6 Best AI Frameworks for Building AI Agents in 2025

AI agents are creating a buzz across the tech industry – and for a reason. They represent the new frontier in software development, capturing the attention of enterprises and becoming a competitive necessity for forward-thinking businesses.

These intelligent, autonomous AI agents that can perceive, decide, and act aren't just theoretical concepts for tomorrow. As a matter of fact, they are practical tools already in use today, fundamentally changing how companies automate complex workflows, interact with customers, and address challenging problems.

In this article, we'll explore the top AI agent frameworks of 2025 that are transforming how AI agents are built and deployed.

AI agent frameworks and their purpose explained

At their core, AI agent frameworks extend large language models (LLMs) with orchestration, planning mechanisms, and tool-use protocols that allow them to function as true agents. While LLM training data and AI data form the foundation of these models' capabilities, frameworks transform them into systems that can observe, decide, and act with structured interaction capabilities.

The best frameworks solve the challenging aspects of agent development, such as:

• Managing context and maintaining persistent memory across interactions;

• Enabling structured interaction with external APIs, tools, and data sources, including processing user input;

• Making logical decisions based on available information;

• Planning multi-step processes to achieve complex goals;

• Evaluating performance, improving reliability, and more.

Different frameworks take varied approaches to agent architecture. Some excel at multi-agent collaboration, while others focus on sophisticated planning or tool integration. Without these frameworks, development teams would spend countless hours reinventing these fundamental components. 

In practice, framework-powered agents can handle increasingly complex tasks. From customer service agents that manage complex booking processes to development assistants that write and debug code, the capabilities are endless. 

The six frameworks in question represent the most advanced options available in 2025. Each offers distinct strengths and approaches to building truly intelligent agents.

CrewAI

CrewAI has emerged as one of the most prominent agent-based AI frameworks, enabling developers to build AI agents and integrate them with cutting-edge LLMs rapidly. This AI agent framework has gained popularity among major corporations for its simplicity and accessibility, allowing teams to implement solutions with minimal configuration.

Pros

• Exceptionally user-friendly with minimal setup requirements;

• Superior agent orchestration for determining optimal agent sequences and managing multi-agent workflows;

• Integration with over 700 applications (Notion, Zoom, Stripe, etc.);

• Supports both code-based and no-code development approaches;

• Comprehensive monitoring dashboard for tracking agent performance;

• Built-in training and testing tools for improving agent responses.

Cons

• Lacks support for streaming function calling, impacting real-time applications;

• Challenges implementing certain chat functions;

• Less responsive maintainer community for feature suggestions;

• Not as feature-rich in advanced orchestration as some competitors.

This framework is an excellent choice for teams looking to implement multiple agents without extensive technical overhead quickly. Its intuitive design makes it particularly well-suited for organizations taking their first steps into agent-based AI implementations.

LlamaIndex

LlamaIndex is a sophisticated AI agent framework that bridges the gap between LLMs and external data sources. It can leverage information obtained through web scraping in AI model training and enable AI agents to access, process, and reason with diverse information. This makes it possible to build context-aware applications that can leverage both proprietary and external knowledge sources effectively.

Pros

• Powerful data connectors for integrating structured and unstructured data from various sources (PDFs, audio files, CRM systems, real-time search data for AI);

• Flexible indexing strategies optimized for different data types (text, tables, APIs);

• Advanced query engine that enables contextual data retrieval and precise responses;

• Modular architecture allowing for customized agent workflows;

• Strong support for metadata filtering to refine information retrieval;

• Comprehensive logging and debugging tools for monitoring agent-data interactions.

Cons

• May require additional technical expertise to fully utilize all features;

• Setup complexity increases with more sophisticated use cases;

• Optimal configuration depends on specific data characteristics;

• Resource considerations become important with extensive datasets;

• Integration complexity varies based on existing infrastructure.

LlamaIndex excels at knowledge-intensive applications where agents need to reason over diverse data sources for data analysis. It's particularly valuable for domains like sales, marketing, legal, finance, and recruitment, where accessing and synthesizing information from multiple sources is essential for intelligent decision-making.

LangGraph

Another popular AI agent framework is LangGraph. Built within the LangChain ecosystem, it transforms agent development through an intuitive graph-based approach where actions become nodes and transitions become edges. This design enables developers to create sophisticated agent behaviors without getting bogged down in implementation details.

Pros

• Free and open-source under MIT licensing;

• Powerful streaming support showing agents' thought processes in real time;

• Automatic state persistence that safeguards against failures and interruptions;

• Seamless integration with LangChain and LangSmith for a unified development experience;

• Human-in-the-loop capabilities for critical oversight and intervention;

• Robust support for complex workflows with cycles and multi-agent interactions;

• Enterprise-grade deployment options, including self-hosted infrastructure;

• Real-world validation through Replit's implementation of their AI coding agent.

Cons

• Requires familiarity with the LangChain ecosystem;

• Graph-based paradigm presents a learning curve for newcomers;

• More setup overhead for simpler agent implementations;

• Documentation and community resources are still maturing;

• Additional configuration needed for certain specialized applications.

This framework is perfect for orchestrating sophisticated workflows and building complex multi agent systems where visibility into agent reasoning and robust state management are essential. Its growing adoption by enterprise users demonstrates its capability to handle critical AI agent implementations at scale.

Flowise

Flowise is a remarkably versatile open-source platform that's changing how developers approach AI agent creation. With its highly customizable architecture and extensive integration capabilities, Flowise enables teams to build advanced AI workflows that can handle complex, production-ready applications without sacrificing ease of use.

Pros

• Exceptional flexibility with extensive customization options;

• Wide range of integrations for connecting to various services and data sources;

• Support for custom node creation to extend functionality;

• Strong capability to handle complex, production-grade workflows;

• Self-contained operation for more autonomous agent implementations;

• An active and growing community of developers;

• Open-source accessibility with regular updates and improvements.

Cons

• Steeper learning curve compared to simpler frameworks;

• Requires more technical expertise to fully leverage advanced features;

• Somewhat less mainstream recognition than some alternatives;

• More setup and configuration needed for complex implementations;

• Documentation may not cover all advanced use cases.

Flowise shines for teams needing highly customized AI agent workflows that can scale to production environments. Its flexibility makes it particularly valuable for developers who need to build agents that integrate with multiple systems and require fine-grained control over agent behavior and capabilities.

n8n

One more AI framework to consider is n8n, which presents a powerful "fair-code" automation platform. It strikes a good balance between state-of-the-art capabilities and accessibility, allowing teams to build complex integration workflows through an intuitive visual interface rather than extensive coding.

Pros

• Visual workflow builder that makes automation accessible to both technical and non-technical users;

• Extensive library of pre-built integrations for popular tools and services;

• Support for custom node creation to address unique business requirements;

• Robust scalability for handling enterprise-level workflow demands;

• Strong performance when processing large volumes of data;

• A fair-code licensing model that combines open-source benefits with sustainability;

• Cross-departmental collaboration capabilities through its accessible interface.

Cons

• Limited native AI agent capabilities compared to dedicated agent frameworks;

• Requires integration with external AI services for advanced intelligence;

• May require additional configuration for complex AI-driven automations;

• Primarily focused on workflow automation rather than autonomous reasoning;

• Learning curve for creating sophisticated custom nodes.

n8n is particularly valuable for businesses looking to automate complex processes across multiple systems while maintaining an accessible development experience for diverse team members.

Semantic Kernel

Microsoft Semantic Kernel offers enterprises a strategic approach to incorporating AI into their existing software systems. This framework allows teams to enhance applications with intelligent features without the disruption of complete architectural overhauls.

Pros

• Seamless integration with existing enterprise applications and codebases;

• Support for multiple programming languages (primarily C#, with Python and Java);

• Advanced multi-agent orchestration for complex business processes;

• Robust memory management with vector database interactions;

• Flexible AI model selection for optimizing performance and costs;

• Enterprise-grade security and compliance features for regulated environments;

• Compatibility with other frameworks like Autogen for expanded capabilities;

• Built-in memory module for persistent context between interactions;

• Code reusability across different projects and applications, including data analysis solutions.

Cons

• Primary development focus on C# with Python features released secondarily;

• Steeper learning curve for developers outside the Microsoft ecosystem;

• May be overly complex for simpler AI agent implementations;

• Requires understanding of enterprise application architecture;

• Less suitable for rapid prototyping compared to some visual frameworks.

Semantic Kernel is the best pick for organizations looking to enhance established systems with AI capabilities. Its strength lies in enabling secure, gradual adoption of intelligent features while preserving existing software investments.

AI agent frameworks compared

When selecting an AI agent framework, it's essential to evaluate how each option aligns with your specific requirements. The following table compares the six frameworks across key dimensions.