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Popular Frameworks for Building AI Agents

  • Writer: Shreyas Naphad
    Shreyas Naphad
  • Aug 17
  • 4 min read

Updated: Aug 27

Artificial Intelligence is evolving at a greater speed. The rise of agentic AI where models don’t just respond but plan, act, and adapt  is changing the way we interact with technology.


AI agents have started behaving more like digital workers where they can search the web, use tools, run tasks, and even collaborate with other agents.

But how do developers build these powerful agents?

The answer tot his question is frameworks for AI agents.


These frameworks give structure, and tools so developers don’t have to restart the process every time. In this article, we’ll explore the most popular frameworks for building AI agents, their strengths, and how they fit into real-world use cases.



1. LangChain

  • Overview: LangChain is arguably the most popular framework for building with LLMs and agents. It provides modular building blocks for connecting models, data, and external tools.

  • Core Idea: Instead of a single prompt, LangChain allows developers to chain together multiple steps of reasoning and tool use. It also supports memory, making agents better at understanding the context.

  • Key Features:

    • Chains: Multi-step workflows where each output feeds into the next input.

    • Agents: Dynamic decision-makers that can select tools at runtime (like calculators, APIs, or vector databases).

    • Memory: Storing context across conversations or sessions.

    • Integrations: Works with vector databases (Chroma, Pinecone, Weaviate) and APIs.

  • Best For: Developers building RAG pipelines, chatbots, and task-driven agents.



2. LlamaIndex

  • Overview: While its related with LangChain, LlamaIndex is a framework focused on data ingestion and retrieval for agents.

  • Core Idea: LLMs are only as good as the knowledge they can access. LlamaIndex makes it easier to feed custom data (documents, APIs, databases) into LLM-powered agents.

  • Key Features:

    • Data Connectors: Load data from PDFs, SQL databases, APIs, or Google Drive.

    • Indexes: Create structures (like tree indexes) to optimize retrieval.

    • Query Engine: Intelligent routing of user questions to the right data chunks.

  • Best For: Knowledge-augmented agents.



3. AutoGen (Microsoft)

  • Overview: AutoGen is Microsoft’s framework for multi-agent conversations. Instead of a single agent, you define multiple roles (researcher, coder, critic) that collaborate.

  • Core Idea: Many tasks are too complex for one agent. AutoGen creates teams of agents to reason, debate, and coordinate until they find the solution.

  • Key Features:

    • Multi-Agent Collaboration: Agents talk to each other, not just the user.

    • Human-in-the-Loop: A human can interrupt and guide agents.

    • Custom Roles: Define agents with specific skills (e.g., “Python expert,” “Data retriever”).

  • Best For: Complex workflows, collaborative problem solving, and research tasks.



4. Haystack (deepset)

  • Overview: Originally designed for document search and Q&A, Haystack has improved into a strong RAG + agent framework.

  • Core Idea: Provide pipelines where models can retrieve information, reason, and generate answers.

  • Key Features:

    • Pipelines: Modular structure for ingestion, retrieval, and generation.

    • Integrations: Works with Hugging Face, OpenAI, Cohere, and vector stores.

    • Agents: Can use tools and APIs beyond text retrieval.

  • Best For: Search-driven agents, enterprise chat systems, and production RAG applications.




5. CrewAI

  • Overview: CrewAI is a new framework focused on coordinating multiple specialized AI agents.

  • Core Idea: Think of CrewAI as “project management for agents” because it helps manage workflows where agents have different skills.

  • Key Features:

    • Task Assignment: Different agents handle different subtasks.

    • Collaboration: Agents can share intermediate results.

    • Autonomy: Supports long-running workflows.

  • Best For: Startup-style projects, business automation, and research tasks.



6. OpenAI Functions / Assistants API

  • Overview: OpenAI itself has added “agent-like” features in its Assistants API.

  • Core Idea: Instead of just chat completion, developers can define tools (functions) that GPT models can call. This turns LLMs into agents capable of action.

  • Key Features:

    • Function Calling: Models decide when to call APIs.

    • Persistent Threads: Memory of past conversations.

    • File Handling: Agents can work with uploaded documents.

  • Best For: Developers who want agentic behavior but with minimal overhead.



7. Semantic Kernel (Microsoft)

  • Overview: A framework for integrating LLMs into traditional applications.

  • Core Idea: It combines orchestration + planning with connectors to external services.

  • Key Features:

    • Skills & Plugins: Wrap APIs into reusable skills.

    • Planner: Helps break down user intent into sub-goals.

    • Integration: Works with Azure OpenAI, Hugging Face, and more.

  • Best For: Enterprise adoption, AI copilots inside existing software.



8. Hugging Face Transformers + Agents

  • Overview: Hugging Face is well known for its models, but it also provides an agents interface.

  • Core Idea: It uses open-source models with tool use in a simple framework.

  • Key Features:

    • Transformers: Access to thousands of pretrained LLMs.

    • Agent API: Define tasks where models can select tools.

    • Community: Huge ecosystem of models and datasets.

  • Best For: Open-source experimentation, custom model agents, and low-cost prototyping.



9. DSPy (Stanford)

  • Overview: A newer framework designed to make LLM programming more declarative and reliable.

  • Core Idea: Instead of manually prompt-engineering, you write high-level instructions, and DSPy optimizes prompts behind the scenes.

  • Key Features:

    • Declarative Design: Define tasks, not prompts.

    • Optimization: Uses reinforcement learning to refine prompts.

  • Best For: Researchers and developers who want reproducible, optimized agent workflows.



10. Other Notable Mentions

  • Cognosis / Adept: Early research-driven frameworks for agentic reasoning.

  • FastChat / Vicuna Agents: Open-source projects for conversational agents.

  • LangGraph: An extension to LangChain focusing on graph-based agent workflows.



Choosing the Right Framework

So, which framework should you use? It depends on your goal:

  • ✅ Learning & Prototyping: LangChain, Hugging Face Agents

  • ✅ Knowledge-Rich Agents: LlamaIndex, Haystack

  • ✅ Multi-Agent Collaboration: AutoGen, CrewAI

  • ✅ Enterprise Integration: Semantic Kernel, OpenAI Assistants API

  • ✅ Research & Innovation: DSPy, LangGraph


For beginners, LangChain + LlamaIndex is the best starting point. For advanced workflows, AutoGen and CrewAI can be appropriate.



Final Thoughts

Agentic AI is not just a trend but it’s a paradigm shift in how AI systems operate. Instead of static Q&A, agents bring autonomy, adaptability, and real-world action. The frameworks we’ve discussed right from LangChain and LlamaIndex to AutoGen and Hugging Face Agents are the backbone of this new era.


If you’re preparing for interviews, understanding these frameworks helps you explain how to actually build agents in practice. If you’re creating content, breaking these down into simple visuals helps to share. And if you’re learning, experimenting with even one framework will give you a hands-on taste of the agentic AI future.

The future belongs to those who can not just use AI, but control these intelligent agents in the right manner.

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