Open Source Agentic Frameworks: LangGraph vs CrewAI & More

This article explores the growing adoption of open-source agentic frameworks in AI development, focusing on LangGraph, SmolAgents by HuggingFace, CrewAI, PhiData, and Composio. Through a technical lens, we compare their architecture, use cases, customization options, and performance. This study aims to guide developers in choosing the most suitable framework for their needs while highlighting trends in multi-agent systems.

The Evolution of Agentic Frameworks

Agentic frameworks have revolutionized AI by enabling autonomous systems to perceive, reason, and act dynamically. This section explores the core concepts of agentic frameworks and highlights why open-source solutions are crucial for innovation and scalability in modern AI development.

What Are Agentic Frameworks?

Agentic frameworks represent a paradigm shift in how artificial intelligence systems are designed. Unlike traditional AI applications that rely on static, predefined workflows, agentic frameworks introduce dynamic, adaptive systems capable of perceiving, reasoning, and acting autonomously. These frameworks enable complex tasks to be broken into smaller subtasks, handled by specialized agents that collaborate to achieve broader objectives. By leveraging large language models (LLMs), agentic frameworks can manage workflows, make decisions, and integrate tools seamlessly, making them ideal for advanced applications such as dynamic decision-making and real-time problem-solving​​.

Key reference: Agentic frameworks such as LangGraph and CrewAI embody this dynamic approach, enabling developers to move beyond single-agent, linear workflows into multi-agent, collaborative systems​​.

Why Open Source?

Open-source frameworks have been a driving force behind the rapid adoption of agentic AI systems. They provide developers with the flexibility to customize and extend frameworks to meet specific needs while fostering community-driven innovation. Open-source projects lower barriers to entry, allowing smaller teams to access cutting-edge technologies without significant cost implications. Additionally, the collaborative nature of open-source development ensures faster iteration cycles, higher code quality, and robust solutions that benefit from collective expertise​​.

Key benefit: Open-source agentic frameworks like SmolAgents and PhiData empower developers with modular tools, allowing them to build scalable and reliable systems without being locked into proprietary ecosystems​​.

Comparative Overview of Frameworks

This section provides an in-depth comparison of LangGraph, SmolAgents, CrewAI, PhiData, and Composio. Each framework’s architecture, strengths, and ideal use cases are explored to help developers make informed decisions when choosing the right tool for their projects.

LangGraph

LangGraph excels in managing structured workflows using its graph-based architecture. By treating workflows as directed acyclic graphs (DAGs), LangGraph provides fine-grained control over task dependencies and process visualization. It is particularly effective in applications requiring contextual coherence, such as conversational AI and complex NLP workflows​​. LangGraph’s integration with LangChain offers seamless access to a broad ecosystem of tools and models, enabling powerful multi-agent interactions​​.

SmolAgents by HuggingFace

SmolAgents focuses on simplicity and lightweight implementation, making it ideal for developers who need quick, task-specific solutions. This minimalist framework integrates effortlessly with HuggingFace models and APIs, allowing for rapid deployment of agents for small-scale applications​​. SmolAgents is designed for single-purpose agents, providing an intuitive interface for tasks like text classification and data preprocessing​​.

CrewAI

CrewAI is tailored for collaborative multi-agent systems, where agents work together to achieve shared goals. Its dynamic task allocation and inter-agent communication features make it well-suited for industries like logistics, healthcare, and research​​. CrewAI excels in creating multi-agent workflows with role-based design, enabling seamless interaction between agents and human users​​.

PhiData

PhiData stands out as a data-centric framework, leveraging retrieval-augmented generation (RAG) techniques for dynamic decision-making. Its robust memory and tool integration capabilities make it suitable for real-time data analysis and information retrieval applications​​. PhiData’s modular design allows developers to build scalable, flexible systems tailored to specific data workflows​​.

Composio

Composio offers a modular and highly customizable approach to multi-agent system design. Its framework supports complex multi-step operations, making it ideal for projects requiring hierarchical planning and flexible task execution​​. Composio’s ability to handle complex dependencies and integrate real-time feedback positions it as a leader in adaptive system design​​.

Feature Comparison

In this section, we delve into the technical aspects of LangGraph, SmolAgents, CrewAI, PhiData, and Composio, comparing their architecture, memory management, tool integrations, and usability. These comparisons provide a comprehensive understanding of each framework’s strengths and potential limitations.

Architecture and Flexibility

LangGraph’s graph-based approach excels in visualizing dependencies, enabling developers to map complex workflows and interactions. It is well-suited for structured applications like NLP and conversational AI. SmolAgents, in contrast, emphasizes minimalism and is designed for lightweight, task-specific agents that can be easily deployed without much overhead. CrewAI offers role-based designs, focusing on collaboration and multi-agent workflows, which are ideal for systems where agents work towards shared objectives. PhiData integrates modularity and scalability into its design, enabling developers to build flexible systems for real-time data analysis. Composio stands out for its hierarchical task execution, supporting adaptive, multi-step processes​​​.

Memory and State Management

Effective memory handling is essential for coherent, context-aware AI agents. LangGraph supports both short-term and long-term memory through its integration with LangChain, making it suitable for applications requiring contextual continuity. SmolAgents adopts a simpler approach, often relying on external APIs for memory needs. CrewAI offers robust state management features that allow agents to track goals and tasks dynamically, supporting efficient multi-agent collaboration. PhiData leverages retrieval-augmented generation (RAG) techniques for memory handling, enabling agents to maintain and access relevant data efficiently. Composio also includes advanced state management for handling dependencies across complex workflows​​.

Tool Support and Extensions

Tool integration is a critical factor for extending agent functionality. LangGraph seamlessly integrates with LangChain, providing a wide range of tools and prebuilt components for NLP and multi-agent orchestration. SmolAgents focuses on API-based tool integrations, ensuring lightweight deployments. CrewAI incorporates flexible task-specific tools and supports dynamic task delegation, making it a versatile option for collaborative environments. PhiData emphasizes data-centric tools and retrieval methods, ideal for workflows involving large datasets. Composio, with its modular framework, supports a wide array of tools for hierarchical planning and real-time task execution​​.

Ease of Use and Adoption

LangGraph’s graph-based structure is intuitive for developers familiar with directed acyclic graphs (DAGs) but may present a learning curve for newcomers. SmolAgents is highly user-friendly, offering a straightforward interface for rapid deployment of single-purpose agents. CrewAI’s role-based framework provides an organized structure that simplifies the process of creating multi-agent systems. PhiData is flexible yet requires a solid understanding of retrieval-augmented workflows to unlock its full potential. Composio’s modularity ensures adaptability but may demand additional effort for configuring hierarchical workflows​​​.

Use Case Scenarios

This section highlights real-world applications of LangGraph, SmolAgents, CrewAI, PhiData, and Composio. By exploring industry use cases and performance benchmarks, we aim to provide a practical perspective on how these frameworks excel in specific scenarios.

Industries and Applications

Agentic frameworks have diverse applications across industries, leveraging their unique features to address real-world challenges. LangGraph is commonly used in conversational AI and NLP workflows, where contextual coherence and structured task dependencies are crucial. For instance, enterprises use LangGraph for customer service chatbots that handle multi-turn conversations with contextual continuity. SmolAgents, due to its simplicity, excels in specific tasks like document summarization, sentiment analysis, or lightweight data processing pipelines. It is popular among small-scale projects or startups that need quick, functional solutions.

CrewAI thrives in industries requiring collaboration, such as logistics, healthcare, and research. For example, CrewAI has been deployed to manage coordinated logistics in supply chain operations, where dynamic task allocation among multiple agents is critical. PhiData’s data-centric design makes it ideal for information retrieval, real-time analytics, and workflows involving large datasets. Its adoption is growing in fields like financial analysis and legal document processing, where rapid and accurate information extraction is paramount. Composio, with its hierarchical planning capabilities, is well-suited for adaptive and multi-step processes, such as project management or advanced decision-making systems in dynamic environments​​​.

Performance Benchmarks

Performance evaluation is a critical factor in selecting an agentic framework. LangGraph has demonstrated exceptional performance in structured NLP workflows, with notable efficiency in managing multi-turn dialogues and resolving complex dependencies. SmolAgents, while lightweight, maintains reliability for single-task operations, achieving quick execution and minimal setup time. CrewAI has excelled in simulations requiring collaboration, showing high success rates in distributed task execution and goal achievement across multi-agent systems. PhiData’s retrieval-augmented workflows ensure high accuracy in data retrieval tasks, making it particularly effective in knowledge-intensive applications. Composio’s strength lies in its ability to handle complex dependencies and adapt dynamically to changing requirements, which has been reflected in benchmarks evaluating hierarchical task execution​​​.

Trends and Future Directions

This section explores the emerging trends shaping agentic frameworks and their integration into advanced AI systems. It also discusses how these frameworks are influencing the future of AI development, with a focus on hierarchical planning, human-in-the-loop systems, and automated design.

Emerging Trends

Agentic AI frameworks are rapidly evolving to address growing demands for scalability, adaptability, and collaborative capabilities. One key trend is the rise of hierarchical planning, where agents can decompose complex tasks into manageable subtasks, ensuring precision and efficiency. This is particularly evident in frameworks like Composio and LangGraph, which excel in multi-step workflows. Another significant trend is the incorporation of human-in-the-loop systems, allowing agents to leverage human expertise for validation and decision-making in critical scenarios. CrewAI demonstrates this effectively in collaborative workflows that require seamless human-agent interaction.

Automated Design of Agentic Systems (ADAS) is another promising area, focusing on using meta-agents to design, evaluate, and optimize agentic systems. This approach reduces development effort and encourages the discovery of novel agent configurations. Frameworks such as PhiData are beginning to experiment with automated design techniques to improve efficiency and adaptability​​.

Overview of multi-agent architectures, including single agent, supervisor, hierarchical, and custom models, demonstrating the flexibility and scalability of agentic frameworks

Potential for Integration

As AI systems become more complex, agentic frameworks are being integrated with other advanced technologies to extend their capabilities. For instance, retrieval-augmented generation (RAG), a key feature of PhiData, is being combined with large language models to enhance knowledge retrieval in real-time applications. Similarly, frameworks like LangGraph and SmolAgents are leveraging generative AI to expand their utility in creative and problem-solving domains.

Another area of innovation is the integration of agentic frameworks with edge computing. With the growing interest in deploying AI at the edge, frameworks such as Composio are exploring ways to optimize agentic workflows for resource-constrained environments. The flexibility of these frameworks ensures their compatibility with emerging trends, such as multimodal AI and real-time analytics, paving the way for more adaptive and responsive systems​​​.

References:

Multi-agent Systems

Build language agents as graphs

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Automated Design of Agentic Systems

Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine learning teaches us that hand-designed solutions are eventually replaced by learned solutions. We formulate a new research area, Automated Design of Agentic Systems (ADAS), which aims to automatically create powerful agentic system designs, including inventing novel building blocks and/or combining them in new ways. We further demonstrate that there is an unexplored yet promising approach within ADAS where agents can be defined in code and new agents can be automatically discovered by a meta agent programming ever better ones in code. Given that programming languages are Turing Complete, this approach theoretically enables the learning of any possible agentic system: including novel prompts, tool use, control flows, and combinations thereof. We present a simple yet effective algorithm named Meta Agent Search to demonstrate this idea, where a meta agent iteratively programs interesting new agents based on an ever-growing archive of previous discoveries. Through extensive experiments across multiple domains including coding, science, and math, we show that our algorithm can progressively invent agents with novel designs that greatly outperform state-of-the-art hand-designed agents. Importantly, we consistently observe the surprising result that agents invented by Meta Agent Search maintain superior performance even when transferred across domains and models, demonstrating their robustness and generality. Provided we develop it safely, our work illustrates the potential of an exciting new research direction toward automatically designing ever-more powerful agentic systems to benefit humanity.

arXiv.orgShengran Hu

Agents: An Open-source Framework for Autonomous Language Agents

Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces. We consider language agents as a promising direction towards artificial general intelligence and release Agents, an open-source library with the goal of opening up these advances to a wider non-specialist audience. Agents is carefully engineered to support important features including planning, memory, tool usage, multi-agent communication, and fine-grained symbolic control. Agents is user-friendly as it enables non-specialists to build, customize, test, tune, and deploy state-of-the-art autonomous language agents without much coding. The library is also research-friendly as its modularized design makes it easily extensible for researchers. Agents is available at https://github.com/aiwaves-cn/agents.

arXiv.orgWangchunshu Zhou

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CtiPath, Engage Better. Helping enterprises and small/medium businesses with their IT needs. Including Contact Center, Machine Learning Operations (MLOps), Cloud services, Managed Services, and DevOps.

CtiPath | CtiPath, Engage Better. Helping enterprises and small/medium businesses with their IT needs. Including Contact Center, Machine Learning Operations (MLOps), Cloud services, Managed Services, and DevOps.Alan Knox

AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks. AutoGen agents are customizable, conversable, and can operate in various modes that employ combinations of LLMs, human inputs, and tools. Using AutoGen, developers can also flexibly define agent interaction behaviors. Both natural language and computer code can be used to program flexible conversation patterns for different applications. AutoGen serves as a generic infrastructure to build diverse applications of various complexities and LLM capacities. Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.

arXiv.orgQingyun Wu

Agent S: An Open Agentic Framework that Uses Computers Like a Human

We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI), aimed at transforming human-computer interaction by automating complex, multi-step tasks. Agent S aims to address three key challenges in automating computer tasks: acquiring domain-specific knowledge, planning over long task horizons, and handling dynamic, non-uniform interfaces. To this end, Agent S introduces experience-augmented hierarchical planning, which learns from external knowledge search and internal experience retrieval at multiple levels, facilitating efficient task planning and subtask execution. In addition, it employs an Agent-Computer Interface (ACI) to better elicit the reasoning and control capabilities of GUI agents based on Multimodal Large Language Models (MLLMs). Evaluation on the OSWorld benchmark shows that Agent S outperforms the baseline by 9.37% on success rate (an 83.6% relative improvement) and achieves a new state-of-the-art. Comprehensive analysis highlights the effectiveness of individual components and provides insights for future improvements. Furthermore, Agent S demonstrates broad generalizability to different operating systems on a newly-released WindowsAgentArena benchmark. Code available at https://github.com/simular-ai/Agent-S.

arXiv.orgSaaket Agashe

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