智能代理脚本
智能代理脚本 是 AI Skill Hub 本期精选Agent工作流之一。综合评分 8.0 分，整体质量较高。我们强烈推荐将其纳入你的 AI 工具库，帮助提升工作效率。
📚 深度解析
智能代理脚本 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
智能代理脚本 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
智能代理脚本 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：npm 全局安装 npm install -g agentfootprint # 方式二：npx 直接运行（无需安装） npx agentfootprint --help # 方式三：项目依赖安装 npm install agentfootprint # 方式四：从源码运行 git clone https://github.com/footprintjs/agentfootprint cd agentfootprint npm install npm start
- 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
agentfootprint --help
# 基本用法
agentfootprint [options] <input>
# Node.js 代码中使用
const agentfootprint = require('agentfootprint');
const result = await agentfootprint.run(options);
console.log(result);
# agentfootprint 配置说明 # 查看配置选项 agentfootprint --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 AGENTFOOTPRINT_CONFIG="/path/to/config.yml"
简介
Agentfootprint
<p align="center"> <strong>Your agent gave an answer that <em>looks</em> right — and it's wrong.<br/>The logs can't tell you who influenced it. Agentfootprint can.</strong> </p>
<p align="center"> The explainable AI agent framework for TypeScript: every read, write, decision, and tool call becomes <strong>connected evidence</strong> as your agent runs. When something goes wrong, you don't grep logs — you ask. </p>
<p align="center"> <strong>Build</strong> agents — skills, steering, RAG, memory, control flow — and <strong>debug</strong> them like nothing else.<br/> <em>Why</em> is a query, not a guess. </p>
<p align="center"> <a href="https://footprintjs.github.io/agentThinkingUI/"> <img src="docs/assets/hero-atui.png" alt="An agent run replayed in Story Lens — the LLM 'brain' calls the Flight-search tool, the step inspector shows the tool's raw output and the brain's reasoning about it, and the timeline scrubs every step of the run." width="100%"> </a> </p> <p align="center"> <sub>A real run, replayed — rendered with <a href="https://github.com/footprintjs/agentThinkingUI"><b>Story Lens</b></a> (<code>npm i agentthinkingui</code>). Every frame is generated from the run's own trace; <a href="https://footprintjs.github.io/agentThinkingUI/">▶ watch it live</a>.</sub> </p>
<p align="center"> <a href="https://github.com/footprintjs/agentfootprint/actions"><img src="https://github.com/footprintjs/agentfootprint/actions/workflows/ci.yml/badge.svg" alt="CI"></a> <img src="https://img.shields.io/badge/coverage-87%25-green.svg" alt="coverage: 87%"> <a href="https://www.npmjs.com/package/agentfootprint"><img src="https://img.shields.io/npm/v/agentfootprint.svg?style=flat" alt="npm version"></a> <a href="https://bundlephobia.com/package/agentfootprint"><img src="https://img.shields.io/bundlephobia/minzip/agentfootprint?label=minzipped" alt="minzipped size"></a> <a href="#tree-shakeable--esm-first"><img src="https://img.shields.io/badge/tree--shakeable-%E2%9C%93-success?style=flat" alt="tree-shakeable"></a> <a href="https://www.npmjs.com/package/agentfootprint"><img src="https://img.shields.io/npm/dm/agentfootprint.svg" alt="Downloads"></a> <a href="https://github.com/footprintjs/agentfootprint/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="MIT"></a> </p>
---
How — we abstract context engineering
Skills, steering, RAG, facts, memory, guardrails — every name for context does one thing: it injects into one of three LLM slots. So we abstracted the injection itself.
<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/assets/hero-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="docs/assets/hero-light.svg"> <img alt="agentfootprint mascot composing context flavors (Skills, Steering, Guardrails, RAG, Tool APIs, Memory) into three structured LLM slots (system, messages, tools) — the central abstraction, visualized." src="docs/assets/hero-light.svg" width="100%"/> </picture> </p> <p align="center"> <sub><em>One primitive: <code>Injection = slot × trigger × cache</code>. Because the framework owns this point, every piece of context is <b>born tracked</b> — observability isn't wired up, it's a consequence of the abstraction. <a href="#the-4-triggers">The 4 triggers ↓</a></em></sub> </p>
<p align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/assets/triggers-dark.svg"> <source media="(prefers-color-scheme: light)" srcset="docs/assets/triggers-light.svg"> <img alt="agentfootprint — Every LLM call has 3 fixed slots (system, messages, tools). Every flavor lands in one slot under one of 4 fixed triggers (always · rule · on-tool-return · llm-activated). Sparkle streams flow from each trigger lane down to a specific pill inside its destination slot — same slot can hold pills from different triggers (RAG via rule, Instruction via on-tool-return), and the same flavor (Skill) can land in different slots." src="docs/assets/triggers-light.svg" width="100%"/> </picture> </p>
The abstraction is three rules:
- Three slots are fixed.
system,messages,tools— the LLM API surface. - N flavors are open. You declare what you have. Tomorrow's flavor (few-shot, reflection, persona, A2A handoff…) plugs in the same way.
- **Rules decide where and when.** You provide the rules. We collect your data, fire the right one, land it in the right slot at the right iteration.
That's the whole model: Injection = slot × trigger × cache.
- Slot — which of the 3 LLM API regions the content lands in (
system/messages/tools). - Trigger — when the content fires (see below).
- Cache — how stable the content is across iterations. The framework places provider cache markers for you — stable content gets 80–90% cheaper prefixes.
Building with an AI coding assistant
agentfootprint is written to be used — and maintained — by coding agents.
- Teach your assistant the API:
npx agentfootprint-setupinstalls the agentfootprint skill for Claude Code (and rule files for Cursor, Copilot, Windsurf, Cline, Kiro). It loads only when the task is about agentfootprint. - Before building something, search CAPABILITIES.md — an index of what already ships, keyed by what you would call it. It is in the npm package too.
- Every example runs:
examples/holds 160+ runnable programs, each executed end to end in CI against mock providers.
Quick start — runs offline, no API key
npm install agentfootprint footprintjsimport { Agent, defineTool } from 'agentfootprint';
import { mock } from 'agentfootprint/providers';
const weather = defineTool({
name: 'weather',
description: 'Get current weather for a city.',
inputSchema: {
type: 'object',
properties: { city: { type: 'string' } },
required: ['city'],
},
execute: async ({ city }: { city: string }) => `${city}: 72°F, sunny`,
});
const agent = Agent.create({
provider: mock({ reply: 'I checked: it is 72°F and sunny.' }),
model: 'mock',
})
.system('You answer weather questions using the weather tool.')
.tool(weather)
.build();
const result = await agent.run({ message: 'Weather in Paris?' });
console.log(result); // → "I checked: it is 72°F and sunny."For production, import a real provider from agentfootprint/providers and swap it in — anthropic(...) / openai(...) / bedrock(...) / gemini(...) / ollama(...). Only the import line changes; the agent code stays the same. (Every provider — mock included — lives on the agentfootprint/providers subpath, so the main agentfootprint barrel stays free of optional peer-dep requires.)
No cloud account? ollama('llama3.2') from agentfootprint/providers runs the same agent against a local model for $0 — the free rung between the mock and the bill. Full recipes: Ollama · OpenAI-compatible endpoints.
高质量的AI工作流项目
- 构建多智能体协作系统的 Agent 开发者
- 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）
- 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
- 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）
👥 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
- +MIT 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。
✅ MIT 协议 — 最宽松的开源协议之一，可自由商用、修改、分发，仅需保留版权声明。
🔗 相关工具推荐
❓ 常见问题 FAQ
经综合评估，智能代理脚本 在Agent工作流赛道中表现稳健，质量优秀。如果你已有明确的使用需求，可以直接上手体验；如果还在评估阶段，建议对比同类工具后再做决策。
| 原始名称 | agentfootprint |
| 原始描述 | 开源AI工作流：Context engineering, abstracted. Build AI agents whose every LLM call traces bac。⭐9 · TypeScript |
| Topics | ai-agentsai-safetyexplainability |
| GitHub | https://github.com/footprintjs/agentfootprint |
| License | MIT |
| 语言 | TypeScript |
收录时间：2026-06-08 · 更新时间：2026-06-11 · License：MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。