activepieces
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
Crawler Summary
🤖 AI Agent Framework Guide (中文) | Scion · AutoGen · CrewAI · LangGraph · MetaGPT · Dify · Coze — 深度对比 + 选型决策树 | Chinese developer guide for choosing the right multi-agent framework 🤖 Awesome AI Agent Frameworks — AI Agent 编排框架中文选型指南 $1 $1 $1 **不是又一个链接列表。** 这是一份面向中国开发者的 **AI Agent 框架深度对比 + 选型指引**,包含架构分析、代码示例和决策流程图。 🔥 **热点更新(2026-04-07)**:Google 刚刚开源 $1 — 多 Agent 容器编排测试平台,本指南提供首发中文深度解读。$1 --- 📊 框架对比总表 | 框架 | 开发者 | 架构类型 | 语言 | 适用场景 | 学习曲线 | 生产就绪度 | Stars | |------|--------|---------|------|---------|---------|-----------|-------| | **$1** 🆕 | Google Cloud | 容器编排 | Go | 多 Agent 并行开发 | ⭐⭐⭐⭐ | 🧪 Capability contract not published. No trust telemetry is available yet. 2 GitHub stars reported by the source. Last updated 5/18/2026.
Freshness
Last checked 5/18/2026
Best For
awesome-ai-agent-frameworks 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 OPENCLEW, runtime-metrics, public facts pack
🤖 AI Agent Framework Guide (中文) | Scion · AutoGen · CrewAI · LangGraph · MetaGPT · Dify · Coze — 深度对比 + 选型决策树 | Chinese developer guide for choosing the right multi-agent framework 🤖 Awesome AI Agent Frameworks — AI Agent 编排框架中文选型指南 $1 $1 $1 **不是又一个链接列表。** 这是一份面向中国开发者的 **AI Agent 框架深度对比 + 选型指引**,包含架构分析、代码示例和决策流程图。 🔥 **热点更新(2026-04-07)**:Google 刚刚开源 $1 — 多 Agent 容器编排测试平台,本指南提供首发中文深度解读。$1 --- 📊 框架对比总表 | 框架 | 开发者 | 架构类型 | 语言 | 适用场景 | 学习曲线 | 生产就绪度 | Stars | |------|--------|---------|------|---------|---------|-----------|-------| | **$1** 🆕 | Google Cloud | 容器编排 | Go | 多 Agent 并行开发 | ⭐⭐⭐⭐ | 🧪
Public facts
5
Change events
1
Artifacts
0
Freshness
May 18, 2026
Capability contract not published. No trust telemetry is available yet. 2 GitHub stars reported by the source. Last updated 5/18/2026.
Trust score
Unknown
Compatibility
OpenClaw
Freshness
May 18, 2026
Vendor
Vincentwei1021
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Unpublished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. 2 GitHub stars reported by the source. Last updated 5/18/2026.
Setup snapshot
git clone https://github.com/Vincentwei1021/awesome-ai-agent-frameworks.gitSetup 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
Vincentwei1021
Protocol compatibility
OpenClaw
Adoption signal
2 GitHub stars
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
6
Snippets
0
Languages
python
bash
# 快速体验 go install github.com/GoogleCloudPlatform/scion/cmd/scion@latest cd my-project scion init scion start debug "Help me debug this error" --attach
python
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
agent = AssistantAgent("assistant", model_client=model_client)
team = RoundRobinGroupChat([agent])
result = await team.run(task="写一个冒泡排序")python
from crewai import Agent, Task, Crew researcher = Agent(role="研究员", goal="深度调研", backstory="资深行业分析师") task = Task(description="调研 2026 年 AI Agent 趋势", agent=researcher) crew = Crew(agents=[researcher], tasks=[task]) result = crew.kickoff()
python
from langgraph.graph import StateGraph
graph = StateGraph(State)
graph.add_node("research", research_node)
graph.add_node("write", write_node)
graph.add_edge("research", "write")
app = graph.compile()python
from metagpt.software_company import generate_repo await generate_repo(idea="开发一个天气查询 CLI 工具") # 自动生成:PRD → 系统设计 → 代码 → 测试
python
from swarms import Agent, SequentialWorkflow
agents = [Agent(agent_name=f"worker-{i}", llm=model) for i in range(5)]
workflow = SequentialWorkflow(agents=agents)
result = workflow.run("分析这份季度报告")Full documentation captured from public sources, including the complete README when available.
Docs source
GITHUB OPENCLEW
Editorial quality
ready
🤖 AI Agent Framework Guide (中文) | Scion · AutoGen · CrewAI · LangGraph · MetaGPT · Dify · Coze — 深度对比 + 选型决策树 | Chinese developer guide for choosing the right multi-agent framework 🤖 Awesome AI Agent Frameworks — AI Agent 编排框架中文选型指南 $1 $1 $1 **不是又一个链接列表。** 这是一份面向中国开发者的 **AI Agent 框架深度对比 + 选型指引**,包含架构分析、代码示例和决策流程图。 🔥 **热点更新(2026-04-07)**:Google 刚刚开源 $1 — 多 Agent 容器编排测试平台,本指南提供首发中文深度解读。$1 --- 📊 框架对比总表 | 框架 | 开发者 | 架构类型 | 语言 | 适用场景 | 学习曲线 | 生产就绪度 | Stars | |------|--------|---------|------|---------|---------|-----------|-------| | **$1** 🆕 | Google Cloud | 容器编排 | Go | 多 Agent 并行开发 | ⭐⭐⭐⭐ | 🧪
不是又一个链接列表。 这是一份面向中国开发者的 AI Agent 框架深度对比 + 选型指引,包含架构分析、代码示例和决策流程图。
🔥 热点更新(2026-04-07):Google 刚刚开源 Scion — 多 Agent 容器编排测试平台,本指南提供首发中文深度解读。→ 查看 Scion 专题
| 框架 | 开发者 | 架构类型 | 语言 | 适用场景 | 学习曲线 | 生产就绪度 | Stars | |------|--------|---------|------|---------|---------|-----------|-------| | Scion 🆕 | Google Cloud | 容器编排 | Go | 多 Agent 并行开发 | ⭐⭐⭐⭐ | 🧪 实验 | 新项目 | | AutoGen | Microsoft | 对话驱动 | Python | 多 Agent 对话协作 | ⭐⭐⭐ | ✅ 生产可用 | 42k+ | | CrewAI | CrewAI Inc. | 角色扮演 | Python | 团队协作任务 | ⭐⭐ | ✅ 生产可用 | 28k+ | | LangGraph | LangChain | 状态图 | Python/JS | 复杂工作流编排 | ⭐⭐⭐⭐ | ✅ 生产可用 | 12k+ | | MetaGPT | DeepWisdom | SOP 驱动 | Python | 软件开发模拟 | ⭐⭐⭐ | 🟡 可用 | 48k+ | | Swarms | Swarms Corp | 群体智能 | Python | 大规模 Agent 集群 | ⭐⭐⭐ | 🟡 可用 | 4k+ | | Dify | Dify.AI | 可视化编排 | Python/TS | 低代码 AI 应用 | ⭐ | ✅ 生产可用 | 62k+ | | Coze | 字节跳动 | 可视化平台 | — | 快速搭建 Bot | ⭐ | ✅ 生产可用 | 平台级 |
💡 Stars 数据截至 2026 年 4 月。 学习曲线 ⭐ 越少越容易上手。
| 维度 | 含义 | |------|------| | 架构类型 | 框架的核心编排模式(对话、图、角色、容器等) | | 适用场景 | 最擅长解决什么类型的问题 | | 部署方式 | 本地/云/Kubernetes/SaaS | | 语言支持 | 主要开发语言 | | 社区活跃度 | GitHub Stars、Issue 响应、生态插件数量 | | 学习曲线 | 从入门到能跑通生产 demo 的时间 | | 生产就绪度 | 能否用于生产环境(🧪实验 / 🟡可用 / ✅生产) |
Google Cloud 刚开源的多 Agent 容器编排测试平台 — 详细解读 →
Scion 的核心理念是 "少即是多":不规定死板的编排模式,而是让 Agent 自行通过 CLI 工具学习如何协调。每个 Agent 运行在独立容器中,拥有独立的 git worktree 和凭证。
# 快速体验
go install github.com/GoogleCloudPlatform/scion/cmd/scion@latest
cd my-project
scion init
scion start debug "Help me debug this error" --attach
核心概念:Grove(项目空间)→ Agent(容器化进程)→ Hub(控制平面)→ Runtime Broker(算力节点)
Microsoft 的对话驱动多 Agent 框架 — 详细解读 →
AutoGen 围绕 多 Agent 对话 构建,Agent 通过自然语言消息传递协作。v0.4 版本重构为事件驱动架构,支持分布式部署。
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
agent = AssistantAgent("assistant", model_client=model_client)
team = RoundRobinGroupChat([agent])
result = await team.run(task="写一个冒泡排序")
最直觉的角色扮演多 Agent 框架 — 详细解读 →
CrewAI 用 角色(Agent)+ 任务(Task)+ 团队(Crew) 三层抽象,像组建一支团队一样编排 AI。
from crewai import Agent, Task, Crew
researcher = Agent(role="研究员", goal="深度调研", backstory="资深行业分析师")
task = Task(description="调研 2026 年 AI Agent 趋势", agent=researcher)
crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()
LangChain 生态的状态图工作流引擎 — 详细解读 →
LangGraph 将 Agent 工作流建模为 有向图,节点是处理步骤,边是条件转移。支持持久化状态和人类介入。
from langgraph.graph import StateGraph
graph = StateGraph(State)
graph.add_node("research", research_node)
graph.add_node("write", write_node)
graph.add_edge("research", "write")
app = graph.compile()
用 SOP 驱动的软件公司模拟器 — 详细解读 →
MetaGPT 让多个 Agent 模拟软件公司的角色(产品经理、架构师、工程师),按照 标准化流程(SOP) 协作开发。
from metagpt.software_company import generate_repo
await generate_repo(idea="开发一个天气查询 CLI 工具")
# 自动生成:PRD → 系统设计 → 代码 → 测试
面向大规模 Agent 集群的群体智能框架 — 详细解读 →
Swarms 支持数百个 Agent 并发运行,提供多种编排模式(顺序、并行、层级、混合)。
from swarms import Agent, SequentialWorkflow
agents = [Agent(agent_name=f"worker-{i}", llm=model) for i in range(5)]
workflow = SequentialWorkflow(agents=agents)
result = workflow.run("分析这份季度报告")
开源的可视化 AI 应用开发平台 — 详细解读 →
Dify 提供拖拽式画布编排 Agent 工作流,内置 RAG、工具调用、对话管理。适合不想写代码的团队。
字节跳动的 AI Bot 搭建平台 — 详细解读 →
Coze(扣子)是字节跳动的 AI 应用开发平台,通过可视化界面搭建 Bot,支持插件、工作流、知识库。国内版直接对接豆包大模型。
不知道选哪个?回答几个问题,找到最适合你的框架。详细版 →
flowchart TD
A[你需要什么?] --> B{需要写代码吗?}
B -->|不想写代码| C{需要自部署吗?}
C -->|不需要| D[✅ Coze]
C -->|需要| E[✅ Dify]
B -->|可以写代码| F{核心需求是什么?}
F -->|多 Agent 对话| G{需要分布式吗?}
G -->|是| H[✅ AutoGen v0.4]
G -->|否| I[✅ CrewAI]
F -->|复杂工作流| J[✅ LangGraph]
F -->|软件开发| K{团队规模?}
K -->|模拟完整团队| L[✅ MetaGPT]
K -->|并行编码 Agent| M[✅ Scion]
F -->|大规模集群| N[✅ Swarms]
style D fill:#10B981,color:#fff
style E fill:#10B981,color:#fff
style H fill:#3B82F6,color:#fff
style I fill:#3B82F6,color:#fff
style J fill:#8B5CF6,color:#fff
style L fill:#F59E0B,color:#fff
style M fill:#EF4444,color:#fff
style N fill:#EC4899,color:#fff
| 模式 | 代表框架 | 核心思想 | 优点 | 缺点 | |------|---------|---------|------|------| | 对话驱动 | AutoGen | Agent 通过消息传递协作 | 灵活、自然 | 输出不可控 | | 角色扮演 | CrewAI | 定义角色 + 任务 + 团队 | 直觉、易上手 | 深度定制受限 | | 状态图 | LangGraph | 有向图 + 条件分支 | 精确控制流 | 学习曲线陡 | | SOP 流程 | MetaGPT | 模拟真实团队 SOP | 结构化输出 | 不够灵活 | | 容器编排 | Scion | 每 Agent 独立容器 | 真隔离、可扩展 | 运维复杂度高 | | 可视化 | Dify/Coze | 拖拽式画布 | 零代码 | 灵活性有限 | | 群体智能 | Swarms | 大量 Agent 并行 | 规模大 | 协调成本高 |
| 场景 | 推荐 | 备选 | |------|------|------| | 快速搭建 AI 客服 Bot | Coze / Dify | CrewAI | | 多 Agent 代码协作 | Scion | AutoGen | | 复杂审批/决策工作流 | LangGraph | AutoGen | | 模拟软件开发团队 | MetaGPT | CrewAI | | 数据分析 Pipeline | LangGraph | AutoGen | | 内容创作团队 | CrewAI | MetaGPT | | 大规模并行处理 | Swarms / Scion | AutoGen | | 企业内部 AI 平台 | Dify | LangGraph |
| 团队类型 | 推荐 | |---------|------| | 非技术团队 | Coze → Dify | | 初创团队(快速验证) | CrewAI → AutoGen | | 中大型工程团队 | LangGraph → Scion | | AI 研究团队 | AutoGen → MetaGPT |
欢迎贡献!请阅读 贡献指南。
| 项目 | 简介 | |------|------| | 📈 Kronos 中文实战指南 | 金融 K 线基础模型 · A 股预测 · 微调 · 回测集成 | | 🚀 MegaTrain 中文实战指南 | 单 GPU 训练 100B+ 大模型 · 硬件选购 · 性能对比 |
MIT License © 2026
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-vincentwei1021-awesome-ai-agent-frameworks/snapshot"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/contract"
curl -s "https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/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.
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents
AI productivity studio with smart chat, autonomous agents, and 300+ assistants.
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!
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": {
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"contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/contract",
"trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/trust"
},
"curlExamples": [
"curl -s \"https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/snapshot\"",
"curl -s \"https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/contract\"",
"curl -s \"https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/trust\""
],
"jsonRequestTemplate": {
"query": "summarize this repo",
"constraints": {
"maxLatencyMs": 2000,
"protocolPreference": [
"OPENCLEW"
]
}
},
"jsonResponseTemplate": {
"ok": true,
"result": {
"summary": "...",
"confidence": 0.9
},
"meta": {
"source": "GITHUB_OPENCLEW",
"generatedAt": "2026-10-09T13:39:39.970Z"
}
},
"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",
"label": "Vendor",
"value": "Vincentwei1021",
"category": "vendor",
"href": "https://github.com/Vincentwei1021/awesome-ai-agent-frameworks",
"sourceUrl": "https://github.com/Vincentwei1021/awesome-ai-agent-frameworks",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-05-18T06:45:12.008Z",
"isPublic": true,
"metadata": {}
},
{
"factKey": "protocols",
"label": "Protocol compatibility",
"value": "OpenClaw",
"category": "compatibility",
"href": "https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/contract",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/contract",
"sourceType": "contract",
"confidence": "medium",
"observedAt": "2026-05-18T06:45:12.008Z",
"isPublic": true,
"metadata": {}
},
{
"factKey": "traction",
"label": "Adoption signal",
"value": "2 GitHub stars",
"category": "adoption",
"href": "https://github.com/Vincentwei1021/awesome-ai-agent-frameworks",
"sourceUrl": "https://github.com/Vincentwei1021/awesome-ai-agent-frameworks",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-05-18T06:45:12.008Z",
"isPublic": true,
"metadata": {}
},
{
"factKey": "docs_crawl",
"label": "Crawlable docs",
"value": "6 indexed pages on the official domain",
"category": "integration",
"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,
"metadata": {}
},
{
"factKey": "handshake_status",
"label": "Handshake status",
"value": "UNKNOWN",
"category": "security",
"href": "https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/trust",
"sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-vincentwei1021-awesome-ai-agent-frameworks/trust",
"sourceType": "trust",
"confidence": "medium",
"observedAt": null,
"isPublic": true,
"metadata": {}
}
]Change Events JSON
[
{
"eventType": "docs_update",
"title": "Docs refreshed: Sign in to GitHub · GitHub",
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