Seven leading AI agent frameworks compared for 2026 — LangGraph, CrewAI, LlamaIndex, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, and PydanticAI — with pros, cons, and honest guidance on which to choose.
Every few months in AI development, the same question shows up in a team channel: "Which agent framework should we standardize on?" On the surface the answers sound interchangeable. Below the surface, the seven most-discussed AI agent frameworks in 2026 take meaningfully different approaches — some are graph runtimes, some model agents as a crew of specialists, one is a data framework first, and one is now the merged successor to two older Microsoft projects. For most teams, picking among these AI agent frameworks has become the first real product decision of any agent project.
This is not a generic guide to agentic AI — it's an AI agent framework comparison built around the projects teams actually reach for: LangGraph, CrewAI, LlamaIndex, OpenAI Agents SDK, Google Agent Development Kit (ADK), Microsoft Agent Framework, and PydanticAI, on what they actually are, what they handle well, where they fall short, and which project they fit best.
AI agent development frameworks come in many shapes, but each one is a toolkit that turns an LLM into a program that can reason and act over multiple steps. The framework typically provides the agent loop (ask the model, run tools, feed results back), a way to define tools, storage for state or memory, and — in most cases — orchestration for running several agents together. Good AI agent frameworks let you stay close to the model while handling the boring parts — tool wiring, state, retries — for you.
The differences between these frameworks come down to the mental model they push you toward. Some structure work as a graph of nodes and edges. Some model it as a team of role-playing agents. Some are built around retrieving and reasoning over your own data. That model shapes everything downstream: how you design, debug, and deploy the system.
Most AI agent frameworks share the same core loop. An agent receives a task, calls the model, receives a decision to either produce an answer or invoke a tool, executes the tool, and feeds the result back into the context. This continues until the task completes or the loop hits a limit.
Frameworks add capabilities on top of that loop rather than replacing it. Common additions include:
The frameworks below implement these differently, and those differences matter more than the shared vocabulary.
LangGraph, the low-level orchestration runtime from LangChain, is one of the most flexible AI agent frameworks for graph-style control. It represents an agent as a graph — nodes are steps, edges are transitions — and state flows through that graph as it executes. You define both deterministic, hand-coded steps and LLM-driven agentic steps in the same graph, which gives you unusually precise control over behavior.
Its core strengths are durable execution, checkpointing, streaming, and human-in-the-loop control. Graph state is saved to a checkpointer at each step, so long-running agents can be interrupted, inspected, resumed, or rolled back. You can use it standalone or pair it with LangChain's broader toolkit. It ships for Python and for JavaScript/TypeScript (LangGraph.js), and is MIT-licensed.
What it's good at: long-running, stateful workflows; exact control over execution flow; human approval points; branching and subgraphs.
Trade-offs: it's lower-level than the rest — you write more orchestration yourself, and the graph model has a real learning curve.
Who should choose it: teams that need fine-grained control, durable long-running agents, or a proven runtime for complex production workflows.
Of all the AI agent frameworks in this comparison, CrewAI is the one most clearly built around a team metaphor. You define specialist agents with roles, goals, and tools, assign them tasks, and organize them into Crews that work toward a shared outcome. Alongside Crews, Flows provide event-driven, stateful orchestration with branching, loops, and clean integration between agents and regular Python code.
It's a standalone framework — built from scratch, not on LangChain — and MIT-licensed. Crews handle autonomous collaboration, while Flows give you deterministic control, and you can combine the two, which is the pattern CrewAI now recommends. It supports sequential and hierarchical processes, memory, knowledge sources, checkpointing, and MCP/A2A tool connectivity.
What it's good at: multi-agent collaboration where role-based specialists make sense; moving from prototype to production with structured orchestration.
Trade-offs: the role and task abstraction can feel heavyweight for simple single-agent jobs, and heavier production features layer on top of the framework rather than living in the core.
Who should choose it: teams building multi-agent systems with clearly defined roles — research pipelines, content workflows, and business-process automations.
LlamaIndex is unusual among AI agent frameworks because it started as a data framework rather than an agent toolkit. Its home turf is Retrieval-Augmented Generation: data connectors, indices, retrievers, query engines, and document parsing. But it has grown real agent capabilities, including FunctionAgent, ReActAgent, CodeActAgent, and AgentWorkflow for multi-agent systems with handoffs.
Its event-driven Workflows let you combine agents, data connectors, and tools into multi-step, stateful processes that you can deploy as microservices. It's open source (MIT), available in Python and TypeScript, and has hundreds of integration packages. LlamaCloud adds managed services — most notably LlamaParse for agentic document parsing and extraction.
What it's good at: RAG and document-heavy applications; building agents over your own data; parsing complex documents (PDFs, tables, slides) into LLM-ready content.
Trade-offs: if your application is pure agent orchestration with no meaningful data layer, the framework's data focus is more than you need.
Who should choose it: developers building search, assistants, and knowledge-work automation over documents and other private data.
The OpenAI Agents SDK is one of the lightweight AI agent frameworks, with a deliberately small set of primitives: agents (an LLM with instructions and tools), tools, guardrails, and handoffs. It's the production-ready successor to OpenAI's earlier Swarm experiment. Orchestration is via handoffs, where a specialist agent takes over a turn, or agents-as-tools, where a manager agent calls specialists and keeps control.
It's Python-first with a JavaScript/TypeScript build, and although it's from OpenAI, it's provider-flexible — it supports OpenAI's Responses and Chat Completions APIs plus a long list of other LLMs. It includes built-in tracing, sessions for state across turns, guardrails for input/output validation, and sandbox agents with native execution environments for work on files, repositories, and code. Guardrails, sessions, and sandbox execution make it a strong fit for production agent applications.
What it's good at: rapid multi-agent applications with clean delegation; resumable sessions; safe code execution in sandboxes; observability out of the box.
Trade-offs: it's thinner on built-in data/RAG tooling than LlamaIndex, and some heavier orchestration patterns are handled in code rather than via a workflow engine.
Who should choose it: developers who want a small, modern agent runtime with solid guardrails and handoffs, and who are comfortable with OpenAI-centric documentation.
Google's Agent Development Kit is the most deployment-first of the cloud-native AI agent frameworks here, and it's built code-first. It's the same framework behind Google's Agentspace and its customer-engagement suite. ADK 2.0 introduced a graph-based Workflow runtime with routing, fan-out/fan-in, loops, state management, dynamic nodes, and human-in-the-loop steps, alongside two core primitives — Agent (instructions and tools) and Workflow (graph-based orchestration).
It's model-agnostic but optimized for Gemini, and supports multiple languages: Python, TypeScript, Go, Java, and Kotlin. ADK 2.0 reached general availability across the runtimes, and the project is Apache 2.0 licensed. Deployment targets include Cloud Run, Google Kubernetes Engine, and Vertex AI's managed agent runtime, and it ships with tracing, evaluation tooling, and context management built in.
What it's good at: production agents on Google Cloud; multi-agent hierarchies; interoperability with LangChain, LangGraph, and CrewAI; evaluation and observability.
Trade-offs: the deepest integrations — Vertex AI managed runtime, particular model features — pull you toward Google's ecosystem.
Who should choose it: teams already on Google Cloud or Gemini who want a framework with first-class deployment and evaluation tooling.
Microsoft Agent Framework is the unified successor to Semantic Kernel and AutoGen — and the only major AI agent framework that treats .NET as a first-class language. It combines AutoGen's simple agent abstractions and multi-agent patterns with Semantic Kernel's enterprise features — session-based state management, type safety, middleware, filters, and telemetry — and adds graph-based workflows for explicit multi-agent orchestration.
It reached 1.0 general availability on April 3, 2026, and is supported as the single call-to-action for new agent development on Microsoft's stack. Both Semantic Kernel and AutoGen are now in maintenance mode with published migration guides. The framework is open source and supports .NET and Python as first-class languages, with model and tool support across the broader ecosystem.
What it's good at: enterprise agent applications on Microsoft and Azure; long-running workflows with state, checkpointing, and human-in-the-loop; teams already invested in Semantic Kernel or AutoGen.
Trade-offs: it's younger than the frameworks it replaces, and .NET teams benefit most while pure Python teams have many equally mature options.
Who should choose it: organizations standardizing on the Microsoft stack, and teams migrating existing Semantic Kernel or AutoGen applications.
PydanticAI, the Python agent framework from the team behind Pydantic validation, is one of the most type-safe AI agent frameworks in this list. It's model-agnostic — OpenAI, Anthropic, Google, Groq, Ollama, and a long list of other providers are swappable with a one-line model string. Its defining feature is type safety: structured outputs, tool arguments, and dependency injection are all validated against Pydantic models, so your type checker catches errors before runtime.
It's MIT-licensed and reached a stable v2.0 in June 2026. Beyond the core loop it offers Pydantic Graph for typed graph workflows, MCP support, observability via Logfire or any OpenTelemetry backend, evals for testing agent behavior, and durable execution on Temporal, DBOS, or Prefect for long-running or human-in-the-loop work.
What it's good at: production Python applications where validated, typed output matters; clean structured outputs; observability and evals; durable long-running agents.
Trade-offs: it's a smaller, focused core — heavier batteries like memory and full agent harnesses live in the companion Pydantic AI Harness.
Who should choose it: Python teams who want type-safe, validated agents with strong observability, especially where structured data flows into other systems.
| Framework | Best for | Main strength | Language / ecosystem | License |
|---|---|---|---|---|
| LangGraph | Complex, stateful workflows | Durable execution + human-in-the-loop | Python, JS/TS (LangChain) | MIT |
| CrewAI | Role-based multi-agent teams | Crews + event-driven Flows | Python | MIT |
| LlamaIndex | RAG and document apps | Data connectors + agent workflows | Python, TypeScript | MIT |
| OpenAI Agents SDK | Lightweight multi-agent apps | Handoffs, guardrails, sandbox agents | Python-first + JS/TS | MIT |
| Google ADK | Production agents on Google Cloud | Graph workflows + Vertex AI deployment | Python, TS, Go, Java, Kotlin | Apache 2.0 |
| Microsoft Agent Framework | Enterprise .NET/Python agents | SK + AutoGen unification, workflows | .NET, Python | MIT |
| PydanticAI | Type-safe Python applications | Validated structured outputs | Python | MIT |
Every framework here is MIT-licensed except Google ADK, which is Apache 2.0. This table condenses the sections above rather than replacing them. The real decision factors between AI agent frameworks — state model, orchestration style, data needs, deployment target, and language — do not all fit cleanly in a row.
OpenAI Agents SDK and CrewAI are the most approachable AI agent frameworks for a first project. The Agents SDK has a small primitive set and clear docs. CrewAI's role-and-task model is intuitive when you're thinking about agents as a team. If the team is anchored to Google, ADK is also gentle to start.
LangGraph is the strongest of these AI agent frameworks when you need precise control over a long-running, branching, stateful process — the graph model, checkpointing, and human approval points are exactly built for that. CrewAI Flows and Microsoft Agent Framework workflows cover similar territory with a different control model.
LangGraph vs CrewAI tends to be the first comparison people make, but the two frameworks are not interchangeable — they sit at different levels of abstraction. LangGraph is a low-level orchestration runtime: you define a graph of nodes and edges, thread state through it, and control transitions and human-approval points yourself. CrewAI is a higher-level team abstraction: you name specialist roles, assign tasks, and let Crews and Flows handle much of the execution. If you need precise control over a branching, stateful process, LangGraph gives you that control directly. If you want role-based multi-agent collaboration and don't need to hand-wire every edge, CrewAI removes the busywork. The trade-off cuts both ways: LangGraph's control costs more wiring and a steeper learning curve, while CrewAI's convenience means the heavyweight parts of production run on top of the framework. If you're still deciding between the two, prototype the same small workflow in each.
CrewAI is the most team-shaped option among the AI agent frameworks here when your problem maps to named specialists that collaborate. OpenAI Agents SDK if you prefer handoffs and agents-as-tools with guardrails. ADK and Microsoft Agent Framework both add graph-based workflows for explicit multi-agent orchestration. Between them, these cover the spectrum of agent orchestration frameworks — from simple handoff chains to explicit graph control.
Microsoft Agent Framework remains the most enterprise-native choice among these AI agent frameworks when your stack runs on Azure and .NET, with features inherited from Semantic Kernel. ADK is the counterpart if the enterprise stack runs on Google Cloud with Vertex AI. Neither should be chosen purely because the word "enterprise" appears in the docs — pick based on your platform.
LlamaIndex is the clear winner among the AI agent frameworks covered here for retrieval-heavy work — it is the data framework of the group. If you're weighing LangGraph vs LlamaIndex, the question usually comes down to orchestration control versus retrieval depth: LangGraph gives you the graph, LlamaIndex gives you the data toolkit. For simpler retrieval needs inside a general agent, CrewAI, OpenAI Agents SDK, or PydanticAI can each call an existing retrieval pipeline as a tool.
PydanticAI is the type-safe choice among the AI agent frameworks reviewed here — built specifically for validated, structured Python agents. If you ship software where structured outputs and typed tool calls prevent production bugs, it's the most direct fit.
Because AI agent frameworks connect models to tools, APIs, files, and external systems, security deserves attention in any framework choice. The frameworks handle this differently, and most of the burden stays on the developer. Licensing matters here too: all seven are open source, so the code is auditable before you rely on it.
Verified capabilities include sandboxed execution for agents (OpenAI Agents SDK's sandbox agents), human-in-the-loop approval in LangGraph, CrewAI, ADK, PydanticAI, and Microsoft Agent Framework, guardrails for input and output validation (OpenAI Agents SDK, ADK), and console/session-based state management across the major frameworks. All support MCP tool connections, which means credentials reach external tools — so secrets management, least-privilege permissions, and per-environment isolation remain your responsibility.
We are not making product-specific security attestations here. Before choosing a framework for regulated or sensitive workloads, read its security documentation, review how it handles tool permissions and state persistence, and confirm the deployment isolation options for your infrastructure. Treat "use a framework" as the start of your security design, not the end.
Picking between AI agent frameworks raises the same questions over and over, so here are the short answers.
What is an AI agent framework?
An AI agent framework is a library or toolkit that provides the building blocks for creating agents — programs that use a large language model to plan, call tools, and complete multi-step tasks. Most frameworks handle the agent loop, tool calling, state or memory, and multi-agent orchestration so you don't build that plumbing yourself.
Which AI agent framework is best in 2026?
There's no single best framework — the right choice depends on your language, workflow complexity, and data needs, which is why teams disagree so much on AI agent frameworks. LangGraph is strong for long-running, stateful graphs. CrewAI suits role-based multi-agent teams. LlamaIndex leads for RAG and document-heavy apps. OpenAI Agents SDK offers a lightweight, provider-flexible option. Google ADK fits Gemini-heavy stacks. Microsoft Agent Framework is the choice for .NET teams. PydanticAI is ideal for type-safe Python applications.
Which AI agent framework is best for beginners?
OpenAI Agents SDK and CrewAI are often the most approachable. The Agents SDK keeps a small set of primitives, and CrewAI's role-and-task model is intuitive for collaborative agents. Google ADK also has a gentle learning curve. Everything still requires Python and some comfort with async programming.
Is LangGraph better than CrewAI?
In a direct LangGraph vs CrewAI decision, the two frameworks answer different questions. LangGraph is a low-level orchestration runtime for stateful, graph-based workflows with durable execution and human-in-the-loop control. CrewAI is built around role-based agents collaborating through Crews and event-driven Flows. Use LangGraph when you need fine-grained control over a complex graph; use CrewAI when a team-of-specialists abstraction fits your problem.
Which framework is best for RAG applications?
LlamaIndex is the most RAG-oriented option here — it started as a data framework and provides connectors, indices, retrievers, query engines, and document parsing. You can still build retrieval into agents with LangGraph, CrewAI, OpenAI Agents SDK, or PydanticAI, but LlamaIndex gives you the deepest data toolkit out of the box.
Which AI agent framework is best for Python developers?
All seven frameworks support Python, so it comes down to your workflow. PydanticAI is Python-first and type-safe end to end. LangGraph, CrewAI, LlamaIndex, and Google ADK are all Python-native. OpenAI Agents SDK is Python-first with a TypeScript build. Microsoft Agent Framework supports Python and .NET, so it suits teams that also ship C#.
Do AI agents need a framework?
No — you can build an agent loop by hand against any model API. Frameworks earn their place once you need tools across multiple providers, durable execution, memory, guardrails, or orchestration across several agents. Start with a plain loop until the plumbing becomes the problem you're actually solving.
How should developers choose an AI agent framework?
Match the framework to your team's language, the architecture that fits your workflow (graph, crew, event-driven flow, or simple loop), your data and retrieval needs, and the model providers you rely on. This is cheap insurance: prototype the same small task in your top two candidates before committing to any of these AI agent frameworks.
The seven AI agent frameworks covered here survive on different ideas of what an agent is. LangGraph treats it as a graph you control step by step. CrewAI treats it as a team of specialists. LlamaIndex treats it as a system that reasons over your data. OpenAI Agents SDK treats it as a clean loop with guardrails and handoffs. Google ADK and Microsoft Agent Framework treat it as an orchestrated workflow for their cloud platforms. PydanticAI treats it as a typed, validated Python object. That is exactly why debates about AI agent frameworks rarely settle on a single winner.
None of them is universally best; the best AI agent frameworks for 2026 differ by use case, so treat framework choice as a product decision rather than a popularity contest. Match the architecture to the workflow, the language to your team, and the deployment target to your infrastructure — then verify the fit by building something real. The AI Agents category and the compare tool are useful starting points for surveying the wider agent ecosystem before you commit.
If you're also choosing agent applications rather than building on AI agent frameworks yourself — for example comparing Manus AI, Devin AI, and Genspark, or pairing coding agents like Claude Code and Cursor with research tools such as NotebookLM — those decisions sit one layer above the framework layer underneath.
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Written by
Editorial Team
The AI Tools Vault editorial team researches, tests, and reviews the best AI tools across every category.
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