Top 11 AI Agent Frameworks You Should Know

LangChain

LangChain is a framework built to simplify the creation of applications using LLMs, with strong support for memory, agents, and tool usage.

Key features:

• Reusable chains and agents

• External tool integrations (APIs, databases, etc.)

• Memory modules

• Native OpenAI support

• LangServe for API deployment

• LangGraph for stateful workflows

Best suited for: Research agents, data enrichment bots, and AI applications that need dynamic memory and reasoning.

LangChain can be overwhelming for beginners due to its modular architecture and rapid ecosystem evolution. Developers must stay on top of frequent updates, which may cause compatibility issues across tools like LangServe or LangGraph.

LangGraph

LangGraph is an extension of LangChain designed to implement stateful, multi-agent workflows using graphs.

Key features:

• Graph-based visual logic structure

• Support for loops and conditional execution

• Persistent memory across tasks

• Built-in concurrency and async workflows

• Native support for LangChain agents

Best suited for: Complex workflows, agent pipelines, and LLM applications requiring logic branching.

LangGraph brings more intricacy to the system due to its graph-based, stateful architecture. It demands a solid grasp of LangChain internals and async execution principles.

CrewAI

CrewAI allows users to build teams of AI agents that can work together collaboratively. It focuses on multi-agent communication and task distribution.

Key features:

• Agent role assignment (e.g., planner, researcher, writer)

• Parallel execution

• Task orchestration

• Human-agent oversight

Best suited for: Multi-agent systems, collaborative workflows, and project management tools.

As a newer framework, CrewAI comes with limited documentation and community support, which can be a challenge for those unfamiliar with multi-AI collaboration.

Semantic Kernel

Semantic Kernel enables developers to build AI agents that integrate with Microsoft services like Azure, Teams, and Office.

Key features:

• Plugin architecture

• Planner and memory engine

• Semantic functions chaining

• Deep integration with Microsoft 365 and Azure

• Python, Java and C# SDKs

Best suited for: Enterprise automation, document processing, and knowledge management within the Microsoft stack.

AutoGen

AutoGen helps developers create LLM-based agents that can reason, plan, and act in a self-directed manner.

Key features:

• LLM-to-LLM and LLM-to-human communication

• Goal-driven architecture

• Group chat templates

• Tool and plugin support

• Shared memory and task management

Best suited for: Advanced use cases like R&D automation, technical workflows, and simulations involving multiple autonomous roles.

With its emphasis on automation and ease of use, AutoGen lowers the barrier for building AI agents, even for developers new to the field. It performs best in clearly defined scenarios where consistency and compatibility with Microsoft tools are more important than flexibility or deep customization.

AutoGPT

One of the earliest and most popular open-source agent frameworks, it introduced the idea of self-prompting agents that can break down objectives and act on them with minimal human input. AutoGPT enables autonomous task completion through iterative reasoning.

Key features:

• Task decomposition

• Internet access via plugins

• Local file memory

• Feedback loops for iterative improvement

Best suited for: Research, solo agents, data retrieval bots, and experimental projects.

Rasa

Rasa is an enterprise-grade framework for building context-aware chatbots using machine learning.

Key features:

• Natural Language Understanding (NLU)

• Dialogue management

• Open-source and on-premise deployment

• Multi-language and multi-channel capabilities

Best suited for: Regulated industries, multilingual bots, and high-security applications.

Rasa’s flexibility comes at the cost of setup complexity. It requires a deep understanding of machine learning pipelines, training data, and intent classification models. It's also heavier compared to lightweight, LLM-native solutions.

Atomic Agents

A newer entrant in the space, Atomic Agents provides a modular setup to build atomic (independent) agents that can also collaborate.

Key features:

• Stateless and stateful agents

• Tool and memory integration

• Event-driven communication

• Real-time logging

Best suited for: Modular agent networks, testing new ideas, and handling lightweight operations.

While powerful, the framework may be challenging for those new to multi-agent system design due to its conceptual complexity. It's best suited for teams aiming to build cooperative agents efficiently without investing heavily in prompt engineering or tool setup.

Langflow

Langflow brings visual programming to LangChain, allowing users to build AI workflows through a drag-and-drop interface.

Key features:

• Visual canvas editor

• Real-time testing

• LangChain compatibility

• Template library and collaborative tools

Best suited for: Low-code teams, product managers, and prototyping AI workflows.

While intuitive for visual prototyping, Langflow lacks the robustness and stability needed for production-scale applications. Teams unfamiliar with LangChain may also struggle to understand how visual components map to underlying logic.

Botpress

Botpress is an open-source conversational AI platform designed for building chatbots and virtual assistants. Botpress Cloud makes it easier to integrate LLMs and deploy across platforms.

Key Features:

• Visual flow editor

• Integrated NLU engine

• Modular plugin system

• Multi-channel deployment

Best suited for: Customer support automation, FAQ bots, and internal communication agents.

Swarm

Swarm is an open-source, experimental framework by OpenAI for orchestrating multi-component LLM workflows using lightweight, stateless agents and handoff logic.

Key features:

• Stateless agent orchestration

• Simple handoff mechanism between roles

• Lightweight, client-side architecture

• Built-in retrieval and memory support

• Runs on top of OpenAI’s Chat Completions API

Best suited for: Educational projects, prototyping multi-role conversations, and experimenting with LLM-based task coordination.

Swarm increases architectural flexibility through its minimal, stateless design and handoff-driven logic. However, it remains experimental and best suited for non-production environments. Developers should be familiar with prompt engineering and OpenAI API usage to use it effectively.

Conclusion

The world of AI agentic frameworks is growing fast, providing developers with powerful tools to build autonomous, scalable, and intelligent systems. From low-code platforms like Langflow to enterprise-ready solutions like Semantic Kernel, the right choice depends on your goals, complexity needs, and infrastructure.

When selecting a framework, weigh factors like integration flexibility, performance, and developer experience. With the right agentic framework, you can unlock new workflows, boost productivity, and elevate your data-driven decision-making.

Whether you're just beginning your journey into agentic AI development frameworks or looking to scale an existing system, these platforms offer the flexibility and power you need to bring your autonomous agents to life.