AI Agent Frameworks
Frameworks for building multi-agent systems — autonomous task completion, collaborative problem-solving, and orchestrated AI workflows.
What Agent Frameworks Solve
Single-LLM applications are limited by one model's capabilities. Agent frameworks enable:
- Multi-agent collaboration — specialized agents working together on complex tasks
- Tool use — agents that interact with APIs, databases, and services
- Autonomous workflows — multi-step task completion without human intervention
- Human-in-the-loop — agents that request human approval at critical decision points
Tool Comparison
| Feature | CrewAI | AutoGen |
|---|---|---|
| Mental Model | Crew of role-based agents with processes | Conversational agents via message passing |
| Agent Definition | Role + Goal + Backstory (declarative) | AssistantAgent + UserProxy (code-first) |
| Orchestration | Sequential, hierarchical, consensual | Flexible conversation patterns, GroupChat |
| Human-in-Loop | Via human input tool | First-class via UserProxyAgent |
| Code Execution | Tool-based | Built-in sandbox with code gen |
| Learning Curve | Lower — intuitive crew metaphor | Moderate — conversation pattern complexity |
| Maintained By | CrewAI (startup) | Microsoft Research |
| Best For | Content pipelines, business automation | Code generation, data analysis, research |
CrewAI
Framework for orchestrating multi-agent AI systems.
CrewAI uses a crew metaphor — agents have roles, goals, and backstories, and work together through defined processes (sequential, hierarchical, or consensual).
Architecture
┌──────────────────────────────────────────────────┐
│ Crew │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ Agent 1 │ │ Agent 2 │ │ Agent 3 │ │
│ │ Research │ │ Analysis │ │ Writing │ │
│ │ Analyst │ │ Expert │ │ Specialist│ │
│ └────┬─────┘ └────┬─────┘ └────┬─────┘ │
│ │ │ │ │
│ ┌────▼──────────────▼──────────────▼──────────┐ │
│ │ Process Engine │ │
│ │ Sequential │ Hierarchical │ Consensual │ │
│ └──────────────────┬──────────────────────────┘ │
│ │ │
│ ┌──────────────────▼──────────────────────────┐ │
│ │ Tool Registry │ │
│ │ Search · Browser · Code · API · Custom │ │
│ └──────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────┘
Use Cases
- Content pipelines — research, write, edit, and publish content
- Business process automation — structured multi-step workflows
- Code review — agents that analyze, review, and suggest improvements
- Research workflows — agents that search, synthesize, and report findings
When to Choose CrewAI
Choose CrewAI when tasks can be clearly divided among specialized agents with defined roles. Best for structured business workflows, content generation, and process automation.
AutoGen
Multi-agent conversational AI framework by Microsoft Research.
AutoGen enables building systems where agents communicate through message passing — supporting collaborative problem-solving, code generation, and human-AI interaction patterns.
Architecture
┌──────────────────────────────────────────────────┐
│ AutoGen GroupChat │
│ │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │Assistant │◄─►│Assistant │◄─►│UserProxy │ │
│ │Agent 1 │ │Agent 2 │ │Agent │ │
│ │(Analyst) │ │(Coder) │ │(Human) │ │
│ └──────────┘ └──────────┘ └──────────┘ │
│ │ │ │ │
│ └──────────────┴──────────────┘ │
│ │ │
│ Message Passing Protocol │
│ │ │
│ ┌─────────────────▼────────────────────────────┐ │
│ │ Code Execution Sandbox │ │
│ │ Python · Shell · Jupyter · Custom │ │
│ └──────────────────────────────────────────────┘ │
└──────────────────────────────────────────────────┘
Use Cases
- Code generation — agents that write, test, and refine code iteratively
- Data analysis — collaborative agents that query, analyze, and visualize data
- Research — agents that debate, critique, and refine analysis
- Human-AI collaboration — tight integration with human reviewers and approvers
When to Choose AutoGen
Choose AutoGen when tasks require iterative collaboration between agents — especially code generation, data analysis, and problems that benefit from agent-to-agent conversation.
DevOps for Agent Systems
Deploying agents in production requires specialized CI/CD, testing, and monitoring practices:
→ DevOps for AI Agents Architecture Guide →