Crawler Summary

agents-learn answer-first brief

Hands-on AI Agents examples covering LangGraph, RAG, multi-agent workflows, LlamaIndex, CrewAI, AutoGen, and smolagents. 深入浅出 AI Agents:从核心原理到全栈实战 **这是一个系统化的 AI Agents 开发者学习路线。** 涵盖了从最基础的原子组件(Tools/Memory)、生产级架构(LangGraph/Self-RAG)、主流 Agent 框架横向对比(CrewAI/AutoGen)、到前沿模式(SOP/技能库/自主循环)的全方位实战。 --- 🌟 本项目能带给你什么? 本仓库不只是代码的堆砌,它沉淀了一套 **Agent 架构师的决策逻辑**: * **全栈范式**:从手写 ReAct 到使用 LlamaIndex/LangGraph 构建复杂工作流。 * **多框架实验室**:深度对比 **LangGraph**, **smolagents**, **CrewAI**, **AutoGen**, **MetaGPT**。 * **工程化深度**:覆盖代码执行安全(AST)、多 Agent 协作约束(SOP)、量化评估(RA Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

agents-learn is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

Agent DossierGITHUB REPOSSafety: 66/100

agents-learn

Hands-on AI Agents examples covering LangGraph, RAG, multi-agent workflows, LlamaIndex, CrewAI, AutoGen, and smolagents. 深入浅出 AI Agents:从核心原理到全栈实战 **这是一个系统化的 AI Agents 开发者学习路线。** 涵盖了从最基础的原子组件(Tools/Memory)、生产级架构(LangGraph/Self-RAG)、主流 Agent 框架横向对比(CrewAI/AutoGen)、到前沿模式(SOP/技能库/自主循环)的全方位实战。 --- 🌟 本项目能带给你什么? 本仓库不只是代码的堆砌,它沉淀了一套 **Agent 架构师的决策逻辑**: * **全栈范式**:从手写 ReAct 到使用 LlamaIndex/LangGraph 构建复杂工作流。 * **多框架实验室**:深度对比 **LangGraph**, **smolagents**, **CrewAI**, **AutoGen**, **MetaGPT**。 * **工程化深度**:覆盖代码执行安全(AST)、多 Agent 协作约束(SOP)、量化评估(RA

OpenClawself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Jameslea

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.

Setup snapshot

  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Jameslea

profilemedium
Observed Oct 9, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Oct 9, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

0

Examples

2

Snippets

0

Languages

python

Executable Examples

mermaid

graph LR
    User --> PM[产品经理]
    PM -- PRD --> Engineer[工程师]
    Engineer -- Code --> Reviewer[评审员]
    Reviewer -- Report --> Engineer

bash

python3 -m venv venv
source venv/bin/activate

# 推荐:按需安装阶段依赖(以阶段 05 为例)
pip install -r requirements/base.txt
# pip install -r requirements/phase05-rag.txt

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Hands-on AI Agents examples covering LangGraph, RAG, multi-agent workflows, LlamaIndex, CrewAI, AutoGen, and smolagents. 深入浅出 AI Agents:从核心原理到全栈实战 **这是一个系统化的 AI Agents 开发者学习路线。** 涵盖了从最基础的原子组件(Tools/Memory)、生产级架构(LangGraph/Self-RAG)、主流 Agent 框架横向对比(CrewAI/AutoGen)、到前沿模式(SOP/技能库/自主循环)的全方位实战。 --- 🌟 本项目能带给你什么? 本仓库不只是代码的堆砌,它沉淀了一套 **Agent 架构师的决策逻辑**: * **全栈范式**:从手写 ReAct 到使用 LlamaIndex/LangGraph 构建复杂工作流。 * **多框架实验室**:深度对比 **LangGraph**, **smolagents**, **CrewAI**, **AutoGen**, **MetaGPT**。 * **工程化深度**:覆盖代码执行安全(AST)、多 Agent 协作约束(SOP)、量化评估(RA

Full README

深入浅出 AI Agents:从核心原理到全栈实战

这是一个系统化的 AI Agents 开发者学习路线。 涵盖了从最基础的原子组件(Tools/Memory)、生产级架构(LangGraph/Self-RAG)、主流 Agent 框架横向对比(CrewAI/AutoGen)、到前沿模式(SOP/技能库/自主循环)的全方位实战。


🌟 本项目能带给你什么?

本仓库不只是代码的堆砌,它沉淀了一套 Agent 架构师的决策逻辑:

  • 全栈范式:从手写 ReAct 到使用 LlamaIndex/LangGraph 构建复杂工作流。
  • 多框架实验室:深度对比 LangGraph, smolagents, CrewAI, AutoGen, MetaGPT。
  • 工程化深度:覆盖代码执行安全(AST)、多 Agent 协作约束(SOP)、量化评估(RAGAS)与生产级护栏(Guardrails)。
  • 前沿模式:复现了 Voyager 的技能库(Skill Library)思想与 BabyAGI 的自主任务循环。

🗺️ AI Agent 开发者路线图 (1-15 阶段)

本项目由浅入深分为四大版块,所有阶段均已完成并配有可运行代码。

1. 核心原理与基础编排 (Foundation)

| 阶段 | 模块 | 核心内容 | 实战入口 | | :--- | :--- | :--- | :--- | | 01-02 | 原子组件 | 工具调用、Prompt 模板、短期/长效记忆 | 01-concepts / 02-assistant | | 03-04 | 流程编排 | LangGraph 状态机、ReAct 规划、多 Agent 主管模式 | 03-langgraph / 04-multi-agent | | 05 | 生产级闭环 | Self-RAG 系统:具备自愈、自评、自检索能力 | 05-final-project |

2. 多框架深度对比实验室 (Framework Lab)

| 阶段 | 框架 | 性格与适用场景 | 核心机制 | | :--- | :--- | :--- | :--- | | 06 | smolagents | 代码即操作:极致简洁的工具调用 | AST 代码智能体 | | 07 | CrewAI | 职场角色扮演:基于 Backstory 的团队协作 | 任务流编排 | | 08 | AutoGen | 对话式自愈:Agent 之间的动态博弈与报错修正 | 多 Agent 会话 | | 09 | 执行安全 | 深度辨析 AST 解析与 Subprocess 执行的安全性 | 执行深度挖掘 |

3. 高级进阶模式 (Advanced Patterns)

| 阶段 | 模式 | 解决的核心问题 | 实战入口 | | :--- | :--- | :--- | :--- | | 10 | 数据中心型 | LlamaIndex 赋能的 Agentic RAG 与企业知识库 | LlamaIndex | | 11 | SOP 驱动型 | MetaGPT:用软件工程 SOP 约束多 Agent 产出 | MetaGPT/SOP | | 12 | 自主循环型 | BabyAGI:目标驱动的任务队列与动态优先级 | 自主任务流 | | 13 | 技能学习型 | Voyager 风格:将成功经验沉淀为可复用技能库 | 技能库 Agent |

4. 生产化治理与平台 (Engineering & Platform)

| 阶段 | 主题 | 核心工具与方法 | 实战入口 | | :--- | :--- | :--- | :--- | | 14 | 低代码平台 | Dify / Coze:从硬核开发到可视化编排的取舍 | 平台对比分析 | | 15 | 生产级工程 | RAGAS 评估、Langfuse 追踪、安全护栏 (Guardrails) | 工程实战代码 |

5. 💎 生产环境生存指南 (Architecture Deep Dives)

强烈推荐中高级开发者阅读:本部分脱离了基础的 API 调用,专注于大模型底层原理、深度数据清洗(ETL)、分布式状态机架构(Checkpoint)以及安全评估。

| 主题 | 核心内容 | 深度指南入口 | | :--- | :--- | :--- | | 高阶架构 | vLLM/量化选型、GraphRAG、断点续传、Prompt 注入防御 | 👉 进入进阶全景图 |


🏗️ 典型架构方案展示

方案 A:Self-RAG (自愈型检索增强)

适用于对回答准确度有极高要求的知识库场景。

<img src="./assets/Self-RAG.png" width="400px">

方案 B:SOP 驱动的多 Agent 协作

适用于软件开发、内容生产等具有标准作业程序的复杂流程。

graph LR
    User --> PM[产品经理]
    PM -- PRD --> Engineer[工程师]
    Engineer -- Code --> Reviewer[评审员]
    Reviewer -- Report --> Engineer

🛠️ 快速开始

1. 基础环境

python3 -m venv venv
source venv/bin/activate

# 推荐:按需安装阶段依赖(以阶段 05 为例)
pip install -r requirements/base.txt
# pip install -r requirements/phase05-rag.txt

2. ⚠️ 特殊阶段说明 (MetaGPT)

阶段 11 (MetaGPT) 由于依赖冲突,必须使用独立的 Python 3.11 环境。 具体配置请参考:11-metagpt-sop/README.md。

3. 配置环境变量

复制 .env.example 为 .env 并填入:

  • OPENAI_API_KEY / OPENAI_BASE_URL
  • MODEL_NAME (推荐 deepseek-chat)
  • TAVILY_API_KEY (搜索功能必需)

📖 核心资产与路线图


🚀 结语

所有的探索都是为了在面对真实业务需求时,不仅知道“怎么做”,更知道“为什么这么做”以及“有没有更好的替代方案”。

祝您的智能体永不报错!✨👋

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-10T04:37:56.722Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Jameslea",
    "href": "https://github.com/jameslea/agents-learn",
    "sourceUrl": "https://github.com/jameslea/agents-learn",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T21:09:26.367Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T21:09:26.367Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-jameslea-agents-learn/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

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