in a pythonic way! |
| Topics | agentautogenchatgptgpt-4langchainlanguage-agentllm-app |
| GitHub | https://github.com/Undertone0809/promptulate |
| License | Apache-2.0 |
| 语言 | Python |
收录时间：2026-05-22 · 更新时间：2026-05-22 · License：Apache-2.0 · AI Skill Hub 不对第三方内容的准确性作法律背书。