Top 7 AI Agent Frameworks in 2025

Excited about building the next generation of AI systems? You’re in the right place. The AI agent revolution is happening right now, and it’s changing how we work faster than anyone thought possible.

Think of an AI agent framework like the foundation of a house. It keeps everything steady while handling the tricky stuff like memory, planning, reasoning, and tools. That way, you can focus on creating something amazing instead of wrestling with the basics.

The numbers tell the story too. The AI agent market was worth $3.7 billion in 2023 and is expected to double by the end of 2025. In other words, this is the best time to learn which frameworks are driving the change.

And 2025 is already packed with action. We’re seeing autonomous workflows take shape, Agentic Process Automation (APA) becoming the new normal, and enterprises rolling out AI agents everywhere. By the end of the year, about 85% of businesses will be using them. The frameworks we’re about to explore are the ones making it all happen. 

Popular AI agent frameworks are enabling the development of AI powered applications and real world applications across industries, helping organizations automate tasks, streamline workflows, and address practical business challenges.

What is an AI Agent Framework?

Here's where things get interesting—an AI agent framework isn't just another library or API wrapper. Agentic frameworks are the building blocks for developing, deploying and managing AI agents with built-in features and functions that help streamline and speed up the process.

These frameworks provide four essential capabilities that separate true low-code AI agents from simple chatbots:

• Memory: Persistent storage of interactions and learned behaviors
• Planning: Multi-step reasoning and goal decomposition
• Reasoning: Decision-making based on context and objectives
• Tool Use: Integration with external APIs, databases, and systems

Unlike general LLM libraries (which just handle model interactions) or basic orchestration tools (which manage workflows), AI agent frameworks provide the complete infrastructure for autonomous decision-making systems.

Who should use them?

• Developers building conversational AI, workflow automation, or intelligent assistants will find these frameworks accelerate development from months to weeks. 
• Enterprises seeking to automate complex business processes can leverage pre-built components instead of building from scratch. 
• Startups looking to integrate AI capabilities can prototype rapidly without deep ML expertise.

Key Components of an AI Agent Framework

A robust AI agent framework is more than just a toolkit—it’s the backbone that empowers developers to build, deploy, and manage intelligent agents capable of tackling complex tasks in real-world environments. Understanding the essential components of an agent framework helps you choose the right platform and design agents that are both powerful and adaptable.

Here are the key components you’ll find in a modern AI agent framework:

• Agent Architecture: At the core, every ai agent framework defines how agents are structured, including their internal state, memory, and behavior models. This architecture supports the creation of both single and multiple agents, enabling everything from simple assistants to sophisticated multi agent systems.
• Task Planning and Orchestration: Advanced agent frameworks provide built-in mechanisms for breaking down complex tasks into manageable steps. This includes multi-step reasoning, dynamic decision making, and the ability to coordinate multiple ai agents working together on shared goals.
• Inter-Agent Communication: For multi agent workflows, seamless communication between agents is essential. Leading frameworks offer protocols and APIs that allow agents to share information, delegate subtasks, and collaborate efficiently—making it possible to solve problems that are too complex for a single agent.
• Tool and API Integration: Modern ai agent frameworks make it easy to connect agents with external tools, APIs, databases, and proprietary software. This enables agents to access real-time data, perform semantic search, leverage retrieval augmented generation, and interact with other intelligent systems.
• Memory and Knowledge Management: Persistent memory allows ai agents to learn from past interactions, store context, and retrieve relevant information when needed. This is crucial for handling multi turn conversations, long-term projects, and data analysis tasks.
• Human-in-the-Loop Capabilities: Many frameworks now support human feedback and intervention, allowing human agents to guide, correct, or approve agent behavior. This ensures higher accuracy, better error handling, and safer deployment in sensitive applications.
• Security and Access Control: As ai agents are increasingly used in enterprise environments, robust security features—such as authentication, authorization, and audit logging—are built into leading agent frameworks to protect data and ensure compliance.
• Extensibility and Customization: The best ai agent frameworks are designed to be extensible, allowing developers to add custom modules, integrate new machine learning models, or tailor agent behavior to specific business needs.

By combining these components, an ai agent framework provides the foundation for building intelligent agents that can automate complex workflows, collaborate with other agents, and deliver real value across a wide range of AI applications. Whether you’re developing a single ai agent or orchestrating a team of specialized agents, understanding these building blocks is key to successful agent development.

Ranking Criteria

We didn't just pick our favorites—this ranking is based on hard data and real-world performance across six critical factors:

• Popularity: GitHub stars, community engagement, and developer adoption
• Enterprise Adoption: Real deployment numbers and case studies
• Ecosystem: Available integrations, plugins, and third-party support
• Documentation: Quality of guides, tutorials, and API references
• Performance: Benchmarks for speed, reliability, and scalability
• Real-world Use: Production deployments and success stories

This methodology ensures you're seeing the frameworks that actually deliver results, not just generate hype.

Top 7 AI Agent Frameworks in 2025

Let's see the best AI agent frameworks for automation workflows that works in 2025:

1. LangChain/LangGraph

The Pioneer That Keeps Evolving

LangChain remains the most recognized name in AI agent development, and LangGraph has elevated it to new heights. 90% of respondents working in non-tech companies have or are planning to put agents in production, and many are choosing LangChain for its maturity and extensive ecosystem.

Why it's leading: Massive community (80K+ GitHub stars), comprehensive documentation, and proven enterprise adoption. LangGraph introduces visual workflow design that makes complex agent interactions manageable.

Best for: Teams wanting battle-tested reliability with cutting-edge features.

2. Microsoft AutoGen

Multi-Agent Collaboration Made Simple

AutoGen has captured developer attention with its unique approach to multi-agent systems. Instead of building monolithic agents, you create teams of specialized agents that collaborate on complex tasks.

Why it's winning: Intuitive multi-agent conversations, strong Microsoft ecosystem integration, and excellent performance on coding tasks.

Best for: Organizations already in the Microsoft ecosystem or those building collaborative AI systems.

3. CrewAI

The Startup Darling

CrewAI has emerged as one of the top frameworks alongside LangChain and AutoGen, gaining rapid adoption for its developer-friendly approach and robust multi-agent orchestration.

Why it's surging: Clean API design, excellent documentation, and focus on real-world business applications rather than academic use cases.

Best for: Startups and mid-size companies building production-ready agent systems quickly.

4. Google ADK (Agent Development Kit)

The Enterprise Heavyweight

Google's ADK is a modular framework announced in April 2025 that integrates with the Google ecosystem, including Gemini and Vertex AI. With around 10,000 GitHub stars, it supports hierarchical agent compositions and requires less than 100 lines of code for efficient development.

Why it matters: Deep integration with Google's AI infrastructure, enterprise-grade security, and simplified development experience.

Best for: Enterprises already using Google Cloud or teams prioritizing rapid development.

5. OpenAI Agents SDK

The Official Solution

With OpenAI's dominance in the LLM space, their official agents framework provides seamless integration with GPT models and the broader OpenAI ecosystem.

Why it's compelling: Native GPT integration, official support from OpenAI, and optimized performance for OpenAI models.

Best for: Developers building primarily on OpenAI models who want first-party support.

6. Semantic Kernel (Microsoft)

The Enterprise Integration Specialist

Microsoft's second entry focuses on enterprise integration and .NET ecosystem compatibility, making it ideal for large organizations with existing Microsoft infrastructure.

Why enterprises choose it: Enterprise-grade security, seamless Office 365 integration, and strong .NET support.

Best for: Large enterprises with Microsoft-heavy tech stacks.

7. LlamaIndex Agents

The Data-Focused Framework

LlamaIndex has evolved from a document processing tool to a full-featured agent framework, maintaining its strength in data integration and retrieval-augmented generation (RAG).

Why it's valuable: Exceptional RAG capabilities, extensive data connector ecosystem, and strong performance on knowledge-intensive tasks.

Best for: Applications requiring heavy document processing and knowledge retrieval.

Top 7 AI Agent Frameworks

How to Choose the Right AI Agent Framework

Feeling overwhelmed by the options? Here's your decision framework:

For Project Size:

• Small projects: Start with CrewAI or OpenAI Agents SDK for simplicity
• Medium projects: LangChain offers the best balance of features and community
• Enterprise projects: Consider Google ADK or Semantic Kernel for infrastructure integration

For Team Skill Level:

• Beginners: Google ADK requires the least code to get started
• Experienced developers: LangChain/LangGraph offers maximum flexibility
• Microsoft shops: AutoGen and Semantic Kernel provide familiar patterns

For Budget Considerations:

• Open source priority: LangChain, AutoGen, or LlamaIndex
• Pay-per-value: Google ADK and OpenAI SDK scale with usage
• Enterprise licensing: Contact vendors for volume pricing

For Integration Needs:

• Best AI agent framework for enterprises: Google ADK or Semantic Kernel
• Open-source AI agent framework: LangChain remains the gold standard
• Lightweight AI agent framework: CrewAI offers minimal overhead

Future of AI Agent Frameworks (2025–2026)

What's coming next will transform everything you know about AI development. 96% of enterprise IT leaders reported plans to expand their use of AI agents over the next 12 months, signaling massive growth ahead.

The convergence of agentic AI, APA, and enterprise adoption is creating three major trends:

• Multi-Modal Agents: Frameworks will native support for voice, vision, and text in unified workflows. Expect LangChain and Google ADK to lead this space.
• No-Code Agent Builders: Visual development environments will democratize agent creation. CrewAI and others are already moving in this direction.
• Industry-Specific Frameworks: Specialized frameworks for healthcare, finance, and legal sectors will emerge, built on these foundational platforms.

The frameworks that win in 2026 will be those that balance power with simplicity—making advanced AI capabilities accessible to broader developer audiences.

Conclusion

Ready to join the AI agent revolution? 

Here's what you need to remember: AI agents dominate with maturity and ecosystem depth, Microsoft AutoGen excels at multi-agent collaboration, and Ampcome offers the best developer experience for rapid development. 

Of those adopting AI agents, nearly two-thirds (66%) report increased productivity, with over half reporting cost savings and faster decision-making. The question isn't whether you should build with AI agents—it's which framework will get you there fastest.

Get an AI agent with Ampcome today. 

FAQs

1. What is the best AI agent framework in 2025?

LangChain/LangGraph currently leads the market due to its mature ecosystem, extensive community support, and proven enterprise adoption. However, the "best" framework depends on your specific needs—CrewAI excels for rapid development, while Google ADK offers superior cloud integration.

2. Is LangChain still the most popular AI agent framework?

Yes, LangChain maintains its position as the most popular framework with the largest community and ecosystem. However, competitors like AutoGen and CrewAI are gaining ground rapidly, especially in enterprise environments.

3. Which AI agent framework is best for enterprises?

For enterprises, Google ADK and Semantic Kernel offer the strongest infrastructure integration and security features. LangChain is also enterprise-ready with extensive deployment options and support services.

4. Can I build an AI agent without coding?

While most current frameworks require coding, visual development tools are emerging. Google ADK requires minimal code (less than 100 lines for basic agents), and several frameworks are developing no-code interfaces for 2025-2026.

5. What's the difference between an AI agent and an AI agent framework?

An AI agent is the final application that performs tasks autonomously, while an AI agent framework provides the tools, libraries, and infrastructure to build, deploy, and manage those agents. Think of the framework as the construction toolkit and the agent as the finished building.

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