Brolly Academy | Agentic AI
Agentic AI Frameworks
LangGraph, CrewAI, Microsoft Agent Framework, OpenAI Agents SDK, Google ADK and More (2026)
A plain-English guide to the agentic AI frameworks that matter in October 2026: what each one does, who owns it, which languages it supports, its licence, where it shines, how MCP and A2A fit in, and a simple decision guide to pick the right one for your project or career.
Agentic AI frameworks compared for 2026: LangGraph, CrewAI, Microsoft Agent Framework, OpenAI Agents SDK, Google ADK and more, plus MCP, A2A and tips.
By Brolly Academy Team · Brolly Academy · Updated 23 October 2026
Table of Contents
1. What is an agentic AI framework?
2. What a framework actually does for you
3. Agentic AI frameworks list at a glance
6. Microsoft Agent Framework (and AutoGen)
8. Google Agent Development Kit (ADK)
11. Amazon Bedrock AgentCore (platform, not framework)
12. Frameworks vs low-code builders
14. Agentic AI frameworks comparison
15. How to choose an agentic AI framework: a decision guide
16. Common mistakes when picking a framework
Quick answer
An agentic AI framework is a software library that gives you ready-made building blocks to create AI agents that plan, call tools, remember context and work towards a goal, so you do not have to write the whole agent loop yourself. The main frameworks in October 2026 are LangGraph, CrewAI, Microsoft Agent Framework, the OpenAI Agents SDK, Google's Agent Development Kit (ADK), LlamaIndex and Pydantic AI. Amazon Bedrock AgentCore is not a framework but a cloud platform for running agents built with any framework. Two open protocols, MCP and A2A, connect agents to tools and to each other.
- Best for control and long-running workflows: LangGraph
- Best for role-based teams of agents: CrewAI
- Best for Microsoft and .NET shops: Microsoft Agent Framework (the successor to AutoGen and Semantic Kernel)
- Best for a light, quick start: OpenAI Agents SDK or Pydantic AI
- Best for Google Cloud and multi-language teams: Google ADK
Key takeaways
- A framework handles the plumbing of an agent: the model connection, tools, memory, orchestration, human approval and tracing. You focus on the business logic.
- Microsoft AutoGen is archived and Semantic Kernel is now Microsoft Agent Framework. Do not start new projects on AutoGen.
- Every framework in this guide is open source except Bedrock AgentCore, which is a managed AWS service. Most use the MIT licence; Google ADK's Python repo uses Apache-2.0.
- MCP (for tools) and A2A (for agent-to-agent talk) now both sit under the Agentic AI Foundation at the Linux Foundation, so skills you learn on one framework carry over.
- Learn the concepts with one framework deeply. Switching frameworks later is much easier than learning agents twice.
1. What is an agentic AI framework?
Short answer: An agentic AI framework is a code library that turns a large language model into a working agent by adding tools, memory, control flow and safety checks around it.
Let us start with the words. A large language model (LLM) is the AI model behind tools like ChatGPT, Gemini and Claude. It reads text and writes text. On its own, it cannot check your bank balance, book a ticket or open a file. It just answers.
An AI agent is an LLM wrapped in a loop. You give it a goal. It decides what to do next, calls a tool (a function, an API or a database query), reads the result, and decides again. It keeps going until the goal is done or it needs your help. If you want the full theory first, read our guide on what agentic AI is.
You can write that loop yourself in plain Python. Many people do for small projects. But real agents need much more than a loop:
- Tool calling: a way for the model to ask your code to run a function with specific inputs, and get the answer back.
- Memory and state: a record of what has happened so far, so the agent does not forget step 2 by step 7.
- Orchestration: the control logic that decides which step or which agent runs next, in what order, and what happens on failure.
- RAG (retrieval-augmented generation): fetching relevant documents from your own data and giving them to the model before it answers.
- Human-in-the-loop: pausing for a person to approve a risky action, like sending money or deleting a record.
- Tracing and evaluation: logs that show every step, so you can debug and measure quality.
An agentic AI framework gives you all of this as tested, reusable parts. Think of it the way you think of Django or Spring Boot for web apps. You could write your own web server, but a framework saves months and avoids common mistakes.
2. What a framework actually does for you
Short answer: A framework connects to models, defines tools, keeps state, runs single or multi-agent workflows, adds guardrails and human approval, and gives you traces to debug and evaluate.
Here is what each building block means in practice.
Two official guides explain the patterns these frameworks implement. Anthropic's "Building effective agents" (December 2024) separates workflows, where code paths are predefined, from agents, where the LLM directs its own process and tool use. It recommends starting simple. OpenAI's "A practical guide to building agents" says an agent has three parts, a model, tools and instructions, and advises starting with a single agent before you move to multiple agents. Every framework below is a different way of packaging those same ideas.
One more useful rule comes from Microsoft's own framework documentation (August 2026): if you can write a normal function to handle a task, do that instead of using an AI agent. A framework is a tool, not a goal.
3. Agentic AI frameworks list at a glance
Short answer: The eight names you need to know in October 2026 are LangGraph, CrewAI, Microsoft Agent Framework, OpenAI Agents SDK, Google ADK, LlamaIndex, Pydantic AI and Amazon Bedrock AgentCore.
The table below shows the owner, the languages and the GitHub stars as of October 2026 (read from the GitHub API on 23 October 2026). Stars show community interest, not quality, so use them as one signal only.
And here is the licence and the one-line purpose of each.
A note on two names you may still see in older articles. Microsoft AutoGen's GitHub page now says the project is archived and no longer maintained, and it points new users to Microsoft Agent Framework. Its repo still shows 61,534 stars (October 2026), but stars do not mean it is current. Flowise, a visual agent builder, was archived in August 2026, so we do not recommend it either.
4. LangGraph
Short answer: LangGraph is LangChain Inc.'s low-level, MIT-licensed framework for building stateful agents as graphs. Pick it when you need fine control, long-running tasks and human approval steps.
Owner: LangChain Inc. Languages: Python, with a JavaScript version. Licence: MIT.
In LangGraph you draw your agent as a graph. Each node is a step (call the model, call a tool, check a result). Each edge is a rule for where to go next. Because the flow is explicit, you can mix fixed, hand-coded steps with steps where the LLM decides.
The LangGraph overview page (October 2026) lists its main strengths:
- Persistence: agents survive failures, run for long periods and resume where they stopped.
- Human-in-the-loop: you can inspect and change the agent's state at any point.
- Memory: short-term working memory and long-term memory across sessions.
- Debugging: LangSmith, LangChain's tracing product, shows execution paths and state changes.
- Mixing deterministic and agentic steps in one graph.
The same page says companies such as Klarna, Uber and J.P. Morgan use it.
Where LangChain fits. LangChain (148,308 stars, October 2026, MIT) is the bigger sister library. Its repo now calls itself "The agent engineering platform". In simple terms, LangChain gives you quick, high-level agent building blocks and integrations, and LangGraph gives you the low-level control layer underneath. Many teams use both. So, yes, LangChain counts as an agentic AI framework today, and LangGraph is the part you reach for when you need control.
Best for: production agents with branching logic, approval steps and long workflows; developers who want to see and control every step.
Watch out for: a steeper learning curve than the lighter SDKs. You need to think in states and edges.
If you are deciding between LangGraph and a typed alternative, our Pydantic AI vs LangChain and LangGraph comparison walks through seven practical checks.
5. CrewAI
Short answer: CrewAI is an MIT-licensed Python framework from CrewAI Inc. that organises agents as a team of roles (a "crew"). It is the most intuitive way to model a multi-agent team.
Owner: CrewAI Inc. Language: Python. Licence: MIT.
CrewAI asks you to think like a manager. You define agents by role, goal and background (for example, "Researcher", "Writer", "Reviewer"). You define tasks. Then you put them in a crew and choose how the work runs. The CrewAI docs (October 2026) describe processes that run tasks sequentially, hierarchically (a manager agent hands out work) or in a hybrid way.
For more control, CrewAI also has Flows. The docs describe Flows as a way to orchestrate start, listen and router steps, with state management, persisted execution and the ability to resume long-running workflows.
Best for: content pipelines, research and report generation, sales research, and any problem that maps naturally to "a team of specialists". It is also a friendly first multi-agent framework for students because the role-and-task model is easy to explain.
Watch out for: role-playing crews can make more LLM calls than you expect, which raises cost and response time. Start with the smallest crew that works.
6. Microsoft Agent Framework (and AutoGen)
Short answer: Microsoft Agent Framework is Microsoft's current, MIT-licensed agent framework for Python and .NET (Go in preview). It is the direct successor to both Semantic Kernel and AutoGen. Use it for new Microsoft projects, not AutoGen.
Owner: Microsoft. Languages: Python, .NET, Go (public preview). Licence: MIT. Status: 1.0 production release.
A short history helps here, because many Indian tutorials still teach the old tools. Microsoft had two popular agent projects: AutoGen, known for multi-agent conversations, and Semantic Kernel, known for enterprise features. Both READMEs now point elsewhere. AutoGen's says "Project archived and no longer maintained". Semantic Kernel's says it "is now Microsoft Agent Framework".
The Microsoft Agent Framework overview (updated August 2026) calls it "the direct successor, created by the same teams". It combines AutoGen's simple agent abstractions with Semantic Kernel's enterprise features, such as session-based state, type safety, middleware and telemetry, and adds graph-based workflows. The page describes four main areas:
- Agents that use LLMs, call tools and MCP servers, and respond. It supports Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic, Ollama and more.
- Harness Agent, a ready-made agent for long, multi-step tasks with planning, memory and tool approval.
- Workflows, functional and graph-based, that connect agents and functions through explicit paths.
- Integrations with model providers, tools, evaluation services and UI frameworks.
The same page gives a clear rule: use an agent when the task is open-ended and needs autonomous tool use; use a workflow when the steps are well defined or several agents must coordinate in a fixed order.
Best for: companies on Azure and Microsoft Foundry, .NET developers, and teams that need enterprise features like telemetry and middleware from day one.
If you learned AutoGen: your concepts still apply. Microsoft publishes a migration guide from AutoGen and from Semantic Kernel, linked from the overview page.
7. OpenAI Agents SDK
Short answer: The OpenAI Agents SDK is OpenAI's lightweight, MIT-licensed framework for single and multi-agent workflows in Python and JavaScript/TypeScript. It has very few concepts, so it is one of the fastest ways to build your first agent.
Owner: OpenAI. Languages: Python, JavaScript/TypeScript. Licence: MIT.
The OpenAI Agents SDK documentation (October 2026) describes it as a "lightweight, powerful framework for multi-agent workflows" and lists a small set of core primitives:
- Agents: LLMs with instructions and tools.
- Handoffs: one agent passes a task to another, more specialised agent (agents can also be used as tools).
- Guardrails: checks that validate an agent's inputs and outputs.
- Sessions: a memory layer that keeps working context across turns.
- Tracing: built-in traces to visualise and debug each run.
The docs also say function tools turn any Python function into a tool, with the input schema generated for you and validated with Pydantic. Hosted tools such as web search, file search, code interpreter and a hosted MCP tool are available when you use OpenAI's Responses API models.
Here is the official "hello world" from the docs. You install it with pip install openai-agents and set your OPENAI_API_KEY environment variable first.
from agents import Agent, Runner
agent = Agent(name="Assistant", instructions="You are a helpful assistant")
result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
print(result.final_output)
Four lines of real code give you a working agent. To make it useful, you give the agent tools (for example, a function that looks up an order status), add a guardrail that rejects off-topic questions, and add a handoff to a "refunds" agent. The structure stays the same as it grows.
Best for: beginners, quick prototypes, customer-support style agents with handoffs, and teams already using OpenAI models.
Watch out for: it is intentionally minimal. For complex branching flows with many approval points, a graph framework such as LangGraph or Microsoft Agent Framework workflows gives you more explicit control.
8. Google Agent Development Kit (ADK)
Short answer: Google ADK is an open-source, model-agnostic framework to build, debug and deploy agents in Python, TypeScript, Go, Java or Kotlin. It is the widest language choice in this list and fits naturally with Google Cloud.
Owner: Google. Languages: Python, TypeScript, Go, Java, Kotlin. Licence: Apache-2.0 (Python repo).
The ADK home page (October 2026) says it is built for "reliable AI agents at enterprise scale". Its key features, as listed there:
- Multi-agent orchestration, growing from a simple prompt and tool call to teams of agents.
- Workflow agents for sequential, parallel and loop patterns, plus graph-based workflows added in ADK 2.0.
- Tools: function tools, MCP tools, OpenAPI tools and authentication support.
- Evaluation with criteria, user simulation, custom metrics and optimisation.
- Deployment in your own containers, or on Google's Agent Runtime, Cloud Run or GKE.
"Model-agnostic" means you are not forced to use Gemini, although it works best inside Google's ecosystem.
Best for: teams on Google Cloud; Java and Kotlin developers who want an agent framework in their own language; projects that need built-in evaluation.
9. LlamaIndex
Short answer: LlamaIndex is an MIT-licensed Python framework from run-llama that is strongest when your agent must read, search and reason over documents and company data.
Owner: run-llama. Language: Python. Licence: MIT. Stars: 52,631 (October 2026).
LlamaIndex became popular as a RAG library: it helps you load PDFs, web pages and databases, split them into chunks, index them and fetch the right pieces for the model. It now includes an agents framework on top of that data layer, and its GitHub repo describes the project as a "document processing platform for AI".
Best for: document Q&A agents, knowledge assistants over policies or manuals, research agents that need accurate retrieval, and "agentic RAG", where the agent decides what to search, checks the results and searches again if needed.
Watch out for: if your agent is mostly about calling business APIs rather than reading documents, a general framework may feel simpler. Many teams use LlamaIndex for the data layer and another framework for orchestration.
10. Pydantic AI
Short answer: Pydantic AI is a type-safe, MIT-licensed Python agent framework from the team behind Pydantic. Pick it if you like clean, typed Python and want validated, structured outputs.
Owner: Pydantic. Language: Python. Licence: MIT. Stars: 20,804 (October 2026).
Pydantic is a widely used Python data validation library. Pydantic AI brings the same style to agents. Its overview page (October 2026) highlights:
- Typed end to end: structured outputs, typed tools and typed dependency injection (passing database connections and other objects to tools in a testable way).
- Model-agnostic: switch providers by changing a model string.
- Observability that is OpenTelemetry-native, with one-line Pydantic Logfire setup.
- MCP support among its core features.
- Pydantic Graph for typed graph control flow when one agent loop is not enough.
- Durable execution on engines such as Temporal, DBOS and Prefect.
Best for: backend developers who already use FastAPI or Pydantic, and agents whose output feeds other software (for example, extracting an invoice into a fixed JSON shape).
To try it hands-on, follow our Pydantic AI tutorial, which builds a typed agent in seven steps.
11. Amazon Bedrock AgentCore (platform, not framework)
Short answer: Bedrock AgentCore is AWS's managed platform for running agents in production. You build the agent with any framework, then run, connect and monitor it on AgentCore.
Owner: AWS. Type: managed cloud service. Supports: "any framework, any model" (AWS page, October 2026).
This one is different from the rest. LangGraph or CrewAI help you write the agent's logic. AgentCore helps you operate it: hosting, security, tool access and evaluation at scale. The AWS page (October 2026) names frameworks such as LangChain, OpenAI Agents SDK, Claude Agent SDK and Strands SDK, plus your own framework. It describes a Gateway that connects agent tool calls to MCP servers, knowledge bases, internal APIs and Lambda functions with authentication and access control, and Evaluations that show where agents go off track.
An important update for 2026: AWS's page for the older Amazon Bedrock Agents now says "Amazon Bedrock Agents Classic is no longer available to new customers" and points to AgentCore. If you are starting fresh on AWS, start with AgentCore.
Best for: companies already on AWS that need to move agents from prototype to a secure, monitored production service.
12. Frameworks vs low-code builders
Short answer: Code frameworks give you full control; low-code builders let you drag and drop agents. Developers should learn a code framework; business users and quick automations can start with a builder.
People often search "is n8n an agentic AI framework?" Strictly, no. n8n is a fair-code workflow automation tool with AI agent features, built around a visual canvas. Tools like n8n, Dify, Langflow and Microsoft Copilot Studio sit in the low-code and no-code category. They are excellent for automations and internal tools, and you can ship something in an afternoon.
Code frameworks win when you need custom logic, version control, automated tests, evaluation and fine-grained security. If you are aiming for an AI engineering role, a code framework is the skill to build. We cover the low-code side, enterprise platforms, vector databases and observability tools in our guide to agentic AI tools.
13. Protocols: MCP and A2A
Short answer: MCP is the open standard for connecting agents to tools and data. A2A is the open standard for agents to talk to other agents. Frameworks implement these protocols, so they work together instead of competing.
A framework decides how your agent thinks and flows. A protocol decides how it plugs into the outside world. Think of it like USB: your laptop brand (the framework) does not matter if the port (the protocol) is standard.
MCP (Model Context Protocol)
MCP is an open protocol that lets any AI application connect to tools, files and data sources through a standard "server". Write one MCP server for your company's ticketing system, and any MCP-aware agent can use it, whether it is built with LangGraph, the OpenAI Agents SDK or Google ADK.
- Anthropic introduced MCP on 25 November 2024.
- On 9 December 2025, Anthropic donated MCP to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation co-founded by Anthropic, Block and OpenAI. Anthropic said the governance model would remain unchanged.
- At that time Anthropic reported more than 10,000 active public MCP servers and more than 97 million monthly SDK downloads (December 2025).
In this guide, Microsoft Agent Framework, Google ADK, Pydantic AI and the OpenAI Agents SDK all list MCP support on their official pages, and Bedrock AgentCore's Gateway connects to MCP servers.
A2A (Agent2Agent protocol)
A2A lets agents built by different teams, on different frameworks, discover each other and share tasks. A support agent built with CrewAI could ask a billing agent built with Microsoft Agent Framework for an invoice, using A2A as the common language.
- Google created A2A in April 2025.
- It became a Linux Foundation project on 23 June 2025, under the Apache-2.0 licence.
- The Linux Foundation's September 2026 newsletter says A2A is now a hosted project at AAIF and is backed by more than 150 organizations.
- Its GitHub repo shows 26,265 stars (October 2026).
Simple way to remember: MCP connects an agent to tools (vertical). A2A connects an agent to other agents (horizontal). With both now under AAIF, these are the two protocols worth learning in 2026.
14. Agentic AI frameworks comparison
Short answer: Choose by your use case and your stack: LangGraph for control, CrewAI for role-based teams, Microsoft Agent Framework for Azure and .NET, OpenAI Agents SDK for speed, Google ADK for Google Cloud and Java, LlamaIndex for documents and Pydantic AI for typed Python.
A few honest observations:
- There is no single "best agentic AI framework". The best one is the one your team can debug at 2 a.m.
- The lighter SDKs (OpenAI Agents SDK, Pydantic AI) are quickest to learn. The graph frameworks (LangGraph, Microsoft Agent Framework workflows, ADK graph workflows) give the most control.
- All of them can call tools, keep memory and run multiple agents. The differences are in style, ecosystem and how much control you get by default.
15. How to choose an agentic AI framework: a decision guide
Short answer: Start from your cloud, your language and the shape of your problem. Then build a small test agent in your top two choices and keep the one that is easier to debug.
Work through these questions in order.
1. Do you need a framework at all? If the task is a single model call or a fixed sequence of steps, plain code may be enough. Microsoft's guidance (August 2026) says if you can write a function to handle the task, do that instead. Anthropic's guide also recommends starting simple.
2. Which cloud does your company use?
- Azure or Microsoft Foundry: Microsoft Agent Framework.
- Google Cloud: Google ADK.
- AWS: any framework, deployed on Bedrock AgentCore.
- No fixed cloud: LangGraph, CrewAI, OpenAI Agents SDK or Pydantic AI.
3. Which language does your team write?
- Python only: every option works.
- .NET or C#: Microsoft Agent Framework.
- Java or Kotlin: Google ADK.
- TypeScript or JavaScript: LangGraph (JS), OpenAI Agents SDK (JS/TS) or Google ADK (TypeScript).
4. What shape is the problem?
- One agent with a few tools: OpenAI Agents SDK or Pydantic AI.
- A team of specialist roles: CrewAI.
- Many branches, approvals and long-running steps: LangGraph or Microsoft Agent Framework workflows.
- Mostly reading and searching documents: LlamaIndex, optionally with another framework for orchestration.
- Output must be strict, validated data: Pydantic AI.
5. How will you run it in production? Check that the framework supports tracing, evaluation and human approval, and that it works with MCP. Then decide where it runs: your own containers, Bedrock AgentCore, Google's Agent Runtime or Azure.
6. Run a two-day spike. Build the same small agent (for example, "answer order questions using a mock orders API") in your top two choices. Compare how clear the code is, how easy the traces are to read, and how much it costs per run.
Quick picks by who you are
16. Common mistakes when picking a framework
Short answer: The biggest mistakes are learning archived tools, picking by GitHub stars alone, jumping to multi-agent too early and skipping tracing and evaluation.
- Learning archived projects. AutoGen and Flowise are archived. Older YouTube playlists still teach them. Check the GitHub README before you invest time.
- Choosing by stars. Stars measure popularity over time, not fit. AutoGen still has more stars than Microsoft Agent Framework, yet it is no longer maintained.
- Starting with five agents. OpenAI's practical guide recommends a single agent first. Add more agents only when one agent clearly cannot handle the tools or instructions.
- Ignoring cost. Open-source frameworks are free to download, but you still pay for every model call and for hosting. Every extra agent and every loop is another model call. Measure tokens per task from day one.
- No tracing or evals. Without traces you cannot explain why an agent did something. Every framework here offers tracing or OpenTelemetry support, so turn it on.
- Hard-wiring tools to one framework. Wrap shared tools as MCP servers so you can change frameworks later without rewriting integrations.
- Framework hopping. Learning the basics of six frameworks teaches you less than building three real projects in one.
17. How to learn agentic AI frameworks step by step
Short answer: Learn Python and LLM API basics first, then tool calling, then one framework deeply, then MCP, multi-agent patterns, evaluation and deployment.
A practical order that works for most learners:
- Python and APIs: functions, classes, JSON, async basics, calling a REST API.
- LLM basics: prompts, tokens, structured output, and calling a model API directly.
- Tool calling without a framework: write one small loop by hand so you understand what frameworks hide.
- One framework deeply: OpenAI Agents SDK or Pydantic AI for a quick start, then LangGraph for control.
- RAG and memory: add document search with LlamaIndex or a vector database.
- MCP: expose one of your own APIs as an MCP server and use it from your agent.
- Multi-agent: try CrewAI or handoffs; then learn A2A concepts.
- Evaluation, guardrails and deployment: traces, test sets, human approval, and running on a cloud platform.
For a week-by-week plan with free official resources, follow our agentic AI roadmap. For portfolio ideas that use these frameworks, see our list of AI agent projects.
18. How Brolly Academy helps
Short answer: Brolly Academy's 3-month Agentic AI Course in Hyderabad teaches the leading frameworks hands-on through six capstone projects, with classroom and online options.
If you want guided practice instead of learning alone, the Agentic AI Course in Hyderabad covers the frameworks in this guide inside a structured plan:
- Duration and format: 3 months (foundations, tools and workflows, then projects and deployment, plus career prep). Classroom and online, with weekend batches and flexible schedules.
- Frameworks and tools: LangChain, LangGraph, CrewAI, LlamaIndex, OpenAI API, Anthropic Claude, Hugging Face, AWS Bedrock, Azure OpenAI and Google Vertex AI, with the course roadmap also showing OpenAI Agents SDK, Google ADK and Foundry Agent Service.
- Modern topics: tool calling, MCP basics, agentic RAG, human-in-the-loop and evaluation.
- Six capstone projects: customer support agent, document Q&A and RAG agent, workflow automation agent, email and sales research agent, resume screening and HR agent, and data analysis agent.
- Trainer: Mr. Dinesh, an Agentic AI, Generative AI Specialist and Cloud Architect with 12+ years in AI, ML, cloud computing and Generative AI.
- Fees (as listed on the course page, October 2026): Video Recording ₹9,999, Online Course ₹24,999, Classroom Training ₹29,999. Easy EMI is available.
- Career support: placement assistance, resume and LinkedIn help, mock interviews and internship opportunities. You receive a Brolly Academy course completion certificate (not a vendor certification).
- Eligibility: no AI experience needed; Python is optional; 18+.
The next batch starts on Monday, 2 November 2026, and new batches start every week. You can book a free demo class first.
Build real agents with LangGraph, CrewAI and more in 3 months
Reading about frameworks is step one. Building six working agents, from a customer support bot to a document Q&A system, is what gets you hired. Join Brolly Academy's Agentic AI Course in Hyderabad, classroom or online, and learn tool calling, MCP, agentic RAG and evaluation with a trainer who has 12+ years in AI and cloud. Book a free demo class and see a live agent being built before you decide.
Frequently asked questions
What are agentic AI frameworks?
Agentic AI frameworks are code libraries that turn a large language model into a working agent. They provide ready-made parts for tool calling, memory, orchestration, guardrails, human approval and tracing, so you do not have to build the agent loop yourself. Popular examples in October 2026 are LangGraph, CrewAI, Microsoft Agent Framework, the OpenAI Agents SDK, Google ADK, LlamaIndex and Pydantic AI.
Which agentic AI framework is best?
No single framework is best for everyone. LangGraph suits complex, long-running agents that need control. CrewAI suits role-based teams of agents. Microsoft Agent Framework suits Azure and .NET teams. The OpenAI Agents SDK and Pydantic AI are quickest to learn. Google ADK suits Google Cloud and Java teams. Pick based on your cloud, language and problem, then test your top two.
Is LangChain an agentic AI framework?
Yes. LangChain's repo now describes itself as "The agent engineering platform", and it provides high-level building blocks and integrations for agents. LangGraph, from the same company, is the lower-level framework for stateful agents built as graphs. Many teams use LangChain for quick building blocks and LangGraph when they need explicit control, persistence and human-in-the-loop steps.
Is LangGraph an agentic AI framework?
Yes. LangGraph is a low-level, MIT-licensed framework from LangChain Inc. for building stateful, long-running agents as graphs of nodes and edges. Its official page highlights persistence through failures, human-in-the-loop review, short and long-term memory, and debugging with LangSmith. It is available in Python and JavaScript and is a strong choice for production agents with branching logic.
Which agentic AI framework uses a graph of nodes and edges?
LangGraph is the best-known framework built around a graph of nodes and edges, where each node is a step and each edge decides what runs next. Microsoft Agent Framework also offers graph-based workflows, Google ADK added graph-based workflows in ADK 2.0, and Pydantic AI offers Pydantic Graph for typed graph control flow.
What is Microsoft's agentic AI framework?
Microsoft's current agentic AI framework is Microsoft Agent Framework. It is MIT-licensed, supports Python and .NET, has Go in public preview, and has reached a 1.0 production release. Microsoft calls it the direct successor to Semantic Kernel and AutoGen, created by the same teams. It combines simple agent abstractions with enterprise features and adds graph-based workflows.
Is AutoGen still worth learning?
No, not for new projects. AutoGen's GitHub README says the project is archived and no longer maintained, and it points new users to Microsoft Agent Framework. If you already know AutoGen, your concepts carry over, and Microsoft publishes a migration guide. For new learning and new projects, start with Microsoft Agent Framework instead.
What is Google's agentic AI framework?
Google's framework is the Agent Development Kit, or ADK. It is open source and model-agnostic, and it supports Python, TypeScript, Go, Java and Kotlin. The Python repo uses the Apache-2.0 licence. ADK offers multi-agent orchestration, sequential, parallel and loop workflow agents, MCP and OpenAPI tools, built-in evaluation, and deployment to containers, Cloud Run, GKE or Google's Agent Runtime.
What is the AWS agentic AI framework?
AWS's main offering is Amazon Bedrock AgentCore, which is a managed platform rather than a coding framework. You build your agent with any framework, such as LangChain or the OpenAI Agents SDK, and run it on AgentCore. AWS says the older Bedrock Agents Classic is no longer available to new customers, so new AWS projects should start with AgentCore.
Is n8n an agentic AI framework?
Not strictly. n8n is a fair-code workflow automation tool with AI agent features, built around a visual canvas. It belongs with low-code builders like Dify, Langflow and Microsoft Copilot Studio. These are great for automations and internal tools. Code frameworks such as LangGraph or CrewAI give you more control, testing and version control for custom production agents.
Which agentic AI frameworks are open source?
LangGraph, LangChain, CrewAI, Microsoft Agent Framework, the OpenAI Agents SDK, LlamaIndex and Pydantic AI are open source under the MIT licence. Google ADK is open source too, with its Python repo under Apache-2.0. Amazon Bedrock AgentCore is the exception, because it is a managed AWS service. Open source means the code is free, but you still pay for model API usage.
Are agentic AI frameworks free to use?
The open-source frameworks are free to download and use under their MIT or Apache-2.0 licences. Your real cost comes from the model calls your agent makes and from where you host it. Every extra agent, loop or tool call adds model usage, so measure tokens per task early. Managed platforms such as Bedrock AgentCore are paid AWS services.
Which agentic AI framework is best for beginners?
The OpenAI Agents SDK is one of the easiest starts, because it has few concepts: agents, handoffs, guardrails, sessions and tracing. Its official hello world is four lines of code. Pydantic AI is another clean option if you like typed Python. CrewAI is friendly for a first multi-agent project. Move to LangGraph once you need more control.
Which agentic AI frameworks support Python?
Every framework in this guide supports Python. LangGraph, LangChain, CrewAI, LlamaIndex and Pydantic AI are Python-first. Microsoft Agent Framework supports Python and .NET. The OpenAI Agents SDK has Python and JavaScript versions. Google ADK supports Python plus four other languages. Python is the safest single language to learn if you want to work with agents.
Are there agentic AI frameworks for Java or TypeScript?
Yes. Google ADK supports Java and Kotlin as well as TypeScript, which makes it the strongest choice for Java teams. For TypeScript or JavaScript, you can use LangGraph's JavaScript version, the OpenAI Agents SDK for JavaScript and TypeScript, or Google ADK's TypeScript version. .NET developers can use Microsoft Agent Framework.
What is the difference between a framework and a protocol in agentic AI?
A framework is the code library you use to build an agent's logic, flow, memory and tools. A protocol is a shared standard for how agents connect to the outside world. MCP connects agents to tools and data, and A2A lets agents talk to other agents. Frameworks implement protocols, so agents built on different frameworks can still work together.
What is MCP in agentic AI?
MCP, the Model Context Protocol, is an open standard that lets AI applications connect to tools, files and data through standard servers. Anthropic introduced it in November 2024 and donated it in December 2025 to the Agentic AI Foundation under the Linux Foundation. Anthropic then reported over 10,000 active public MCP servers and over 97 million monthly SDK downloads.
What is the A2A protocol?
A2A, the Agent2Agent protocol, lets agents built by different teams and frameworks discover each other and share tasks. Google created it in April 2025, and it became a Linux Foundation project in June 2025 under the Apache-2.0 licence. The Linux Foundation said in September 2026 that A2A is now hosted at the Agentic AI Foundation and backed by over 150 organizations.
Do I need a framework to build an AI agent?
Not always. For a single model call or a fixed sequence of steps, plain code is often better. Microsoft's guidance says if you can write a function for a task, do that instead of using an agent. Frameworks become valuable when you need tools, memory, multiple agents, human approval, tracing and production deployment. Writing one small loop by hand first is great practice.
What is the difference between LangGraph and CrewAI?
LangGraph models an agent as an explicit graph of steps, giving you fine control over state, branching, persistence and human approval. CrewAI models agents as a team of roles with goals and tasks, run sequentially, hierarchically or in a hybrid way, with Flows for more control. LangGraph suits complex production logic; CrewAI suits role-based multi-agent pipelines.
When should I use LlamaIndex for agents?
Use LlamaIndex when your agent mainly needs to read, search and reason over documents and company data. It is strong at loading, indexing and retrieving content for RAG, and it includes an agents framework on top. It suits document Q&A, knowledge assistants and agentic RAG. Many teams pair it with another framework that handles orchestration.
Is Pydantic AI a good agentic AI framework?
Yes, especially for backend Python developers. Pydantic AI is MIT-licensed and type-safe end to end, with structured outputs, typed tools and typed dependency injection. It is model-agnostic, supports MCP, offers OpenTelemetry-native observability with Logfire, includes Pydantic Graph for graph control flow, and supports durable execution on engines like Temporal, DBOS and Prefect.
Can I use more than one agentic AI framework together?
Yes, and many teams do. A common pattern is LlamaIndex for document retrieval with LangGraph or another framework for orchestration. Protocols make mixing easier: tools exposed as MCP servers work with any MCP-aware framework, and A2A lets agents on different frameworks share tasks. You can also build with any framework and deploy on Bedrock AgentCore.
How long does it take to learn an agentic AI framework?
If you already know Python and APIs, you can build a first agent with a light framework like the OpenAI Agents SDK very quickly, since its hello world is four lines of code. Becoming job-ready takes longer, because you also need tool calling, RAG, MCP, multi-agent patterns, evaluation and deployment. Brolly Academy's Agentic AI Course covers this path in 3 months with six capstone projects.
Does Brolly Academy teach agentic AI frameworks?
Yes. Brolly Academy's 3-month Agentic AI Course in Hyderabad covers LangChain, LangGraph, CrewAI, LlamaIndex and major model platforms, and its roadmap shows the OpenAI Agents SDK, Google ADK and Foundry Agent Service. It includes tool calling, MCP basics, agentic RAG, human-in-the-loop and evaluation, with six capstone projects, in classroom and online formats.
Learn with Brolly Academy
Review the syllabus, practical projects and batch timings, and book a free demo before you enrol.
Related Articles
Continue Your Learning
Agentic AI Course in Hyderabad
3 months of hands-on training in AI agents, multi-agent systems, LLMs, tools and frameworks, with 6 capstone projects and placement assistance.
View CourseAI Orchestration Course in Hyderabad
Build LLM workflows with LangGraph, APIs, RAG and human approvals, and learn to run AI pipelines that are reliable in production.
View CourseAzure AI Course
Microsoft Foundry, Azure OpenAI, RAG, agents and evaluation, for building AI apps and agents on Azure.
View Course