The rise of agentic AI is one of the most exciting shifts happening in technology today. According to S&P Global, GenAI startups raised over $20 billion in just the first 9 months of 2024, already surpassing the total funding of 2023. That’s a clear sign that investors believe in the long-term potential of agentic systems. At the same time, Gartner predicts that by 2028, 15% of people will rely on agentic AI to make daily work decisions. These numbers highlight how quickly agentic AI is moving from experimentation to real-world adoption. For businesses, it means a new layer of infrastructure that can automate complex workflows and support decision-making. For engineers, it means learning how to use these frameworks is no longer optional but it’s becoming a core skill. In this blog, we’ll explore the top 7 agentic AI frameworks that are shaping this space. You’ll learn what each framework does, why it matters, and how you can start building with them to stay ahead in this rapidly evolving field.
Top 7 Agentic AI Frameworks
Choosing the right framework depends on needs like communication, model and tool support, customizable agents, human oversight, multi-agent collaboration, workflow control, and memory management. These criteria guide how today’s top agentic AI frameworks are evaluated. Below are the 7 Agentic AI frameworks that are driving adoption and innovation.
1. Autogen (Microsoft)
Autogen is Microsoft’s framework designed to simplify the orchestration and optimization of large language model (LLM) workflows. It enables customizable, conversable agents that can collaborate with humans, tools, and other agents via automated chat.
Key Features
- Multi-Agent Conversations: Human-like collaboration between agents to manage complex workflows.
- Enhanced Inference Optimization: Ensures applications are cost-efficient and performant.
- Teachability & Personalization: Agents can learn from users and adapt to their unique preferences, making interactions feel more intuitive and tailored over time.
- Modular & Extensible: The framework allows developers to plug in new components or customize features for domain-specific applications.
Use Cases
Autogen stands out for enabling real-world, high-value applications through multi-agent collaboration.
- Coordinate agents to handle research, data analysis, or experiment design.
- Automate coding tasks with generation, execution, and refinement. Streamline question answering and task execution with minimal human input.
- Use role-based agents for interactive, personalized learning.
- Optimize campaigns, track engagement, and generate targeted ad content.
Autogen is extensible, customizable, and optimized for performance, making it suitable for developers of varying experience levels.
2. Semantic Kernel (Microsoft)
Semantic Kernel (SK) offers a developer-friendly SDK for bringing LLMs into existing enterprise systems. By handling prompt management and orchestration, it removes complexity and makes AI adoption smoother across business applications.
Key Features
- Prompt Management: Store and reuse prompts for consistency.
- Memory Integration: Maintain context across interactions for coherent workflows.
- Planner & Orchestration: Dynamically generate sequences of actions.
- Agent Framework: Supports chat and group-chat collaboration.
- Extensibility: Built with plugins for customization.
Use Cases
Some of the most impactful uses include:
- Create conversational chatbots or personal assistants with memory and plugin support.
- Intelligent Document Processing(IDP): Summarize, classify, and extract insights from reports or resumes.
- Power CRMs and ERPs with instant AI-driven answers.
- Knowledge Retrieval Systems: Enable natural language search across large document sets.
- Coordinate agents for research, writing, and publishing tasks.
Semantic Kernel balances power with simplicity and has a comprehensive documentation, making it approachable for newcomers while still offering depth for advanced users.
3. Swarm (OpenAI)
Swarm is OpenAI’s experimental framework for coordinating multiple AI agents. It introduces abstractions like Agents and Handoffs to create modular and reusable AI systems.
Key Features
- Agent Abstractions: Self-contained units encapsulating instructions and tools.
- Handoffs: Seamless transfer of tasks between agents for dynamic workflows.
- Tool Integration: Supports external system interactions.
- Stateless Design: Built on the Chat Completions API, simplifying architecture.
Use Cases
Swarm is especially useful when tasks require smooth coordination between multiple agents. Some powerful applications include:
- Customer Service Workflows: Direct users to the right specialist through seamless handoffs.
- Personal Shopping Assistants: Combine recommendation and ordering agents for tailored experiences.
- Educational Tools: Pair a tutor agent with a quiz agent for interactive learning.
- Task Delegation: Split complex workflows across specialized agents.
- Consultations: Collect details with one agent and deliver expert advice with another.
Swarm emphasizes simplicity. Its abstractions are intuitive, making the learning curve smooth for developers familiar with AI concepts. The modular design also makes it easy to extend and integrate.
4. LangGraph (LangChain)
LangGraph is an open-source library developed by LangChain to build stateful, multi-agent applications. Unlike other frameworks, it uses graph-based workflows, giving developers precise control over agent interactions.
Key Features
- Graph-Based Workflows: Agents operate through directed graphs, allowing for intricate and cyclic workflows.
- State Management: Inherent statefulness enables context retention, error handling, and human-in-the-loop operations.
- Multi-Agent Collaboration: Facilitates both hierarchical and sequential configurations.
- Distributed Applications: Powers large-scale, enterprise-level deployments.
Use Cases
LangGraph is built for complex, stateful applications where agents need structure and memory. Its most powerful uses include:
- Context-aware chatbots that adapt conversations dynamically
- Document summarization and Q&A across large datasets
- Multi-step code generation and debugging pipelines
- Automated agent-based workflows for tasks and tool use
- Data analysis and visualization with interactive insights
Since LangGraph integrates naturally with LangChain while providing amazing flexibility and control, it’s well-suited for enterprise-grade applications requiring robust multi-agent systems.
5. Crew AI
CrewAI represents an open-source Python framework that tackles multi-agent AI system development head-on. Rather than relying on single models, it orchestrates teams of specialized agents with defined roles and autonomous decision-making capabilities. This approach delivers superior results for complex problem-solving scenarios.
Key Features
- Agent Orchestration: Coordinate workflows and behaviors for efficient collaboration.
- Role-Based Architecture: Assign distinct roles and permissions to structure agent systems.
- Flexible Communication: Supports diverse communication protocols for seamless agent interaction.
- Tool Integration: Expands functionality through API connections, search capabilities, and analytical tools.
- Scalability: Adapts from prototype development to enterprise-level implementations.
Use Cases
- Lead scoring and customer segmentation in sales
- Marketing content creation and optimization
- Customer service chatbots and call analytics
- Financial analytics, fraud detection, and stock analysis
- Coding agents for automated code generation and debugging
CrewAI is developer-friendly, scalable, and community-driven. Its open-source nature and active ecosystem make experimentation easy, while endorsements from industry leaders highlight its growing role as a go-to platform for building collaborative AI systems.
6. Haystack Agents
Haystack, developed by deepset, delivers a specialized open-source framework targeting advanced search and NLP implementations. Unlike broader platforms, Haystack Agents concentrate on document retrieval, multi-step reasoning, and retrieval-augmented workflows. This focused approach optimizes performance for enterprise AI applications requiring sophisticated search functionality.
Key Features
- Multi-Step Reasoning: Processes complex queries through logical decomposition sequences.
- External Tool Integration: Connects seamlessly with APIs, computational tools, and database systems.
- Retrieval-Augmented Generation: Enhances output quality via intelligent search pipeline integration.
- Modular Design: Integrates efficiently with existing ML infrastructure and workflows.
- Multimodal Support: Handles diverse data formats including text, images, and audio.
Use Cases
- Semantic search across large document repositories
- Automated information extraction for financial and legal sectors
- Conversational AI for support systems and virtual assistants
- Content generation and summarization workflows
- Advanced RAG pipelines for knowledge management systems
The framework's open-source nature and developer-friendly architecture appeal to organizations requiring customizable solutions for production-ready, scalable AI search and reasoning capabilities.
7. Llama Agents
Llama-agents provides a production-oriented open-source framework for building and deploying multi-agent AI systems. Unlike experimental platforms, it transforms agents into independently running microservices orchestrated through a centralized control plane, making it ideal for enterprise operationalization of multi-agent workflows.
Key Features
- Distributed Service Architecture: Agents function as individual microservices coordinated by an LLM-powered control plane.
- Standardized Communication: Message queue and API-based interfaces ensure seamless agent interoperability.
- Flexible Orchestration: Supports both explicit workflow definition and intelligent agentic orchestration.
- Deployment Efficiency: Independent scaling and monitoring of agents and control systems.
- Scalability & Observability: Built-in performance tracking and system health monitoring tools.
Use Cases
- Intelligent search and Q&A systems with retrieval-augmented generation
- Collaborative chatbots spanning multiple workflows
- Structured data extraction from unorganized datasets
- Autonomous task coordination and distribution systems
- Multi-modal AI experimentation across data types
Its microservice architecture and comprehensive monitoring make it particularly suitable for teams prioritizing operational reliability and scalability in production environments.
Agentic AI frameworks represent a critical advancement in artificial intelligence architecture and offer unique technical strengths; however, challenges in memory management and system scalability continue to be significant barriers requiring resolution. As the ecosystem evolves, these frameworks will become foundational infrastructure for next-generation AI-powered enterprise applications.
The best way to understand the workings of these frameworks is to build with them and move beyond theory. If you’d like to explore practical applications, I recommend looking at this AI Agent Projects resource, which is a collection of project ideas that demonstrate how these frameworks can be applied by both beginners and experienced practitioners.
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