Top 5 AI Agent Frameworks in 2025

The field of artificial intelligence (AI) is rapidly evolving, with AI agents emerging as a significant advancement in how we interact with technology and automate tasks. AI agents are autonomous software entities that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike traditional AI models like large language models (LLMs) that operate solely based on their training data, AI agents can interact with external systems, learn from those interactions, and execute tasks with a degree of independence . Some AI agents may still involve human-in-the-loop interactions, allowing for a combination of automated decision-making and human oversight .

This blog post delves into the top 5 AI agent frameworks, providing a comprehensive overview of their key features, use cases, advantages, and disadvantages. The selection of these frameworks is based on a combination of factors, including their popularity within the AI community, the breadth of their capabilities, and their potential to shape the future of AI agent development.

1. LangChain

LangChain has rapidly gained popularity as a framework for developing applications powered by large language models (LLMs). It streamlines the development of complex LLM-powered applications by providing a structured and modular approach. LangChain employs a modular architecture where each module represents an abstraction that encapsulates the complex concepts and necessary steps for working with LLMs. These modules can be chained together to create sophisticated AI applications. This modularity empowers developers to build customized applications by combining different components and tailoring them to specific needs.

One of LangChain's key strengths is its ability to build applications that involve LLMs and intricate workflows. It seamlessly integrates with APIs, databases, and external tools, making it highly flexible for various applications. This adaptability is particularly beneficial for use cases such as:

• Building conversational assistants

• Automating document analysis and summarization

• Creating personalized recommendation systems

• Developing research assistants across various domains

Furthermore, LangChain offers support for vector databases and utilities for incorporating memory into applications. This allows applications to retain history and context, enabling more sophisticated and personalized interactions.

LangChain is well-suited for both established corporations with large-scale natural language processing (NLP) needs and startups developing AI-powered products.

However, building and running applications in LangChain, especially those involving large language models and external integrations, can be resource-intensive. Additionally, LangChain relies on several external dependencies and integrations, which may require constant updates or troubleshooting.

2. AutoGen

Developed by Microsoft, AutoGen is a powerful framework that automates the generation of code, models, and processes needed for complex workflows. It leverages LLMs to assist developers in building, fine-tuning, and deploying AI solutions with minimal manual coding. This automation significantly lowers the barrier to entry for developers who may not have extensive AI expertise, enabling them to harness the power of AI agents more easily.

AutoGen's architecture consists of three layers:

• Core: A programming framework for developing a scalable and distributed network of agents, with tools for tracing and debugging agent workflows.

• AgentChat: Built on top of Core, AgentChat can be used to craft conversational AI assistants. It's the recommended starting point for beginners, offering default single agents and multiagent teams with predefined behaviors and interaction patterns.

• Extensions: A package containing implementations of Core and AgentChat components to further expand their capabilities and interface with external libraries and other services. Developers can use built-in extensions, those developed by the AutoGen community, or even create their own.

AutoGen also provides two handy developer tools:

• AutoGen Bench: For assessing and benchmarking agentic AI performance.

• AutoGen Studio: A no-code interface for developing agents.

AutoGen's strengths lie in its focus on automation, which streamlines the creation of customized agents, and its user-friendly design, making it accessible even for those without an extensive AI background. This ease of use allows a wider range of developers to leverage AI without needing specialized knowledge, simplifying the development process significantly.

Compared to frameworks like LangChain, AutoGen prioritizes standardization over extensive customization. It is recommended for targeted, well-defined use cases where reliability and seamless Microsoft ecosystem integration are paramount, rather than highly customized AI applications requiring granular control over the development stack.

3. Phidata (It is Agno now)

Phidata is a Python-based framework designed to simplify the process of converting large language models (LLMs) into agents for AI products. It offers compatibility with both closed and open LLMs from various providers, including OpenAI, Anthropic, Cohere, Ollama, and Together AI. This versatility allows developers to choose the LLM that best suits their specific needs and preferences.

One of Phidata's key strengths is its extensive support for databases and vector stores. It seamlessly connects AI systems with popular databases like Postgres and PgVector, as well as vector databases such as Pinecone and LanceDb. This integration enables developers to leverage existing data infrastructure and build AI agents that can access and process information from various sources.

Phidata empowers developers to create a wide range of AI agents, from basic agents to more advanced ones that utilize function calling, structured output, and fine-tuning techniques. This flexibility makes it suitable for various applications, including:

• Building conversational AI assistants that can interact with users in natural language

• Developing AI-powered agents for automating tasks such as data analysis, content generation, and research

• Creating personalized recommendation systems that can provide tailored suggestions to users

Phidata offers different pricing options, including free, Pro, and enterprise plans, catering to the needs of individual developers, startups, and large organizations.

4. LangGraph

LangGraph leverages graph-based architectures to manage stateful AI workflows. This framework excels at orchestrating complex workflows for multi-agent systems by providing a visual and structured approach to managing agent interactions and dependencies.

LangGraph employs a graph architecture where nodes represent specific tasks or actions of AI agents, while the transitions between those actions are represented as edges. A state component maintains the task list across all interactions. This type of architecture is suitable for cyclical, conditional, or nonlinear workflows, enabling dynamic decision-making and human intervention.

LangGraph simplifies complex stateful systems with advanced logic and offers flexible API integration for real-world applications. It is particularly well-suited for industries that require precise, sequential task execution, such as healthcare or supply chain management.

For example, an airline could use LangGraph to build a travel assistant AI agent that helps users find and book flights. Each action (finding flights, selecting flights, booking flights) would be represented as a node in the graph, and those nodes can have multiple agents performing particular tasks. If the user needs to modify their search or booking, the agent can easily transition back to previous nodes in the graph, ensuring a flexible and adaptable workflow.

5. CrewAI

CrewAI is an orchestration framework for multi-agent AI solutions. Its unique role-based architecture treats agentic AI as a "crew" of "workers," promoting collaboration and communication between agents. This approach is particularly valuable for building AI systems that mimic human teamwork and can handle complex tasks through coordinated effort.

Key components of CrewAI include:

• Agents: Assigned specialized roles while collaborating on complex workflows. Developers can use natural language to outline an agent's role, goal, and backstory, providing a more intuitive and human-like definition of agent behavior.

• Tasks: Define the specific responsibilities of each agent. These tasks are also described in natural language, along with the expected output, making it easier for developers to define and manage agent workflows.

• Process: Identifies how agents work together and how tasks are executed. It can be sequential, with tasks completed according to a preset order, or hierarchical, with a custom manager agent overseeing task delegation, execution, and completion.

CrewAI is particularly well-suited for startups focused on building collaborative AI systems. It is ideal for applications that demand human-AI or multi-agent cooperation, such as virtual assistants, fraud detection, or personalized learning platforms where seamless collaboration and coordination are essential.

Frameworks Comparison

Conclusion

In conclusion, the landscape of AI agent frameworks in 2025 is diverse and dynamic, offering a range of solutions tailored to different needs and applications. From LangChain's versatility in LLM-powered applications to AutoGen's focus on automation and user-friendly design, each framework brings unique strengths to the table. Agno (formerly Phidata) excels in LLM-to-agent conversion with robust database support, while LangGraph and CrewAI offer innovative approaches to managing complex workflows and collaborative problem-solving, respectively. As AI continues to evolve, these frameworks will play a crucial role in shaping the future of AI agent development, enabling developers to create more sophisticated, efficient, and adaptable AI systems.