---
title: "Agentic AI Stack"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Agentic AI Stack

[Must Read: The AI Agents Stack](https://www.letta.com/blog/ai-agents-stack)

Developing AI agents requires a comprehensive stack that integrates various components to enable autonomous, intelligent behavior. This stack encompasses layers from foundational models to deployment frameworks, each playing a crucial role in the agent's functionality. Two notable frameworks within this ecosystem are **CrewAI** and **LangGraph**, each offering unique approaches to building and orchestrating AI agents.

**1. AI Agents Development Stack Overview**

The AI agents stack can be conceptualized into several key layers:

- **Model Serving**: At the core are Large Language Models (LLMs) that process and generate human-like text. These models are served via inference engines, often through APIs provided by platforms like OpenAI and Anthropic. 

- **Storage**: Agents require mechanisms to store and retrieve information, such as conversation history and external data sources. Vector databases like Chroma, Weaviate, and Pinecone facilitate this by enabling efficient embedding-based search and storage. 

- **Tools & Libraries**: To perform tasks beyond text generation, agents utilize tools and functions. Frameworks like LangChain provide the necessary infrastructure for integrating these tools, allowing agents to execute actions and interact with external systems. 

- **Agent Frameworks**: These frameworks orchestrate the components, managing state, tool execution, and interactions. CrewAI and LangGraph are prominent examples, each offering distinct methodologies for agent development.

**2. CrewAI: Role-Based Multi-Agent Collaboration**

CrewAI is designed to facilitate the collaboration of role-based AI agents, enabling them to operate as a cohesive unit. Each agent is assigned specific roles, backgrounds, goals, and memories, allowing for structured and efficient task execution. This framework is particularly advantageous for building sophisticated multi-agent systems, such as research teams or complex problem-solving units. 

**Key Features of CrewAI**:

- **User-Friendly Interface**: CrewAI offers an intuitive interface, making it accessible for developers without a steep learning curve. 

- **Integration with LangChain**: Built atop the LangChain framework, CrewAI provides access to a wide array of tools and models, enhancing the agents' capabilities. 

- **Advanced Monitoring and Reporting**: The framework includes tools for tracking task progress and agent performance, facilitating detailed analysis of AI-driven processes. 

**3. LangGraph: Graph-Based Workflow Orchestration**

LangGraph adopts a graph architecture to define and orchestrate agentic workflows, treating each task or function as a node within langgraph in a cyclic workflow. This approach provides fine-grained control over the flow and state of applications, making it particularly suitable for complex workflows that require advanced memory features, error recovery, and human-in-the-loop interactions. 

**Key Features of LangGraph**:

- **Seamless Integration with LangChain**: LangGraph integrates with LangChain, granting access to a broad ecosystem of tools and models. 

- **Support for Multi-Agent Interaction Patterns**: The framework accommodates various multi-agent interaction patterns, enabling the development of collaborative agent systems. 

- **Fine-Grained Workflow Control**: LangGraph's graph-based approach allows for detailed management of workflows, beneficial for applications requiring complex orchestration. 

**4. Comparative Analysis: CrewAI vs. LangGraph**

Both CrewAI and LangGraph offer robust solutions for AI agent development, each with its strengths:

- **Ease of Use**: CrewAI provides an intuitive interface, facilitating quick onboarding for developers. In contrast, LangGraph's graph-based structure may require a deeper understanding of graph architectures but offers greater control over workflows. 

- **Customization and Flexibility**: LangGraph's design allows for extensive customization, making it suitable for complex applications. CrewAI, while user-friendly, may present challenges in customization due to its opinionated framework. 

- **Integration Capabilities**: Both frameworks integrate with LangChain, providing access to a comprehensive range of tools and models. 

**5. Conclusion**

The development of AI agents necessitates a well-structured stack that integrates model serving, storage, tools, and orchestration frameworks. CrewAI and LangGraph represent two distinct approaches within this ecosystem, each catering to different development needs. Understanding their unique features and capabilities enables developers to select the most appropriate framework for their specific applications, thereby advancing the field of autonomous AI systems.
