Multi-Agent Orchestration

多智能体编排

Design and orchestrate sophisticated multi-agent systems where specialized agents collaborate to solve complex problems, combining different expertise and perspectives.

设计并编排复杂的多智能体系统，让专业智能体结合不同的专业知识与视角，协作解决复杂问题。

Quick Start

快速开始

Get started with multi-agent implementations in the examples and utilities:

Examples: See
```
examples/
```
directory for complete implementations:
- ```
orchestration_patterns.py
```
  - Sequential, parallel, hierarchical, and consensus orchestration
- ```
framework_implementations.py
```
  - Templates for CrewAI, AutoGen, LangGraph, and Swarm
Utilities: See
```
scripts/
```
directory for helper modules:
- ```
agent_communication.py
```
  - Message broker, shared memory, and communication protocols
- ```
workflow_management.py
```
  - Workflow execution, optimization, and monitoring
- ```
benchmarking.py
```
  - Team performance and agent effectiveness metrics

通过示例和工具快速上手多智能体实现：

示例：查看
```
examples/
```
目录获取完整实现：
- ```
orchestration_patterns.py
```
  - 顺序、并行、分层以及共识编排模式
- ```
framework_implementations.py
```
  - CrewAI、AutoGen、LangGraph和Swarm的实现模板
工具：查看
```
scripts/
```
目录获取辅助模块：
- ```
agent_communication.py
```
  - 消息代理、共享内存和通信协议
- ```
workflow_management.py
```
  - 工作流执行、优化与监控
- ```
benchmarking.py
```
  - 团队性能与智能体有效性指标

Overview

概述

Multi-agent systems decompose complex problems into specialized sub-tasks, assigning each to an agent with relevant expertise, then coordinating their work toward a unified goal.

多智能体系统将复杂问题分解为专业子任务，为每个任务分配具备相关专业知识的智能体，然后协调它们的工作以达成统一目标。

When Multi-Agent Systems Shine

多智能体系统的适用场景

Complex Workflows: Tasks requiring multiple specialized roles
Domain-Specific Expertise: Finance, legal, HR, engineering need different knowledge
Parallel Processing: Multiple agents work on different aspects simultaneously
Collaborative Reasoning: Agents debate, refine, and improve solutions
Resilience: Failures in one agent don't break the entire system
Scalability: Easy to add new specialized agents

复杂工作流：需要多个专业角色的任务
领域特定专业知识：金融、法律、人力资源、工程等领域需要不同的知识储备
并行处理：多个智能体同时处理不同方面的任务
协作推理：智能体通过辩论、优化来改进解决方案
韧性：单个智能体故障不会导致整个系统崩溃
可扩展性：可轻松添加新的专业智能体

Architecture Overview

架构概述

User Request
    ↓
Orchestrator
    ├→ Agent 1 (Specialist) → Task 1
    ├→ Agent 2 (Specialist) → Task 2
    ├→ Agent 3 (Specialist) → Task 3
    ↓
Result Aggregator
    ↓
Final Response

用户请求
    ↓
编排器
    ├→ 智能体1（专业型）→ 任务1
    ├→ 智能体2（专业型）→ 任务2
    ├→ 智能体3（专业型）→ 任务3
    ↓
结果聚合器
    ↓
最终响应

Core Concepts

核心概念

Agent Definition

智能体定义

An agent is defined by:

Role: What responsibility does it have? (e.g., "Financial Analyst")
Goal: What should it accomplish? (e.g., "Analyze financial risks")
Expertise: What knowledge/tools does it have?
Tools: What capabilities can it access?
Context: What information does it need to work effectively?

智能体由以下要素定义：

角色：它承担什么职责？（例如："金融分析师"）
目标：它需要完成什么？（例如："分析金融风险"）
专业知识：它具备哪些知识/工具？
工具：它可以访问哪些能力？
上下文：它有效工作需要哪些信息？

Orchestration Patterns

编排模式

1. Sequential Orchestration

1. 顺序编排

Agents work one after another
Each agent uses output from previous agent
Use Case: Steps must follow order (research → analysis → writing)

智能体按顺序工作
每个智能体使用前一个智能体的输出
适用场景：步骤必须遵循特定顺序（研究 → 分析 → 撰写）

2. Parallel Orchestration

2. 并行编排

Multiple agents work simultaneously
Results aggregated at the end
Use Case: Independent tasks (analyze competitors, market, users)

多个智能体同时工作
最终聚合结果
适用场景：独立任务（分析竞争对手、市场、用户）

3. Hierarchical Orchestration

3. 分层编排

Senior agent delegates to junior agents
Manager coordinates flow
Use Case: Large projects with oversight

高级智能体将任务委托给初级智能体
管理者协调工作流
适用场景：需要监督的大型项目

4. Consensus-Based Orchestration

4. 基于共识的编排

Multiple agents analyze problem
Debate and refine ideas
Vote or reach consensus
Use Case: Complex decisions needing multiple perspectives

多个智能体分析问题
辩论并优化想法
投票或达成共识
适用场景：需要多视角的复杂决策

5. Tool-Mediated Orchestration

5. 工具介导的编排

Agents use shared tools/databases
Minimal direct communication
Use Case: Large systems, indirect coordination

智能体使用共享工具/数据库
极少直接通信
适用场景：大型系统、间接协调

Multi-Agent Team Examples

多智能体团队示例

Finance Team

金融团队

Coordinator Agent
    ├→ Market Analyst Agent
    │   ├ Tools: Market data API, financial news
    │   └ Task: Analyze market conditions
    ├→ Financial Analyst Agent
    │   ├ Tools: Financial statements, ratio calculations
    │   └ Task: Analyze company financials
    ├→ Risk Manager Agent
    │   ├ Tools: Risk models, scenario analysis
    │   └ Task: Assess investment risks
    └→ Report Writer Agent
        ├ Tools: Document generation
        └ Task: Synthesize findings into report

协调智能体
    ├→ 市场分析师智能体
    │   ├ 工具：市场数据API、财经新闻
    │   └ 任务：分析市场状况
    ├→ 金融分析师智能体
    │   ├ 工具：财务报表、比率计算
    │   └ 任务：分析公司财务状况
    ├→ 风险经理智能体
    │   ├ 工具：风险模型、场景分析
    │   └ 任务：评估投资风险
    └→ 报告撰写智能体
        ├ 工具：文档生成
        └ 任务：将研究结果整合为报告

Legal Team

法律团队

Case Manager Agent (Coordinator)
    ├→ Contract Analyzer Agent
    │   └ Task: Review contract terms
    ├→ Precedent Research Agent
    │   └ Task: Find relevant case law
    ├→ Risk Assessor Agent
    │   └ Task: Identify legal risks
    └→ Document Drafter Agent
        └ Task: Prepare legal documents

案件管理智能体（协调者）
    ├→ 合同分析智能体
    │   └ 任务：审查合同条款
    ├→ 先例研究智能体
    │   └ 任务：查找相关判例法
    ├→ 风险评估智能体
    │   └ 任务：识别法律风险
    └→ 文档起草智能体
        └ 任务：准备法律文件

Customer Support Team

客户支持团队

Support Coordinator
    ├→ Issue Classifier Agent
    │   └ Task: Categorize customer issue
    ├→ Knowledge Base Agent
    │   └ Task: Find relevant documentation
    ├→ Escalation Agent
    │   └ Task: Determine if human escalation needed
    └→ Solution Synthesizer Agent
        └ Task: Prepare comprehensive response

支持协调者
    ├→ 问题分类智能体
    │   └ 任务：分类客户问题
    ├→ 知识库智能体
    │   └ 任务：查找相关文档
    ├→ 升级处理智能体
    │   └ 任务：判断是否需要人工介入
    └→ 解决方案整合智能体
        └ 任务：准备全面的响应

Implementation Frameworks

实现框架

1. CrewAI

Best For: Teams with clear roles and hierarchical structure

python

from crewai import Agent, Task, Crew

最佳适用场景：角色明确、层级结构清晰的团队

python

from crewai import Agent, Task, Crew

Define agents

analyst = Agent( role="Financial Analyst", goal="Analyze financial data and provide insights", backstory="Expert in financial markets with 10+ years experience" )

researcher = Agent( role="Market Researcher", goal="Research market trends and competition", backstory="Data-driven researcher specializing in market analysis" )

analyst = Agent( role="Financial Analyst", goal="Analyze financial data and provide insights", backstory="Expert in financial markets with 10+ years experience" )

researcher = Agent( role="Market Researcher", goal="Research market trends and competition", backstory="Data-driven researcher specializing in market analysis" )

Define tasks

analysis_task = Task( description="Analyze Q3 financial results for {company}", agent=analyst, tools=[financial_tool, data_tool] )

research_task = Task( description="Research competitive landscape in {market}", agent=researcher, tools=[web_search_tool, industry_data_tool] )

analysis_task = Task( description="Analyze Q3 financial results for {company}", agent=analyst, tools=[financial_tool, data_tool] )

research_task = Task( description="Research competitive landscape in {market}", agent=researcher, tools=[web_search_tool, industry_data_tool] )

Create crew and execute

crew = Crew( agents=[analyst, researcher], tasks=[analysis_task, research_task], process=Process.sequential )

result = crew.kickoff(inputs={"company": "TechCorp", "market": "AI"})

undefined

crew = Crew( agents=[analyst, researcher], tasks=[analysis_task, research_task], process=Process.sequential )

result = crew.kickoff(inputs={"company": "TechCorp", "market": "AI"})

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2. AutoGen (Microsoft)

Best For: Complex multi-turn conversations and negotiations

python

from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager

最佳适用场景：复杂多轮对话与协商

python

from autogen import AssistantAgent, UserProxyAgent, GroupChat, GroupChatManager

Define agents

analyst = AssistantAgent( name="analyst", system_message="You are a financial analyst..." )

researcher = AssistantAgent( name="researcher", system_message="You are a market researcher..." )

analyst = AssistantAgent( name="analyst", system_message="You are a financial analyst..." )

researcher = AssistantAgent( name="researcher", system_message="You are a market researcher..." )

Create group chat

groupchat = GroupChat( agents=[analyst, researcher], messages=[], max_round=10, speaker_selection_method="auto" )

Manage group conversation

manager = GroupChatManager(groupchat=groupchat)

User proxy to initiate conversation

user = UserProxyAgent(name="user")

Have conversation

user.initiate_chat( manager, message="Analyze if Company X should invest in Y market" )

undefined

user.initiate_chat( manager, message="Analyze if Company X should invest in Y market" )

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3. LangGraph

Best For: Complex workflows with state management

python

from langgraph.graph import Graph, StateGraph
from langgraph.prebuilt import create_agent_executor

最佳适用场景：带状态管理的复杂工作流

python

from langgraph.graph import Graph, StateGraph
from langgraph.prebuilt import create_agent_executor

Define state

class AgentState: research_findings: str analysis: str recommendations: str

Create graph

graph = StateGraph(AgentState)

Add nodes for each agent

graph.add_node("researcher", research_agent) graph.add_node("analyst", analyst_agent) graph.add_node("writer", writer_agent)

Define edges (workflow)

graph.add_edge("researcher", "analyst") graph.add_edge("analyst", "writer")

Set entry/exit points

graph.set_entry_point("researcher") graph.set_finish_point("writer")

Compile and run

workflow = graph.compile() result = workflow.invoke({"topic": "AI trends"})

undefined

workflow = graph.compile() result = workflow.invoke({"topic": "AI trends"})

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4. OpenAI Swarm

Best For: Simple agent handoffs and conversational workflows

python

from swarm import Agent, Swarm

最佳适用场景：简单智能体交接与对话式工作流

python

from swarm import Agent, Swarm

Define agents

triage_agent = Agent( name="Triage Agent", instructions="Determine which specialist to route the customer to" )

billing_agent = Agent( name="Billing Specialist", instructions="Handle billing and payment questions" )

technical_agent = Agent( name="Technical Support", instructions="Handle technical issues" )

triage_agent = Agent( name="Triage Agent", instructions="Determine which specialist to route the customer to" )

billing_agent = Agent( name="Billing Specialist", instructions="Handle billing and payment questions" )

technical_agent = Agent( name="Technical Support", instructions="Handle technical issues" )

Define handoff functions

def route_to_billing(reason: str): return billing_agent

def route_to_technical(reason: str): return technical_agent

def route_to_billing(reason: str): return billing_agent

def route_to_technical(reason: str): return technical_agent

Add tools to triage agent

triage_agent.functions = [route_to_billing, route_to_technical]

Execute swarm

client = Swarm() response = client.run( agent=triage_agent, messages=[{"role": "user", "content": "I have a billing question"}] )

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client = Swarm() response = client.run( agent=triage_agent, messages=[{"role": "user", "content": "I have a billing question"}] )

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Orchestration Patterns

编排模式

Pattern 1: Sequential Task Chain

模式1：顺序任务链

Agents execute tasks in sequence, each building on previous results:

python

undefined

智能体按顺序执行任务，每个任务基于前一个的结果：

python

undefined

Task 1: Research

research_output = research_agent.work("Analyze AI market trends")

Task 2: Analysis (uses research output)

analysis = analyst_agent.work(f"Analyze these findings: {research_output}")

Task 3: Report (uses analysis)

report = writer_agent.work(f"Write report on: {analysis}")


**When to Use**: Steps have dependencies, each builds on previous

report = writer_agent.work(f"Write report on: {analysis}")


**适用场景**：步骤存在依赖关系，每个步骤基于前序结果

Pattern 2: Parallel Execution

模式2：并行执行

Multiple agents work simultaneously, results combined:

python

import asyncio

async def parallel_teams():
    # All agents work in parallel
    market_task = market_agent.work_async("Analyze market")
    technical_task = tech_agent.work_async("Analyze technology")
    user_task = user_agent.work_async("Analyze user needs")

    # Wait for all to complete
    market_results, tech_results, user_results = await asyncio.gather(
        market_task, technical_task, user_task
    )

    # Synthesize results
    return synthesize(market_results, tech_results, user_results)

When to Use: Independent analyses, need quick results, want diversity

多个智能体同时工作，结果合并：

python

import asyncio

async def parallel_teams():
    # All agents work in parallel
    market_task = market_agent.work_async("Analyze market")
    technical_task = tech_agent.work_async("Analyze technology")
    user_task = user_agent.work_async("Analyze user needs")

    # Wait for all to complete
    market_results, tech_results, user_results = await asyncio.gather(
        market_task, technical_task, user_task
    )

    # Synthesize results
    return synthesize(market_results, tech_results, user_results)

适用场景：独立分析、需要快速结果、追求多样性

Pattern 3: Hierarchical Structure

模式3：层级结构

Manager agent coordinates specialists:

python

manager_agent.orchestrate({
    "market_analysis": {
        "agents": [competitor_analyst, trend_analyst],
        "task": "Comprehensive market analysis"
    },
    "technical_evaluation": {
        "agents": [architecture_agent, security_agent],
        "task": "Technical feasibility assessment"
    },
    "synthesis": {
        "agents": [strategy_agent],
        "task": "Create strategic recommendations"
    }
})

When to Use: Clear hierarchy, different teams, complex coordination

管理智能体协调专业智能体：

python

manager_agent.orchestrate({
    "market_analysis": {
        "agents": [competitor_analyst, trend_analyst],
        "task": "Comprehensive market analysis"
    },
    "technical_evaluation": {
        "agents": [architecture_agent, security_agent],
        "task": "Technical feasibility assessment"
    },
    "synthesis": {
        "agents": [strategy_agent],
        "task": "Create strategic recommendations"
    }
})

适用场景：清晰的层级结构、不同团队、复杂协调

Pattern 4: Debate & Consensus

模式4：辩论与共识

Multiple agents discuss and reach consensus:

python

agents = [bull_agent, bear_agent, neutral_agent]
question = "Should we invest in this startup?"

多个智能体讨论并达成共识：

python

agents = [bull_agent, bear_agent, neutral_agent]
question = "Should we invest in this startup?"

Debate round 1

arguments = {agent: agent.argue(question) for agent in agents}

Debate round 2 (respond to others)

counter_arguments = { agent: agent.respond(arguments) for agent in agents }

Reach consensus

consensus = mediator_agent.synthesize_consensus(counter_arguments)


**When to Use**: Complex decisions, need multiple perspectives, risk assessment

consensus = mediator_agent.synthesize_consensus(counter_arguments)


**适用场景**：复杂决策、需要多视角、风险评估

Agent Communication Patterns

智能体通信模式

1. Direct Communication

1. 直接通信

Agents pass messages directly to each other:

python

agent_a.send_message(agent_b, {
    "type": "request",
    "action": "analyze_document",
    "document": doc_content,
    "context": {"deadline": "urgent"}
})

智能体之间直接传递消息：

python

agent_a.send_message(agent_b, {
    "type": "request",
    "action": "analyze_document",
    "document": doc_content,
    "context": {"deadline": "urgent"}
})

2. Tool-Mediated Communication

2. 工具介导的通信

Agents use shared tools/databases:

python

undefined

智能体使用共享工具/数据库：

python

undefined

Agent A writes to shared memory

shared_memory.write("findings", {"market_size": "$5B", "growth": "20%"})

Agent B reads from shared memory

findings = shared_memory.read("findings")

undefined

findings = shared_memory.read("findings")

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3. Manager-Based Communication

3. 基于管理者的通信

Central coordinator manages agent communication:

python

manager.broadcast("update_all_agents", {
    "new_deadline": "tomorrow",
    "priority": "critical"
})

中央协调者管理智能体通信：

python

manager.broadcast("update_all_agents", {
    "new_deadline": "tomorrow",
    "priority": "critical"
})

Best Practices

最佳实践

Agent Design

智能体设计

✓ Clear, specific role and goal
✓ Appropriate tools for the role
✓ Relevant background/expertise
✓ Distinct from other agents
✓ Reasonable scope of work

✓ 清晰、明确的角色与目标
✓ 为角色配备合适的工具
✓ 相关的背景/专业知识
✓ 与其他智能体职责区分
✓ 合理的工作范围

Workflow Design

工作流设计

✓ Clear task dependencies
✓ Identified handoff points
✓ Error handling between agents
✓ Fallback strategies
✓ Performance monitoring

✓ 清晰的任务依赖关系
✓ 明确的交接点
✓ 智能体之间的错误处理
✓ fallback策略
✓ 性能监控

Communication

通信

✓ Structured message formats
✓ Clear context sharing
✓ Error propagation strategy
✓ Timeout handling
✓ Audit logging

✓ 结构化的消息格式
✓ 清晰的上下文共享
✓ 错误传播策略
✓ 超时处理
✓ 审计日志

Orchestration

编排

✓ Define process clearly (sequential, parallel, etc.)
✓ Set clear success criteria
✓ Monitor agent performance
✓ Implement feedback loops
✓ Allow human intervention points

✓ 明确定义流程（顺序、并行等）
✓ 设置清晰的成功标准
✓ 监控智能体性能
✓ 实现反馈循环
✓ 预留人工介入点

Common Challenges & Solutions

常见挑战与解决方案

Challenge: Agent Conflicts

挑战：智能体冲突

Solutions:

Clear role separation
Explicit decision-making rules
Consensus mechanisms
Conflict resolution agent
Clear authority hierarchy

解决方案：

清晰的角色划分
明确的决策规则
共识机制
冲突解决智能体
清晰的权限层级

Challenge: Slow Execution

挑战：执行缓慢

Solutions:

Use parallel execution where possible
Cache results from expensive operations
Pre-process data
Optimize agent logic
Implement timeout handling

解决方案：

尽可能使用并行执行
缓存高成本操作的结果
预处理数据
优化智能体逻辑
实现超时处理

Challenge: Poor Quality Results

挑战：结果质量差

Solutions:

Better agent prompts/instructions
More relevant tools
Feedback integration
Quality validation agents
Result aggregation strategies

解决方案：

更优的智能体提示词/指令
更相关的工具
集成反馈
质量验证智能体
结果聚合策略

Challenge: Complex Workflows

挑战：复杂工作流

Solutions:

Break into smaller teams
Hierarchical structure
Clear task definitions
Good state management
Documentation of workflow

解决方案：

拆分为更小的团队
层级结构
清晰的任务定义
良好的状态管理
工作流文档

Evaluation Metrics

评估指标

Team Performance:

Task completion rate
Quality of results
Execution time
Cost (tokens/API calls)
Error rate

Agent Effectiveness:

Task success rate
Response quality
Tool usage efficiency
Communication clarity
Collaboration score

团队性能：

任务完成率
结果质量
执行时间
成本（令牌/API调用）
错误率

智能体有效性：

任务成功率
响应质量
工具使用效率
通信清晰度
协作得分

Advanced Techniques

高级技术

1. Self-Organizing Teams

1. 自组织团队

Agents autonomously decide roles and workflow:

python

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智能体自主决定角色与工作流：

python

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Agents negotiate roles based on task

agents = [agent1, agent2, agent3] task = "complex financial analysis"

Agents determine best structure

negotiated_structure = self_organize(agents, task)

Returns optimal workflow for this task

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2. Adaptive Workflows

2. 自适应工作流

Workflow changes based on progress:

python

undefined

工作流根据进度调整：

python

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Monitor progress

if progress < expected_rate: # Increase resources workflow.add_agent(specialist_agent) elif quality < threshold: # Increase validation workflow.insert_review_step()

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if progress < expected_rate: # Increase resources workflow.add_agent(specialist_agent) elif quality < threshold: # Increase validation workflow.insert_review_step()

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3. Cross-Agent Learning

3. 跨智能体学习

Agents learn from each other's work:

python

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智能体从彼此的工作中学习：

python

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After team execution

execution_trace = crew.get_execution_trace()

Extract learnings

learnings = extract_patterns(execution_trace)

Update agent knowledge

for agent, learning in learnings.items(): agent.update_knowledge(learning)

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Resources

资源

Frameworks

框架

CrewAI: https://crewai.com/
AutoGen: https://microsoft.github.io/autogen/
LangGraph: https://langchain-ai.github.io/langgraph/
Swarm: https://github.com/openai/swarm

CrewAI: https://crewai.com/
AutoGen: https://microsoft.github.io/autogen/
LangGraph: https://langchain-ai.github.io/langgraph/
Swarm: https://github.com/openai/swarm

Papers

论文

"Generative Agents" (Park et al.)
"Self-Organizing Multi-Agent Systems" (research papers)

"Generative Agents" (Park et al.)
"Self-Organizing Multi-Agent Systems" (研究论文)

Implementation Checklist

实施检查清单

Getting Started

入门指南

Start Small: Begin with 2-3 agents
Clear Workflow: Document how agents interact
Test Thoroughly: Validate agent behavior individually and together
Monitor Closely: Track performance and results
Iterate: Refine based on results
Scale: Add agents and complexity as needed

从小规模开始：从2-3个智能体起步
清晰的工作流：记录智能体的交互方式
充分测试：单独与协同验证智能体行为
密切监控：跟踪性能与结果
迭代优化：根据结果改进
逐步扩展：根据需要添加智能体与复杂度

multi-agent-orchestration

Original

Translation

Multi-Agent Orchestration

多智能体编排

Quick Start

快速开始

Overview

概述

When Multi-Agent Systems Shine

多智能体系统的适用场景

Architecture Overview

架构概述

Core Concepts

核心概念

Agent Definition

智能体定义

Orchestration Patterns

编排模式

1. Sequential Orchestration

1. 顺序编排

2. Parallel Orchestration

2. 并行编排

3. Hierarchical Orchestration

3. 分层编排

4. Consensus-Based Orchestration

4. 基于共识的编排

5. Tool-Mediated Orchestration

5. 工具介导的编排

Multi-Agent Team Examples

多智能体团队示例

Finance Team

金融团队

Legal Team

法律团队

Customer Support Team

客户支持团队

Implementation Frameworks

实现框架

1. CrewAI

1. CrewAI

Define agents

Define agents

Define tasks

Define tasks

Create crew and execute

Create crew and execute

2. AutoGen (Microsoft)

2. AutoGen (Microsoft)

Define agents

Define agents

Create group chat

Create group chat

Manage group conversation

Manage group conversation

User proxy to initiate conversation

User proxy to initiate conversation

Have conversation

Have conversation

3. LangGraph

3. LangGraph

Define state

Define state

Create graph

Create graph

Add nodes for each agent

Add nodes for each agent

Define edges (workflow)

Define edges (workflow)

Set entry/exit points

Set entry/exit points

Compile and run

Compile and run

4. OpenAI Swarm

4. OpenAI Swarm

Define agents

Define agents

Define handoff functions

Define handoff functions

Add tools to triage agent