Top AI Agent frameworks and platforms in 2025
AI agent frameworks and platforms empower developers to build software that can reason, remember, and act independently. Which should you choose?
This post examines seven standout frameworks and platforms in 2025, analyzing their features, strengths, and limitations to help you choose.
CrewAI
CrewAI focuses on real-time collaboration, enabling multiple agents or humans and agents to work together effectively. The framework is Pythonic, leveraging Python annotations to define agents, tools, tasks, and processes. These components are organized into “crews” (collections of agents), and CrewAI orchestrates their interactions.
Key features
- Pythonic Design: Uses Python annotations to define agents, tools, tasks, and processes, creating well-structured relationships.
- Inter-Agent Communication: Focuses on real-time message passing between agents.
- Collaboration Features: Enables human input and shared task ownership.
- Workflow Management: Coordinates concurrent activities among agents.
Pros
- Well-structured and intuitive for Python developers.
- UI-driven engine for quickly defining workflows.
- Efficient orchestration of complex multi-agent systems.
- Ideal for scenarios requiring coordinated teamwork.
Cons
- Designed for collaborative, multi-agent environments; less suited for single-agent applications.
- Smaller community and fewer third-party resources compared to larger frameworks.
Fine
Fine is an AI-driven platform that acts as a virtual team member for developers. It autonomously resolves tickets and integrates deeply with codebases to streamline development workflows.
Key features
- Automated Code Reviews: Performs code reviews and implements revisions directly in GitHub.
- Contextual Understanding: Answers questions about the codebase, aiding in debugging and feature design.
- AI-Driven Implementation: Generates and executes code changes based on specified tasks.
Pros
- Automates routine development tasks, boosting productivity.
- Seamlessly integrates with GitHub for streamlined workflows.
- Learns from developer feedback, improving its performance over time.
Cons
- Requires initial setup and training for specific project needs.
- Effectiveness varies depending on codebase complexity.
Microsoft Semantic Kernel
The Microsoft Semantic Kernel delivers enterprise-grade AI workflows with robust security, multi-language support, and scalability for production environments.
Key features
- Enterprise Integration: Offers encryption, authentication, and auditing for secure workflows.
- Language Compatibility: Supports Python, C#, Java, and other major languages.
- Dynamic Orchestration: Handles large-scale workflows and production-level demands.
Pros
- Tailored for environments with strict security and compliance needs.
- Supports integration with existing enterprise software stacks.
- Efficiently manages complex, high-availability systems.
Cons
- Integration can require significant effort in large organizations.
- Less ideal for rapid prototyping or experimentation.
Microsoft AutoGen
AutoGen simplifies multi-agent orchestration and code generation with a no-code graphical interface and performance benchmarking tools.
Key features
- No-Code GUI (AutoGen Studio): Allows developers to design workflows visually.
- Benchmarking Tools: Provides metrics for agent performance and resource usage.
- Microsoft Ecosystem Integration: Connects seamlessly to Azure and other Microsoft platforms.
Pros
- Accelerates development with minimal coding.
- Offers clear performance insights through benchmarks.
- Reduces manual effort for structured workflows.
Cons
- Limited flexibility for unconventional or experimental projects.
- Primarily suited for structured task automation.
Eliza
Eliza is built for decentralized AI applications, emphasizing blockchain integrations and multimodal interactions.
Key features
- Blockchain Integrations: Compatible with Ethereum, Solana, TON, and other chains.
- Plugin System: Supports extensions for image, audio, and other input types.
- Decentralized Focus: Designed for trustless AI-driven services.
Pros
- Ideal for blockchain-based AI logic.
- Expands agent capabilities with a multimodal plugin system.
- Encourages decentralized, transparent designs.
Cons
- Less applicable to conventional, non-Web3 use cases.
- Autonomous features are still evolving
LangGraph
LangGraph provides advanced state management and workflow control, making it an excellent choice for building flexible, branching agent workflows.
Key features
- State Management: Tracks and updates agent states across tasks.
- Workflow Complexity: Handles conditional and branching logic seamlessly.
- Behavior Definitions: Lets developers specify agent responses under various conditions.
Pros
- Enables detailed orchestration of agent behavior.
- Supports complex task structuring with branching workflows.
- Offers fine-grained control over agent states.
Cons
- Requires familiarity with state-machine concepts.
- Initial configuration can be time-consuming.
LangChain
LangChain streamlines the creation of context-aware agents by chaining tasks and integrating memory and external tools into workflows.
Key features
- Task Chaining: Simplifies building multi-step workflows.
- Memory Management: Preserves context across interactions.
- Tool Integration: Connects to APIs, databases, and external services.
Pros
- Simplifies the design of complex workflows.
- Retains relevant context for improved agent responses.
- Integrates easily with external systems.
Cons
- Steep learning curve for newcomers.
- Performance may degrade with overly complex workflows.
Considerations for selecting a framework
Each framework is optimized for specific use cases. For example:
- CrewAI excels in collaborative, multi-agent workflows and will be familiar to Python developers.
- Fine is ideal for automating developer tasks from within GitHub.
- Microsoft Semantic Kernel fits enterprise environments with strict security needs.
- LangGraph offers precise control for advanced workflows.
- Eliza serves decentralized, blockchain-based applications.
- LangChain is perfect for building context-aware agents.