The Best AI Agents in 2025: Tools, Frameworks, and Platforms Compared

2. AutoGen

AutoGen is Microsoft’s multi-agent conversation framework that uses event-driven architecture for complex collaborative tasks. Released in September 2023, it has grown to over 45,000 GitHub stars and outperforms single-agent solutions on GAIA benchmarks, with companies like Novo Nordisk implementing it for data science workflows.

• Multi-agent conversations : Coordinates multiple AI agents for collaborative problem-solving.
• Event-driven architecture : Handles complex interactions between agents.
• Extensive documentation : Comprehensive tutorials and migration guides.
• LLM integration : Works with various large language models.
• Scalable workflows : Designed for complex enterprise tasks.
• Educational tools : Popular in academic and training environments.

Get started by checking out our AutoGent Tutorial which teaches you how to build multi-agent AI applications. 

3. CrewAI

CrewAI orchestrates role-playing AI agents for collaborative tasks with a focus on simplicity and minimal setup requirements. Launched in early 2024, it has gained over 32,000 GitHub stars and nearly 1 million monthly downloads, making it popular for customer service and marketing automation.

• Role-based agents : Assigns specific responsibilities to each agent in the crew.
• Simple implementation : Minimal code required for agent setup.
• LangChain independence : Operates without complex framework. dependencies.
• Collaborative workflows : Agents work together on shared objectives.
• Popular adoption : Widely used in customer service and marketing sectors.
• Rapid deployment : Quick setup for multi-agent systems.

For hands-on implementation guidance, see our CrewAI: A Guide With Examples of Multi AI Agent Systems tutorial.

4. OpenAI Agents SDK

The OpenAI Agents SDK is a lightweight Python framework released in March 2025 that focuses on creating multi-agent workflows with comprehensive tracing and guardrails. With over 11,000 GitHub stars, it offers provider-agnostic compatibility with more than 100 different LLMs.

• Lightweight design : Minimal overhead for multi-agent workflows.
• Provider-agnostic : Compatible with 100+ different language models.
• Comprehensive tracing : Detailed monitoring and debugging capabilities.
• Built-in guardrails: Safety mechanisms and behavior controls.
• Low learning curve : Accessible for Python developers.
• OpenAI integration: Seamless connection with OpenAI services.

Get started with our OpenAI Agents SDK Tutorial for a step-by-step implementation.

5. Google Agent Development Kit (ADK)

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.

• Google ecosystem integration : Native connection with Gemini and Vertex AI.
• Modular architecture : Flexible component-based development approach.
• Hierarchical compositions : Supports complex agent relationships and dependencies.
• Custom tool development : Build specialized tools for specific use cases.
• Minimal code requirements : Efficient development with under 100 lines of code.
• Agentspace platform : Used in Google’s own agent deployment platform.

Learn implementation details in our Agent Development Kit (ADK) Tutorial.

AI Agent Framework comparison 

The table below compares the top frameworks and tools for building AI agents, highlighting their key features, ideal use cases, and real-world adoption to help you choose the right fit for your needs.

Best no-code and open-source AI Agents

For teams without deep coding expertise or those looking to move fast, these no-code and open-source AI agent tools offer powerful functionality with minimal setup.

1. Dify

Dify is a low-code platform for creating AI agents with over 93,000 GitHub stars that makes agent development accessible to non-technical users. Its visual interface supports hundreds of different LLMs and includes built-in RAG, Function Calling, and ReAct strategies for comprehensive agent capabilities.

• Visual interface : Drag-and-drop components for agent development.
• Multi-LLM support : Compatible with hundreds of different language models.
• Built-in strategies : Includes RAG, Function Calling, and ReAct approaches.
• TiDB Vector Search : Scalable vector database integration.
• Enterprise features : Document generation and financial report analysis.
• Rapid prototyping : Quick development for startups and enterprises.

You can get started using Dify today with our article, Dify AI: A Guide With Demo Project.

2. AutoGPT

AutoGPT established the open-source AI agent space by breaking down complex goals into manageable sub-tasks that it can execute independently. 

Built on OpenAI’s GPT models, it can access the internet, interact with various APIs, and maintain memory across sessions. The platform’s adaptability makes it valuable for research, data collection, and automating repetitive processes. 

However, as I explore in our AutoGPT guide, it requires some technical knowledge for setup and maintenance.

Its open-source nature and modular design offer unique advantages for technical teams:

• Task decomposition : Automatically breaks complex goals into executable sub-tasks.
• Internet access : Searches and interacts with web services independently.
• Memory management : Maintains context across extended task sequences.
• API integration : Modular design supports numerous third-party tools.
• Open-source freedom : Complete customization and modification rights.
• Cost structure : Free platform with OpenAI API costs ($0.03-$0.06 per 1,000 tokens).

3. n8n

n8n offers a workflow automation platform that allows teams to create AI agent workflows through drag-and-drop interfaces. This open-source tool supports AI integrations and provides visual workflow building capabilities for automating complex business processes without programming knowledge.

• Drag-and-drop interface: Visual workflow creation without coding.
• AI integration support: Connects with various AI services and models.
• Workflow automation: Automates complex business processes and data flows.
• Open-source platform: Community-driven development with self-hosting options.
• Extensive connectors : Supports hundreds of different services and APIs.
• Visual debugging : Easy troubleshooting and workflow monitoring tools.

See our n8n AI Tutorial for workflow automation examples.

4. Rasa

Rasa provides an open-source framework for building sophisticated conversational AI with extensive customization potential. Trusted by enterprises like American Express, its CALM architecture separates language understanding from business logic, allowing any LLM integration without disrupting workflows.

• Full customization control : Modify any aspect of the conversational system.
• CALM architecture : Clean separation between language understanding and business logic.
• On-premises deployment : Maintains complete data control for sensitive applications.
• Enterprise support : Professional services and support for production deployments.
• Multi-language support : Handles diverse linguistic requirements.
• Community development : Active contributor ecosystem with regular updates.

5. BotPress

BotPress combines a visual flow builder with code hooks to create highly customizable chatbots with extensive analytics capabilities. This open-source platform provides a GUI flow-builder while supporting custom integrations and detailed performance monitoring for agent interactions.

• Visual flow builder : GUI-based conversation design without coding requirements.
• Code hooks : Custom programming integration for advanced functionality.
• Analytics dashboard : Comprehensive tracking of agent performance and user interactions.
• Multi-platform deployment: Supports various messaging channels and platforms.
• Open-source flexibility: Community-driven development with extensible architecture.
• Custom integrations : Connects with external APIs and business systems.

No-code and open-source AI agent comparison 

The following table breaks down top no-code and open-source AI agent tools, comparing their core features, strengths, and ideal use cases, making it easier to select the right solution based on your team’s technical needs and goals.

Best pre-built enterprise AI agents

The tools below represent the leading pre-built enterprise AI agents—designed for scalable, production-grade deployment. These platforms offer robust capabilities out of the box, with deep integration into existing ecosystems

1. Devin AI (Cognition Labs)

Devin AI stands out as the first truly capable AI software engineer, handling complete development projects from planning to deployment. Built by competitive programmers with 10 IOI gold medals, this platform combines large language models with reinforcement learning in a sandboxed environment. 

Companies like Nubank have reported 12x efficiency improvements and 20x cost savings when migrating multi-million-line codebases. The platform excels at legacy code migration, bug fixing, and AI model fine-tuning.

The platform’s capabilities and pricing structure reflect its focus on development:

• Independent coding: Writes, debugs, and deploys complete applications independently.
• Real-time collaboration : Allows developers to work alongside the AI agent.
• Legacy code migration: Specializes in modernizing complex, outdated codebases.
• API integration : Connects with VSCode and other development tools.
• Flexible pricing: Core plan at $20/month, Team at $500/month, Enterprise custom pricing.
• Learning capability: Improves performance through user feedback and coaching.

2. Agentforce (Salesforce)

Agentforce extends Salesforce’s CRM dominance into AI agent territory, providing pre-built solutions for sales, service, marketing, and commerce functions. 

The platform combines generative AI with agentic reasoning, using Salesforce’s Data Cloud for context-aware automation. Major clients like The Adecco Group, OpenTable, and Saks use Agentforce to provide faster, more personalized customer responses. 

The platform’s strength lies in its deep CRM integration and established enterprise relationships.

The platform’s enterprise focus delivers comprehensive business automation capabilities:

• CRM integration : Connects directly with existing Salesforce data and workflows.
• Pre-built agents : Ready-made solutions for common business functions.
• Low-code builder : Agent Builder tool for creating custom automation without programming.
• Multi-channel deployment: Works across web, mobile, Slack, and other platforms.
• Data Cloud access : Uses Salesforce’s unified customer data for personalized interactions.
• Subscription pricing : Integrated with existing Salesforce plans (specific costs undisclosed).

3. Microsoft Copilot Studio

Microsoft Copilot Studio provides a comprehensive platform for building AI assistants that integrate with Microsoft 365 applications. 

The low-code approach allows business users to create custom agents without extensive programming knowledge. Companies like ICG have reported $500,000 in cost savings and 20% margin improvements through Copilot implementation. 

The platform’s tight integration with Microsoft 365 provides immediate value for organizations already using these tools. You can get familiar with Microsoft Copilot in our Introduction to Microsoft Copilot course. 

The platform’s productivity focus delivers immediate value for Microsoft ecosystem users:

• Microsoft 365 integration : Native automation across Word, Excel, Outlook, and Teams.
• Low-code development : Visual tools for creating agents without programming.
• Multi-agent orchestration : Coordinates multiple AI agents for complex workflows.
• Azure AI integration : Access to over 1,800 Azure AI Foundry models.
• Computer use capabilities —: Recent updates allow agents to interact with desktop applications.
• Subscription model : Included with Microsoft 365 Copilot add-on pricing.

4. IBM Watsonx Assistant

IBM Watsonx Assistant brings decades of AI research experience to conversational interface development, focusing on enterprise-grade security and compliance requirements. 

The platform combines natural language understanding with machine learning and an intuitive dialog editor. It suits industries with strict compliance requirements, such as banking and healthcare, where IBM’s reputation for enterprise security provides confidence. However, the platform may involve higher costs and more complex setup compared to newer entrants.

The platform’s enterprise heritage delivers strong conversational AI capabilities:

• Enterprise security : Meets strict compliance requirements for regulated industries.
• Multi-channel support: Handles text and voice interactions across various platforms.
• No-code dialog editor : Visual interface for creating conversation flows.
• Business system integration: Connects with existing enterprise applications and databases.
• Generative AI improvement : Uses watsonx LLMs for improved response quality.
• Cloud-based pricing : Subscription plans through IBM Cloud (specific costs vary by usage).

Other notable mentions

Several specialized platforms address specific business needs with unique approaches. 

• OpenAI’s Codex: Codex is OpenAI’s cloud-based software engineering agent designed to automate coding tasks such as writing features, fixing bugs, running tests, and proposing pull requests. Each task operates within its own secure cloud sandbox, preloaded with the user’s repository. Learn more in this tutorial on Codex.
• Google Jules: Jules is Google’s asynchronous AI coding assistant that integrates directly with developers’ repositories. It clones the codebase into a secure Google Cloud virtual machine, understands the full context of the project, and performs tasks such as writing tests, building new features, fixing bugs, and updating dependencies. Learn more in this tutorial on Google Jules.
• OpenAI’s Operator: An AI agent that is designed to interact with websites like a human, by clicking, typing, and navigating interfaces based on visual input. Running in a secure browser environment, it can book travel, order food, and fill out forms. Sensitive actions like logins or payments require user approval. Learn more in this blog on OpenAI’s Operator.
• Project Astra represents Google’s vision for a universal AI assistant that can understand and interact through multiple modalities. This prototype combines advanced language models with computer vision and real-time processing capabilities, allowing natural interactions through text, voice, images, and video. 
• Yellow.ai specializes in conversational automation with support for 135+ languages, serving global companies like Domino’s and Hyundai. 
• Moveworks focuses on employee support automation, helping organizations like CVS Health reduce live agent chats by 50%.
• AWS Q Dev Amazon upgraded Amazon Q Developer Chat with agentic, multi-step reasoning so the assistant can autonomously call 200 + AWS APIs, diagnose resource issues, and apply fixes inside the console or Slack without human hand-holding. 
• SAP Joule . Joule Studio lets SAP customers build no-code agents (“skills”) that pull live ERP data, suggest next-best actions, and automate approvals—keeping governance intact while speeding decisions. GA for custom skills in June; custom agents land later this year. 

Comparison analysis

Development teams should also consider AI coding assistants as complementary tools to full agent platforms. Our guide to The 12 Best AI Coding Assistants in 2025 covers specialized tools that work alongside agent systems.

The choice between platforms often depends more on existing technology investments and organizational capabilities than pure feature comparisons. 

Development-focused platforms like Devin AI excel at coding tasks, while enterprise solutions like Agentforce and Watsonx Assistant prioritize business system integration. 

Open-source options provide adaptability but require technical expertise, whereas subscription-based platforms offer comprehensive support with ecosystem dependencies.

Implementation Strategies and Best Practices

Moving from agent evaluation to successful deployment requires a structured approach that addresses both technical requirements and organizational readiness.

Getting started

If you’re just starting out, these tips can help you get up to speed in no time. 

1. Start with assessment and planning

Successful AI agent implementation begins with a thorough assessment of your current workflows and technical infrastructure. Identify processes involving repetitive decision-making or data analysis, as these represent the strongest candidates for agent automation. Document existing pain points, measure current performance metrics, and establish baseline measurements for evaluating agent effectiveness later.

2. Choose the right platform for your team

Selection should align agent capabilities with your specific use cases rather than choosing based on popularity alone. Technical teams benefit from frameworks like LangGraph or AutoGen for custom development, while business users often find more value in low-code platforms like Dify or established enterprise solutions. Consider your team’s programming expertise, existing technology stack, and long-term maintenance capabilities.

3. Run focused pilot tests

Begin with a single, well-defined use case that offers measurable business value but won’t disrupt operations if issues arise. Most organizations find that 2–3 month pilot periods provide sufficient time to evaluate effectiveness and address initial technical hurdles.

Technical teams can build expertise through structured learning with our Associate AI Engineer for Developers track, while data science teams benefit from the Associate AI Engineer for Data Scientists pathway.

Best practices

Once you have chosen the right tool and you start developing your AI agents, here are some best practices to bear in mind. 

1. Build agent systems, not isolated tools

Rather than deploying isolated agents, build agent systems where specialized components work together — one agent handles data collection while another performs analysis and a third takes action based on results. This approach mirrors the collaborative workflows that leading companies like OpenAI and Anthropic recommend.

2. Follow the proven four-step workflow

Implement the four-step agent workflow: user task assignment, planning and work allocation, iterative output improvement, and action execution. Build feedback loops where agents can review and refine their work before final delivery, improving output quality over time.

3. Avoid common implementation mistakes

Agents work best for unpredictable situations where rule-based systems would fail, rather than simple workflow automation. Avoid automating everything immediately; instead, focus on high-value processes that benefit from intelligent decision-making.

4. Measure what matters

Track both quantitative metrics like issue resolution rates and qualitative measures such as user satisfaction. Set clear performance baselines and establish regular review cycles to identify optimization opportunities.

5. Plan for growth from day one

Consider increased API usage costs, infrastructure requirements, and support needs as agent usage grows. Develop internal expertise through training programs to reduce dependency on external vendors. Successful agent implementations often lead to process reimagining and broader digital transformation initiatives.

Final Thoughts 

AI agents are evolving fast, from simple chatbots to systems that plan, act, and collaborate with minimal input. They're becoming more capable, more multimodal, and more integrated into real business workflows.

But with this power comes responsibility. Regulations like the EU AI Act mean organizations must prioritize oversight, transparency, and compliance from the start.

Want to learn how to work with modern AI agents? Check out our Build dynamic LangChain agents Course to get hands-on with the tools shaping this new era.