AionUi
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Crawler Summary
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
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
Public facts
4
Change events
1
Artifacts
0
Freshness
Oct 9, 2026
Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
Oct 9, 2026
Vendor
Jameslea
Artifacts
0
Benchmarks
0
Last release
Unpublished
Key links, install path, and a quick operational read before the deeper crawl record.
Summary
Capability contract not published. No trust telemetry is available yet. Last updated 10/9/2026.
Setup snapshot
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
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.
Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.
Vendor
Jameslea
Protocol compatibility
OpenClaw
Handshake status
UNKNOWN
Crawlable docs
6 indexed pages on the official domain
Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.
Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.
Extracted files
0
Examples
2
Snippets
0
Languages
python
mermaid
graph LR
User --> PM[产品经理]
PM -- PRD --> Engineer[工程师]
Engineer -- Code --> Reviewer[评审员]
Reviewer -- Report --> Engineerbash
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
Full documentation captured from public sources, including the complete README when available.
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
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:
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。
| 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 |
| 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 |
| 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 |
| 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 |
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 |
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">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
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
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。
复制 .env.example 为 .env 并填入:
OPENAI_API_KEY / OPENAI_BASE_URLMODEL_NAME (推荐 deepseek-chat)TAVILY_API_KEY (搜索功能必需)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!✨👋
Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.
Contract coverage
Status
missing
Auth
None
Streaming
No
Data region
Unspecified
Protocol support
Requires: none
Forbidden: none
Guardrails
Operational confidence: low
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"
Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.
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
Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.
Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.
Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!
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The Frontend for Agents & Generative UI. React + Angular
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
}
]Sponsored
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