AI agents sound exciting – autonomous systems that can reason through complex tasks, coordinate with other agents, and achieve magical results with minimal supervision.
But if you’ve actually tried to build an AI agent, you know that the reality is much messier: many AI agent frameworks promise to simplify agent development but end up adding layers of complexity through abstractions and unpredictable behavior. When your agent suddenly starts hallucinating or loses track of crucial context after you’ve spent days configuring it, that slick YouTube demo feels like a distant dream.
In this article, we'll look at 9 AI agent frameworks with three levels of complexity that really work.
We’ll show how n8n strikes the balance many developers are looking for – providing ready-made components for AI agents' rapid development while preserving the flexibility to customize or simplify your agent as needed.
Table of contents
- 9 best AI agent frameworks comparison
- Visual no-code frameworks
- Intermediate low-code complexity frameworks
- Programming-first frameworks
- Why use n8n to build AI agents?
9 best AI agent frameworks comparison
We've prepared a list of 9 popular AI agent frameworks, focusing on solutions that offer flexibility in deployment.
The frameworks we selected represent a diverse range of approaches to building AI agents, from visual workflow designers to code-centric solutions. This variety allows developers to choose a framework that best fits their skill level, project requirements, and desired level of customization. These frameworks are arranged by complexity level, from beginner-friendly to more advanced options.
| Framework | Primary Strength | Best For | Language |
|---|---|---|---|
| Flowise | Visual workflow building with drag-and-drop interface |
Quick prototyping without coding skills |
JavaScript |
| Botpress | Visual workflow design with extensive AI integrations |
Customer service automation and chatbots |
JavaScript |
| Langflow | Visual IDE on top of LangChain with pre-built templates |
Visual LangChain prototyping and workflow design |
Python |
| n8n | Visual AI agent orchestration with extensible architecture for custom LLM integrations and agent workflows |
Building production-ready AI agents with the flexibility to scale from simple automations to complex multi-agent systems |
JavaScript/ TypeScript |
| CrewAI | Role-based collaboration with specialized agent teams |
Complex workflows requiring role-specific expertise |
Python |
| Rivet | Visual scripting for AI agents with debugging capabilities |
Rapid prototyping with visual logic design |
TypeScript |
| AutoGen | Advanced multi-agent orchestration with agent-to-agent communication |
Complex problem-solving requiring autonomous collaboration |
Python |
| LangGraph | Graph-based workflows for structured reasoning |
Multi-step reasoning tasks with explicit decision paths |
Python |
| SmolAgents | Minimal, efficient design with direct code execution |
Quick automation tasks with lightweight implementation |
Python |
Visual no-code frameworks
These frameworks prioritize visual interfaces and simplified workflows, making AI agent development accessible to users with minimal technical experience.
Flowise
Primary strength: Visual workflow building with a drag-and-drop interface
Flowise is an open-source platform for building customized LLM applications. It offers a drag-and-drop user interface and integrates with popular frameworks such as LangChain and LlamaIndex.
While Flowise simplifies many aspects of AI development, there’s still a learning curve. For example, Flowise sequential agents are built on top of the LangGraph framework.
Key features:
- Integration with popular AI frameworks such as LangChain, LangGraph and LlamaIndex
- Support for sequential agents, multi-agent systems and Retrieval-Augmented Generation (RAG)
- Extensive library of pre-built nodes and integrations
- Tools to analyze and troubleshoot chatflows and agentflows (two types of applications you can build with Flowise)
- Generation of the chat widget that can be embedded into websites or applications
Pricing:
- Cloud version starts at $35/month
- Open-source version for self-hosted deployment
Botpress
Primary strength: Visual workflow design with extensive AI integrations
Botpress is an AI agent development platform that is available both in the cloud and as an open-source version. Its browser-based Studio interface features a visual flow builder, making it accessible to both developers and non-developers.
With Botpress, creating your first AI agent requires no coding. You can simply select templates that fit your use case, define your agent’s instructions and identity, and integrate knowledge bases by uploading documents or providing textual inputs.
Key features:
- Visual workflow design with a drag-and-drop interface
- Built-in chat emulator for testing your chatbot
- Knowledge base capabilities for uploading documents and external data sources
- Template-based approach for rapid agent creation
- Multi-channel deployment options for websites, messaging apps, and more
- Support for both technical and non-technical users
Pricing:
- Cloud hosting is free for 1 bot, paid tiers start at $79/month
- Paid add-ons available
- An open-source version of V12 Botpress is available
Langflow
Primary strength: Visual IDE on top of LangChain with pre-built templates
Langflow is a visual framework for creating multi-agent and RAG applications that is built on top of the LangChain ecosystem. Langflow provides LangChain tools and components as pre-built elements, allowing developers to quickly add functionality to their AI applications without having to code from scratch.
Key features:
- Drag-and-drop interface for building AI workflows
- Integration with various LLMs, APIs, and data sources
- Export flows as JSON files: can import in another Langflow instance or reuse in the Python Langflow runtime
- Pre-built templates for quick prototyping
- Open-source with cloud deployment options
- Fully customizable and LLM/vector store agnostic
Pricing:
- Langflow offers a free-to-use model, available as both a self-hosted project and as a cloud service
- While the cloud version is free, its default vector store is backed by AstraDB, which has usage-based pricing
Intermediate low-code complexity frameworks
These frameworks balance visual development with more powerful customization capabilities, which is ideal for developers who want to easily build sophisticated agents, but still be able to code when needed.
n8n
Primary strength: Visual AI agent orchestration with extensible architecture for custom LLM integrations and agent workflows
n8n is a powerful source-available automation platform that combines AI capabilities with traditional workflow automation. This approach allows users with varying levels of expertise to build custom AI applications and integrate them into business workflows.
As one of the leading frameworks for AI agent orchestration, n8n offers an intuitive drag-and-drop interface while allowing for advanced customization when needed. It offers a balance between ease of use and functionality, making it suitable for both simple automations and complex multi-agent systems.
Key features:
- Flexible deployment: choose between cloud-hosted or self-hosted solutions to meet security and compliance requirements
- Advanced AI components: implement chatbots, personalized assistants, multi-agent systems and more with pre-built AI nodes
- Custom code support: add custom JavaScript when needed (available for LangChain components and as a separate Code node)
- LangChain integration and vector store compatibility: integrate with various vector databases (Pinecone, Qdrant, Zep) for efficient storage and retrieval of embeddings
- Memory management and RAG support: implement context-aware AI applications with built-in memory options for ongoing conversations and relevant information from custom data sources
- Use of tools: extend agent functionality with built-in nodes: HTTP Request tool, workflow tool to run other workflows, or Nodes as Tools
- n8n interacts with MCP as an MCP client via the MCP Client Tool node to use external tools by connecting to MCP servers with authentication. Additionally, n8n acts as an MCP server through the MCP Server Trigger node to expose its tools and workflows using SSE and configurable URLs with authentication
- Scalable architecture: handles enterprise-level workloads with a robust infrastructure
Pricing:
- Cloud version starts at 24€/month
- Custom pricing for enterprise customers (with discounts for eligible startups)
- Community edition is free for self-hosted deployment
CrewAI
Primary strength: Role-based collaboration with specialized agent teams
CrewAI is a lean Python framework developed entirely from scratch – completely independent of LangChain or other agent frameworks. With the Studio interface users can create agents without actually programming them.
CrewAI stands out for its ability to create a “crew” of AI agents, each with specific roles, goals and backstories. For instance, you can have a researcher agent that gathers information, a writer agent that creates content, and an editor agent that refines the final output – all working in concert within the same framework.
Key features:
- Role-based agents with defined roles, expertise, and goals
- Flexible tools to equip agents with custom tools and APIs
- Task management for defining sequential or parallel workflows
- Support for both Crews (autonomous collaboration) and Flows (structured automation)
- Event-driven orchestration with fine-grained control
Pricing:
- Custom pricing for enterprise customers
- An open-source version is available
Rivet
Primary strength: Visual scripting for AI agents with debugging capabilities
Rivet is a powerful Integrated Development Environment (IDE) and library for creating AI agents using a visual, graph-based interface. It consists of two main components: Rivet Application (editor/IDE) and Rivet Core/Node (TypeScript libraries for execution).
Rivet’s node-based editor enables you to create, configure, and debug complex AI prompt chains and AI agent chains visually. This approach makes it easier to understand the data flow and state of your AI agent at any point in time, with real-time visibility into inputs, outputs, and AI responses.
Key features:
- Visual editor for creating and debugging complex AI prompt chains
- Live debugging of AI chains as they run, with support for remote debugging
- TypeScript libraries for executing projects in applications
- Integrated testing to ensure graphs work for all inputs (via separate Trivet library)
Pricing:
- Rivet editor is a multi-platform free desktop app for MacOS, Windows and Linux
Programming-first frameworks
These frameworks emphasize code-first approaches, offering maximum flexibility and control for developers with programming experience.
AutoGen
Primary strength: Advanced multi-agent orchestration with agent-to-agent communication
AutoGen is one of Microsoft's frameworks for building AI agents. Its main focus is on the creation of scalable multi-agent systems.
AutoGen’s event-driven programming approach makes it particularly suited for building agentic workflows for business processes, research on multi-agent collaboration, and distributed agents for multi-language applications (Python & Dotnet).
Key features:
- AgentChat for building conversational agents
- Event-driven programming framework for scalable multi-agent AI systems
- Extensions for using LangChain tools, Assistant API, and Docker container execution
- Command-line interface (Magentic-One CLI) for fast agent interactions
- AutoGen Studio for prototyping and managing agents without writing code
Pricing:
- Open-source project
LangGraph
Primary strength: Graph-based workflows for structured reasoning
LangGraph is a low-level orchestration framework for building controllable agents. While LangChain provides integrations and composable components for LLM application development, LangGraph enables agent orchestration with customizable architectures, long-term memory, and human-in-the-loop capabilities.
Key features:
- Reliability and controllability with moderation checks and human-in-the-loop approvals
- Low-level and extensible design with fully descriptive primitives
- First-class streaming support with token-by-token visibility into agent reasoning
- Customizable agent architectures for specific use cases
- Persistence capabilities for context in long-running workflows
- Integration with other LangChain products (LangSmith and LangGraph Platform)
Pricing:
- Paid tiers on the LangGraph platform
- Open-source version available
SmolAgents
Primary strength: Minimal, efficient design with direct code execution
SmolAgents is a newer framework from HuggingFace for agentic systems. It positions itself as a minimalistic framework for building powerful agents. For example, the core agent logic fits in approximately 1,000 lines of code.
One of SmolAgents’ standout features is its first-class support for Code Agents – agents that write their actions in code, as opposed to agents being used to write code. This approach enables direct code execution for efficient automation tasks.
Key features:
- Minimal, efficient design with minimal abstractions
- Support for any LLM, including models from Hugging Face, OpenAI, Anthropic, and more
- HuggingFace hub integrations for sharing and loading Gradio Spaces as tools
- Simplified framework for quick implementation of automation tasks
Pricing:
- Open-source project
How to pick an AI agent framework?
When selecting an AI agent framework, consider the following key factors:
- Project complexity: assess whether you need a simple chatbot or a complex multi-agent system. AutoGen, for example, is great for orchestrating multiple AI agents for complex tasks, while CrewAI is more suitable for smaller projects.
- Developer expertise: evaluate your team’s familiarity with AI concepts and programming skills. Frameworks like Flowise offer a more intuitive approach, while LangChain or AutoGen provide deeper customization for experienced developers.
- Language preferences: consider the programming languages your team is comfortable with. Most frameworks support either Python or JavaScript, and there are also some less popular solutions for other languages.
- Build type: decide between no-code, low-code or code-centric development frameworks depending on your technical expertise and project requirements.
- Integration needs: determine whether you want to build from scratch or add AI to existing systems. n8n, for example, is designed to integrate AI into existing business systems without major rewrites.
- Scalability: ensure that the framework can handle your current needs and future growth. Frameworks like LangGraph offer solutions for complex, long-running applications.
By carefully evaluating these factors, you can select an AI agent framework that meets your project goals, your team's capabilities and your long-term business needs.
Why use n8n to build AI agents?
n8n is a powerful choice for building AI agents that connect with existing business systems and scale to production. Its blend of visual development, robust integrations, and enterprise-level scalability makes it ideal for real-world use. While other AI agent frameworks may offer niche features, n8n’s flexibility stands out for production-ready AI solutions.
Here’s why:
Agentic workflows, not just agents
n8n excels at creating complete workflows and agents. It uniquely enables agents to trigger traditional workflows as tools, resulting in more controlled agentic behavior. Agents initiate workflows, reducing the chances of unpredictable or random behavior (AI agentic workflows guide).
Out-of-the-box agent components
n8n provides a rich set of pre-built components specifically for creating agents:
- LangChain nodes: direct integration with LangChain’s components.
- Memory: built-in support for various memory types (see below).
- Flexible tools: HTTP Request tool node, workflow execution via the Workflow tool node and Nodes as tools.
- Interchangeable LLMs: easily swap between cloud providers and local models.
- Structured output parsing: ensure reliable, consistent agent outputs.
A variety of tools
One of the most important capabilities of the AI agents is interaction with other systems. This is done via a range of tools in n8n:
- Web parsing: extract data from websites using the HTTP Request tool node.
- Workflow execution: trigger complete n8n workflows as tools using the Workflow Tool.
- Nodes as tools: use specific n8n nodes as tools for fine-grained control.
- RAG features: integrate with vector databases like Qdrant, Pinecone, Supabase, and PGVector for knowledge-augmented agents. n8n also provides an in-memory vector store for quick prototyping.
Model Context Protocol (MCP) support: a native support for both MCP clients and servers. This opens up completely new integration opportunities with programs and services tools that may not natively support REST API integrations.
Diverse memory approaches:
Maintain the conversation context with different storage options:
- Postgres Chat Memory: for persistent, auditable memory.
- Redis Chat Memory: high-performance caching of conversation history.
- Zep Memory: use Zep for long-term memory capabilities.
Window Buffer Memory: a simple, in-memory buffer for short-term context.
Robust output parsing
When creating AI agents or integrating LLMs into products, you need to ensure agents produce consistent, reliable outputs. n8n supports two way of doing so:
- Structured Output Parser: enforce a predefined JSON schema, automatically retrying if the output is invalid.
- Auto-Fixing Parser: use the LLM to automatically correct parsing errors.
Model flexibility
Easily switch between different LLMs:
- Cloud providers: connect to OpenAI, Anthropic, Azure, DeepSeek and Mistral.
- OpenRouter: access a wide range of models through the OpenRouter integration.
Local models: run models locally using the Ollama integration.
Wrap up
In this guide, we’ve reviewed 9 powerful AI agent frameworks in three main categories:
- No-code visual tools: Flowise, Botpress and Langflow for visual workflow design.
- Intermediate cow-code frameworks: n8n, CrewAI and Rivet for balanced customization.
- Programming-first solutions: AutoGen, LangGraph and SmolAgents for code-centric development.
Each framework offers unique advantages depending on your project requirements, team expertise and scalability needs.
For teams looking to deploy AI agents in business-critical systems, n8n stands out with its unique hybrid approach. Unlike pure AI agent frameworks, n8n enables you to:
- Build agents that trigger traditional workflows as tools.
- Combine hundreds of pre-built integrations and create your own via the HTTP Request tool node.
- Scale from simple chatbots to complex multi-agent systems.
What’s next?
Deepen your AI agent expertise with these resources:
- Explore real-world AI agent implementations in various industries.
- Learn about the differences between LlamaIndex and LangChain for RAG applications and how RAG extends the capabilities of AI agents.
- Develop an AI adoption strategy for your enterprise environment.
