AI Agentic Frameworks
Discover, compare, and choose the perfect AI agent framework for your next project. From LangChain to CrewAI, find comprehensive guides, comparisons, and tutorials.
39+ Frameworks • Feature Comparisons • Implementation Guides • Real-world Case Studies
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LangChain
by LangChain Inc
Framework for building context-aware LLM applications
Key Features:
Open Source
Dify
by Dify.AI
Production-ready platform for LLMOps and agent development
Key Features:
Open Source + Cloud
AutoGen
by Microsoft
Multi-agent conversation framework for LLM applications
Key Features:
Open Source
n8n
by n8n GmbH
Fair-code workflow automation with native AI capabilities
Key Features:
Freemium • 20
LlamaIndex Agents
by LlamaIndex
Data framework for LLM applications with agent support
Key Features:
Open Source + Cloud
Langflow
by Langflow
Low-code platform for building AI agents and RAG workflows
Key Features:
Open Source
CrewAI
by CrewAI Inc
Framework for orchestrating role-playing autonomous AI agents
Key Features:
Open Source + Enterprise
Flowise
by FlowiseAI
Open-source low-code tool for building customized LLM flows
Key Features:
Open Source + Cloud
LangGraph
by LangChain Inc
Low-level orchestration framework for stateful multi-agent systems
Key Features:
Open Source + Commercial
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Framework Comparison
Select Frameworks to Compare (up to 6)
Criteria | LangChain | LangGraph | AutoGen | CrewAI |
|---|---|---|---|---|
Category | RAG & Knowledge | Multi-Agent Systems | Multi-Agent Systems | Multi-Agent Systems |
Primary Language | Python | Python | Python | Python |
License | MIT | MIT | Apache-2.0 | MIT |
Pricing | Open Source | Open Source + Commercial | Open Source | Open Source + Enterprise |
Difficulty | Intermediate | Advanced | Advanced | Intermediate |
Ease of Use | 4/5 | 3/5 | 3/5 | 4/5 |
Scalability | 5/5 | 5/5 | 4/5 | 4/5 |
Documentation | 5/5 | 4/5 | 4/5 | 4/5 |
Community | 5/5 | 4/5 | 5/5 | 5/5 |
Performance | 4/5 | 5/5 | 4/5 | 4/5 |
GitHub Stars | 117,000 | 19,900 | 50,600 | 39,200 |
Frequently Asked Questions
Get answers to common questions about AI agent frameworks, implementation, and choosing the right solution for your needs.
What is an AI agent framework?
An AI agent framework is a software structure that provides tools, libraries, and abstractions for building intelligent agents. These frameworks handle complex tasks like model integration, memory management, tool usage, and multi-agent coordination, allowing developers to focus on business logic rather than infrastructure.
Which framework is best for beginners?
For beginners, we recommend starting with n8n for visual workflow creation, CrewAI for its intuitive role-based approach, or LangChain if you prefer Python and have some programming experience. These frameworks offer excellent documentation and community support.
What's the difference between LangChain and LangGraph?
LangChain is a general-purpose framework for building LLM applications with chains and agents. LangGraph extends LangChain with graph-based workflows, offering more explicit control flow, state management, and debugging capabilities for complex, stateful applications.
Are these frameworks suitable for enterprise use?
Yes, several frameworks are enterprise-ready including Semantic Kernel (Microsoft), LangChain with enterprise features, AutoGen, and n8n. Consider factors like security, scalability, compliance, vendor support, and integration capabilities when choosing for enterprise use.
How do I choose between multi-agent and single-agent frameworks?
Choose multi-agent frameworks (AutoGen, CrewAI, LangGraph) when you need specialized agents working together, complex workflows with different roles, or parallel processing. Single-agent frameworks work well for simpler, linear tasks or when you want to start simple and scale up.
What programming languages are supported?
Python is the most widely supported language across frameworks. TypeScript/JavaScript is supported by LangChain, n8n, and LangGraph. C# developers should consider Semantic Kernel. Java support is available in Semantic Kernel. Check each framework's documentation for specific language support.
Are these frameworks free to use?
Most frameworks offer open-source versions that are free to use. However, some have commercial features, enterprise support, or cloud hosting that require payment. Check the pricing details for each framework, especially for commercial use or advanced features.
How do I handle framework updates and compatibility?
AI agent frameworks are rapidly evolving. Follow framework roadmaps, join their communities, use version pinning in production, test updates in staging environments, and consider the framework's stability and update frequency when choosing for production use.