Background: Multi-Agent Framework Comparison

Multi-agent systems are becoming the standard architecture for complex AI applications. This article compares the features and use cases of mainstream frameworks.

1. Framework Overview

2. Framework Details

2.1 Shannon

Positioning: Production-grade multi-agent orchestration platform

Core Features:

• Go + Rust + Python multi-language architecture
• Temporal workflow engine (deterministic replay)
• Built-in budget control and cost tracking
• WASI sandbox execution
• OPA policy engine

Architecture:

Orchestrator (Go)     → Task routing, budget control
Agent Core (Rust)     → Sandbox execution
LLM Service (Python)  → Model calls, tool execution
Temporal              → Workflow orchestration

Use Cases:

• Production environment deployment
• Cost control requirements
• Deterministic debugging needs
• Security isolation requirements

Example:

curl -X POST http://localhost:8080/api/v1/tasks \
  -d '{"query": "Analyze market data", "session_id": "quant-1"}'

2.2 AutoGen

Positioning: Microsoft's open-source conversational multi-agent framework

Core Features:

• Conversation-based Agent interaction
• Human-in-the-loop support
• Code execution capabilities
• Flexible Agent definitions

Architecture:

UserProxyAgent    → Represents user, can execute code
AssistantAgent    → LLM-powered assistant
GroupChat         → Multi-Agent conversation management

Use Cases:

• Research and prototyping
• Code generation tasks
• Human-AI collaboration needs
• Conversational workflows

Example:

from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent("assistant", llm_config=llm_config)
user_proxy = UserProxyAgent("user_proxy", code_execution_config={"work_dir": "coding"})

user_proxy.initiate_chat(assistant, message="Analyze this dataset")

2.3 CrewAI

Positioning: Role-playing multi-agent collaboration framework

Core Features:

• Clear role definitions (Role, Goal, Backstory)
• Task-driven
• Tool integration
• Simple and easy to use

Architecture:

Agent    → Define roles and capabilities
Task     → Define specific tasks
Crew     → Organize Agent collaboration
Tool     → Available tools for Agents

Use Cases:

• Rapid prototyping
• Tasks with clear roles
• Content generation
• Research assistants

Example:

from crewai import Agent, Task, Crew

researcher = Agent(
    role='Market Researcher',
    goal='Analyze market trends',
    backstory='You are a senior quantitative analyst'
)

task = Task(
    description='Analyze BTC trends over the past week',
    agent=researcher
)

crew = Crew(agents=[researcher], tasks=[task])
result = crew.kickoff()

2.4 LangGraph

Positioning: Graph-structured Agent workflow framework

Core Features:

• State machine + graph structure
• Loop and branch support
• LangChain ecosystem integration
• Workflow visualization

Architecture:

StateGraph   → Define states and transitions
Node         → Processing nodes (functions or Agents)
Edge         → State transition logic
Checkpointer → State persistence

Use Cases:

• Complex workflows
• Loop logic requirements
• State management needs
• LangChain users

Example:

from langgraph.graph import StateGraph

workflow = StateGraph(AgentState)
workflow.add_node("analyze", analyze_market)
workflow.add_node("decide", make_decision)
workflow.add_edge("analyze", "decide")

app = workflow.compile()
result = app.invoke({"input": "Analyze market"})

2.5 MetaGPT

Positioning: Multi-agent system for software development

Core Features:

• Simulates software teams
• Roles: PM, Architect, Engineer, QA
• Document-driven development
• Code generation

Architecture:

ProductManager → Requirements analysis
Architect      → System design
Engineer       → Code implementation
QA             → Testing and validation

Use Cases:

• Software development tasks
• Code generation
• Technical documentation generation

3. Comparison Matrix

4. Quant Trading Scenario Fit

4.1 Scenario: Market Analysis Multi-Agent

4.2 Scenario: Real-Time Trade Execution

Reasons:

• Shannon has production-grade orchestration and security sandbox
• AutoGen/CrewAI lack execution control
• Real-time trading requires low latency and high reliability

4.3 Scenario: Strategy Research Prototyping

Reason: Research phase needs rapid iteration, ease of use matters more

5. Architecture Selection Decision Tree

What is your main requirement?
│
├─ Production Deployment
│   ├─ Need cost control → Shannon
│   ├─ Need deterministic replay → Shannon
│   └─ Need security sandbox → Shannon
│
├─ Rapid Prototyping
│   ├─ Conversational interaction → AutoGen
│   ├─ Role-playing tasks → CrewAI
│   └─ Complex workflows → LangGraph
│
├─ Code Generation Tasks
│   ├─ Need human-AI collaboration → AutoGen
│   └─ Simulate dev team → MetaGPT
│
└─ Quant Trading
    ├─ Research phase → AutoGen / CrewAI
    └─ Live trading → Shannon

6. Integration Examples

6.1 Shannon + Quant Strategy

from shannon import ShannonClient

client = ShannonClient(base_url="http://localhost:8080")

# Submit analysis task
handle = client.submit_task(
    query="Analyze BTC/USDT market state, determine if trending or ranging",
    session_id="regime-detection"
)

# Wait for result
result = client.wait(handle.task_id)

6.2 AutoGen + Quant Research

from autogen import AssistantAgent, UserProxyAgent

quant_analyst = AssistantAgent(
    "quant_analyst",
    system_message="You are a quantitative analyst skilled in market data analysis and strategy design",
    llm_config=llm_config
)

user = UserProxyAgent(
    "user",
    code_execution_config={"work_dir": "research"}
)

user.initiate_chat(
    quant_analyst,
    message="Design an RSI-based mean reversion strategy"
)

6.3 CrewAI + Research Team

from crewai import Agent, Task, Crew

analyst = Agent(
    role='Quant Analyst',
    goal='Analyze market data',
    tools=[market_data_tool]
)

developer = Agent(
    role='Strategy Developer',
    goal='Convert analysis into code',
    tools=[code_execution_tool]
)

reviewer = Agent(
    role='Risk Control Reviewer',
    goal='Check strategy risks',
    tools=[risk_analysis_tool]
)

crew = Crew(
    agents=[analyst, developer, reviewer],
    tasks=[...],
    process="sequential"
)

7. Future Trends

1. Standardization: Agent communication protocol standards
2. Specialization: Domain-specific vertical frameworks
3. Production-Ready: More frameworks supporting production deployment
4. Integration: Multi-framework integration becomes common
5. Low-Code: Visual Agent orchestration tools

8. Selection Recommendations

Core Principle: There's no perfect framework, only the right choice for your scenario. Use simple frameworks for rapid validation in research, use robust frameworks for reliable operation in production.