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Agents are systems that leverage large language models (LLMs) as reasoning engines to decide which actions to take and the inputs required to perform those actions. Once actions are executed, their results are fed back into the LLM to determine if further actions are necessary or if the task is complete.
In this article, we will explore 7 popular agentic frameworks that enable you to build your own multi-agent applications in minutes. These frameworks provide simple and fast solutions for integrating LLMs with external tools and data sources, making it easier than ever to create powerful, autonomous AI systems.
1. LangChain
GitHub Stars: ⭐ 108k
LangChain is one of the most popular frameworks for building applications powered by large language models. It offers a wide range of integrations and tools for creating AI applications. Notably, LangChain provides an Agents module, allowing developers to create and test agents within the LangChain ecosystem.
Repository: LangChain GitHub
2. Microsoft AutoGen
GitHub Stars: ⭐ 44.7k
AutoGen is an open-source framework for building multi-agent AI systems that can collaborate, communicate, and solve tasks autonomously. It supports dynamic workflows, natural language interactions, and scalable applications through tools like AutoGen Studio, AgentChat, Core, and Extensions.
Repository: Microsoft AutoGen GitHub
3. CrewAI
GitHub Stars: ⭐ 31.8k
CrewAI is a fast, lightweight Python framework built from scratch, independent of other agent frameworks like LangChain. It enables developers to create autonomous AI agents with high-level simplicity (Crews) and precise, event-driven control (Flows) for tailored, collaborative intelligence and task orchestration.
Repository: CrewAI GitHub
4. Haystack by Deepset
GitHub Stars: ⭐ 20.8k
Haystack is an open-source Python framework for building customizable, production-ready AI applications. With its modular architecture, it supports retrieval-augmented generation (RAG), agent workflows, and advanced search systems. Haystack integrates seamlessly with tools like OpenAI, Hugging Face, and Elasticsearch, enabling developers to create end-to-end AI systems with just a few lines of code.
Repository: Haystack GitHub
5. Hugging Face SmolAgents
GitHub Stars: ⭐ 18.9k
SmolAgents is the simplest and most lightweight framework for building powerful AI agents with minimal complexity. With a compact design (~10,000 lines of code, compared to AutoGen’s 147K), it offers streamlined functionality without unnecessary overhead. It supports a wide range of LLMs, including OpenAI, Anthropic, and Hugging Face models, and provides first-class support for Code Agents.
Repository: SmolAgents GitHub
6. LangGraph
GitHub Stars: ⭐ 12.9k
LangGraph is a low-level orchestration framework for building, managing, and deploying long-running, stateful agents. It provides durable execution, human-in-the-loop oversight, comprehensive memory capabilities, and debugging with LangSmith. Seamlessly integrated with the LangChain ecosystem, LangGraph enables developers to design, test, and deploy AI agents in days, not months.
Repository: LangGraph GitHub
7. OpenAI Agents Python
GitHub Stars: ⭐ 10.4k
The OpenAI Agents SDK is a lightweight yet powerful framework for building multi-agent workflows. Provider-agnostic, it supports the OpenAI Responses and Chat Completions APIs, along with 100+ other LLMs.
Core features include Agents (LLMs with tools, instructions, and guardrails), Handoffs (specialized control transfers between agents), Guardrails (safety checks for validation), and Tracing (built-in tools for debugging and optimizing workflows).
Repository: OpenAI Agents Python GitHub
Final Thoughts
Developing multi-agent AI solutions has never been easier, thanks to the rise of powerful Python frameworks that simplify the process. These frameworks allow you to build agents, tools, workflows, and collaborative teams of agents that can seamlessly integrate into your systems.
Here are some guides that will help you get started:
- Haystack AI Tutorial: Building Agentic Workflows
- Mistral Medium 3 Tutorial: Building Agentic Applications
- Building Agentic Application Using Streamlit and Langchain
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in technology management and a bachelor’s degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.