Otonom Yapay Zeka Ajanları ve Çoklu Ajan İş Akışları (LangGraph, CrewAI, AutoGen)

This comprehensive guide explores autonomous AI agents and multi-agent workflows using three major frameworks: LangGraph, CrewAI, and AutoGen. The content covers how these platforms enable building intelligent systems where multiple AI agents collaborate to solve complex tasks. It demonstrates practical implementations of agent-based architectures and their applications in modern AI development.

Key Points

• •LangGraph enables building stateful, graph-based agent workflows with explicit control flow and memory management
• •CrewAI provides a high-level framework for orchestrating multiple specialized agents with defined roles and responsibilities
• •AutoGen facilitates multi-agent conversations and collaboration patterns for complex problem-solving scenarios
• •Agent frameworks abstract away complexity of prompt engineering and enable focus on workflow design and task decomposition
• •Multi-agent systems excel at tasks requiring diverse expertise, parallel processing, and iterative refinement
• •State management and message passing are critical for coordinating actions across multiple autonomous agents
• •Each framework offers different trade-offs between flexibility (LangGraph), ease-of-use (CrewAI), and conversation patterns (AutoGen)
• •Agent architectures support hierarchical task decomposition where complex problems are broken into specialized sub-tasks

Workflow Diagram

Concepts