7 AI Agent Frameworks Compared for 2026

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 AI agent frameworks in this guide take meaningfully different approaches — some are graph runtimes, one models agents as a crew of specialists, one is a data framework first, and one is now the 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 AI agent framework comparison is built around the projects developers actually reach for: LangGraph, CrewAI, LlamaIndex, OpenAI Agents SDK, Google Agent Development Kit (ADK), Microsoft Agent Framework, and PydanticAI. It covers what each project actually is, what changed in 2026, how they differ under the hood, and how to choose. The AI Agents category and the compare tool are useful starting points for surveying the wider agent ecosystem, but this guide goes one level deeper than a feature table.

What an AI Agent Framework Actually Does

An agent framework turns an LLM into a program that can reason and act over multiple steps. The core loop is the same across AI agent frameworks: receive a task, call the model, either produce an answer or invoke a tool, feed the tool result back, and repeat until done. What frameworks add is everything around that loop — tool wiring, state, retries, orchestration, guardrails, and observability.

The important distinction is that the seven projects below are not all the same abstraction at the same layer:

• LangGraph is an orchestration runtime built around a graph of nodes and edges.
• CrewAI is a higher-level team abstraction where specialists with roles and tasks collaborate.
• LlamaIndex is a data framework that grew agents and workflows, with retrieval as its home turf.
• OpenAI Agents SDK is a lean, primitives-first SDK with guardrails, sessions, and tracing built in.
• Google ADK and Microsoft Agent Framework are cloud-native workflow kits with first-class deployment paths.
• PydanticAI is a typed Python SDK where validation and structured output are the point.

Pretending all of these solve the same layer is where most AI agent framework comparisons go wrong. A framework provides software abstractions; it does not automatically make the resulting system reliable or safe. Durable execution, human approval, observability, and security are separate concerns that each project handles (or doesn't) in its own way.

What Changed in AI Agent Frameworks in 2026

2026 has been the year AI agent frameworks converged on production concerns: durable execution, checkpointing, human-in-the-loop, and observability. The biggest changes are verifiable from official sources:

• Microsoft shipped Microsoft Agent Framework 1.0 on April 2, 2026, for Python and .NET. Microsoft describes it as the next generation of both Semantic Kernel and AutoGen, and AutoGen is now officially in maintenance mode with a published migration guide pointing new projects at the framework.
• Google shipped ADK 2.0, a new graph-based workflow runtime. Python 2.0 reached general availability on May 19, 2026, Go 2.0 on June 30, and TypeScript 2.0 on August 21. The upgrade moves ADK from a hierarchical agent executor to a graph execution engine where agents, tools, and functions run as nodes.
• PydanticAI hit stable 2.0 on June 23, 2026, with typed graph workflows, durable execution engines, and the companion Pydantic AI Harness for long-running work.
• The OpenAI Agents SDK expanded beyond its core primitives with sandbox agents that run specialists inside isolated execution environments, realtime and voice agent guides, and a broad session ecosystem spanning SQLite, Redis, MongoDB, and Dapr storage.
• LlamaIndex matured its agent layer, recommending FunctionAgent and AgentWorkflow (a multi-agent workflow) while its event-driven Workflows deploy as microservices through llama_deploy.
• CrewAI kept shipping steadily through version 1.15, with Flows — its event-driven orchestration layer with persisted state — now central to the recommended patterns.

The takeaway: in 2026, choosing between AI agent frameworks is less about demo capabilities and more about which operational guarantees each project gives you.

The Seven Frameworks Compared

The sections below reflect the current status of these AI agent frameworks as documented by their maintainers as of September 2026.

LangGraph

LangGraph describes itself as a low-level orchestration framework and runtime for building, managing, and deploying long-running, stateful agents. You model an agent as a graph, thread state through it step by step, and mix deterministic hand-coded steps with LLM-driven agentic steps in the same graph. 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 work can be interrupted, inspected, resumed, or rolled back.

The design is built around directness: among the AI agent frameworks profiled here, it gives you fine-grained control over how execution flows. It can be used standalone or paired with LangChain's model and tool components, and it ships for Python and JavaScript/TypeScript. It is MIT-licensed; the latest SDK release at the time of writing is 0.4.4 (August 2026).

What it's good at: long-running, stateful workflows; fine-grained control over execution flow; branching, subgraphs, and explicit human approval points.

Trade-offs: it's lower-level than the rest — you write more orchestration yourself, and the graph model has a real learning curve.

Who it fits: teams that need precise control over a complex process, durable long-running agents, or a runtime they can extend for unusual workflows.

CrewAI

Few of the AI agent frameworks here lean into the team metaphor the way CrewAI does. 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 persisted execution that can be resumed. According to its documentation, CrewAI ships with guardrails, memory, knowledge sources, and observability, and task processes can run sequentially or hierarchically.

It is a standalone framework — not built on LangChain — and MIT-licensed. The current release line is 1.15 (September 2026), and it runs on Python 3.10 and above.

What it's good at: multi-agent collaboration where named specialist roles make sense; moving from a prototype crew to a structured production pipeline.

Trade-offs: the role-and-task abstraction feels heavyweight for simple single-agent jobs, and some production features live in CrewAI's hosted enterprise platform rather than the core library.

Who it fits: teams building research pipelines, content workflows, and business-process automation with clearly defined roles.

LlamaIndex

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, indexes, retrievers, query engines, and document parsing. Its current agent layer documents several concrete options — FunctionAgent for tool-calling, AgentWorkflow for multi-agent systems, plus ReActAgent and CodeActAgent — and its event-driven Workflows combine agents, connectors, and tools into multi-step stateful processes.

Those workflows can be deployed as production microservices through llama_deploy, and the managed LlamaCloud adds LlamaParse for agentic document parsing and LlamaExtract for schema-driven extraction. It is MIT-licensed (release line 0.14 as of August 2026) and available in Python and TypeScript.

What it's good at: RAG and document-heavy applications; building agents that reason over your own data; extracting structured content from messy documents.

Trade-offs: if your application is pure agent orchestration with no meaningful data layer, its data focus is more than you need.

Who it fits: developers building search, assistants, and knowledge-work automation over documents and private data.

OpenAI Agents SDK

Among the AI agent frameworks here, the OpenAI Agents SDK keeps a deliberately small set of primitives: agents (an LLM with instructions and tools), tools, guardrails, and handoffs. It is the production-ready successor to OpenAI's earlier Swarm experiment. Orchestration works through handoffs — where a specialist agent takes over a turn — or agents-as-tools, where a manager agent calls specialists and keeps control. The SDK also documents sessions for persistent state across turns (backed by SQLite, Redis, MongoDB, Dapr, or encrypted storage), built-in tracing, MCP support, and human-in-the-loop features.

In 2026 it added sandbox agents that run specialists inside isolated environments (Docker or a local Unix sandbox) with resumable sessions, plus realtime and voice agent guides. It is Python-first with a TypeScript build, and although it is from OpenAI, the documentation shows provider flexibility: non-OpenAI models integrate through built-in provider hooks or adapters such as LiteLLM. It is MIT-licensed; current version 0.22.3 (September 2026).

What it's good at: rapid multi-agent applications with clean delegation; resumable sessions; sandboxed execution; observability out of the box.

Trade-offs: it is thinner on first-party data and retrieval tooling than LlamaIndex, and heavier orchestration patterns are written in code rather than driven by a workflow engine.

Who it fits: developers who want a small, modern agent runtime with guardrails and handoffs, and who are comfortable with OpenAI-centric documentation.

Google Agent Development Kit (ADK)

Google's Agent Development Kit is an open-source framework that Google positions for building, debugging, and deploying reliable AI agents at enterprise scale, available in Python, TypeScript, Go, Java, and Kotlin. ADK 2.0 introduced a graph-based workflow runtime: agents, tools, and functions run as nodes in a workflow graph, with explicit routing, code-based dynamic workflows for loops and branching, and collaborative workflows where a coordinator agent manages subagents. Google documents automatic retries and human-in-the-loop pauses as first-class parts of the 2.0 runtime.

Deployment is where the docs invest real attention — it is a natural fit for teams already on Google Cloud. Google documents local development through the agents CLI, and deployment to Cloud Run, Google Kubernetes Engine, and Vertex AI's managed agent runtime, alongside built-in tracing and evaluation tooling. The project is Apache 2.0 licensed; current version 2.9 (September 2026).

What it's good at: production agents on Google Cloud; multi-agent hierarchies; evaluation and observability as part of the framework rather than an add-on.

Trade-offs: the deepest integrations — the Vertex AI managed runtime and Gemini model features — pull you toward Google's ecosystem.

Who it fits: teams already on Google Cloud or Gemini who want an AI agent framework with first-class deployment and evaluation tooling.

Microsoft Agent Framework

Microsoft Agent Framework reached 1.0 general availability on April 2, 2026, and it is the only major AI agent framework that treats .NET as a first-class language — while also serving as the unified successor to Semantic Kernel and AutoGen. Microsoft describes it as combining AutoGen's agent abstractions with Semantic Kernel's enterprise features — session-based state management, type safety, middleware, and telemetry — and adding graph-based workflows. AutoGen is officially in maintenance mode (community-managed, no new features), and Microsoft publishes migration guides from both Semantic Kernel and AutoGen.

Its documentation covers agents, a batteries-included Harness agent for long multi-step tasks, functional and graph-based workflows with checkpointing, streaming, human-in-the-loop, and time-travel, plus OpenTelemetry observability and declarative YAML agents. It supports .NET (C#) and Python as first-class languages, with a separate Go SDK, and a range of providers from Microsoft Foundry and Azure OpenAI to OpenAI, Anthropic, and Ollama. It is MIT-licensed and shipping actively (1.22 for .NET, 1.19 for Python in September 2026).

What it's good at: enterprise agent applications on Microsoft and Azure; long-running workflows with state, checkpointing, and human approval; teams already invested in Semantic Kernel or AutoGen.

Trade-offs: it is younger than the projects it replaces, and while .NET teams gain a first-class option, pure Python teams without Microsoft dependencies have many equally mature alternatives.

Who it fits: organizations standardizing on the Microsoft stack and teams migrating existing Semantic Kernel or AutoGen applications.

PydanticAI

PydanticAI is the Python AI SDK from the team behind Pydantic, and it reached a stable 2.0 on June 23, 2026. Its defining feature is type safety: structured outputs, tool arguments, and dependency injection are validated against Pydantic models, so your type checker catches errors before runtime. It is model-agnostic — OpenAI, Anthropic, Google, Groq, and many others are swappable with a one-line model string — and it adds Pydantic Graph for typed graph workflows.

For production work it documents durable execution on engines including Temporal, DBOS, and Prefect (with several co-maintained integrations), human-in-the-loop via deferred tools that wait for approval, MCP support, evals, and observability through Logfire or any OpenTelemetry backend. The companion Pydantic AI Harness adds memory, guardrails, sub-agents, planning, and a sandboxed interpreter for model-written code. It is MIT-licensed; current version 2.46 (September 2026).

What it's good at: production Python applications where validated, typed output matters; structured data flowing into downstream systems; durable long-running agents.

Trade-offs: it is Python-only, and heavier batteries like memory and a full harness live in the separate Harness product.

Who it fits: Python teams who want type-safe, validated agents with strong observability.

Framework	Strong fit	Main strength	Languages	License
LangGraph	Complex, stateful workflows	Graph runtime, durable execution, human-in-the-loop	Python, JS/TS	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	Lean multi-agent apps	Guardrails, sessions, sandbox agents	Python-first + JS/TS	MIT
Google ADK	Production agents on Google Cloud	ADK 2.0 graph workflows + GCP deployment	Python, TS, Go, Java, Kotlin	Apache 2.0
Microsoft Agent Framework	Enterprise .NET/Python agents	SK + AutoGen unification, workflows	.NET, Python (Go SDK)	MIT
PydanticAI	Type-safe Python applications	Validated structured outputs, durable execution	Python	MIT

Every project 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, like state model, orchestration style, data needs, and deployment target, do not fit cleanly in a row.

How the Frameworks Differ Under the Hood

To read the docs, each framework sounds interchangeable; to the problems they solve, they are not. These five dimensions are where AI agent frameworks actually differ.

State and workflow control

State is where AI agent frameworks differ most. LangGraph persists graph state to a checkpointer at each step for interruption and resume. CrewAI Flows persist execution so long-running flows can be resumed. PydanticAI delegates durable execution to engines like Temporal, DBOS, or Prefect. Microsoft Agent Framework's workflows add checkpointing and time-travel over a workflow's history. Google ADK 2.0 manages session state inside its graph runtime, and the OpenAI Agents SDK ties state to sessions you can store in SQLite, Redis, MongoDB, Dapr, or encrypted storage. LlamaIndex's AgentWorkflow is, per its documentation, stateless between runs by default — you add persistence yourself.

Tool use and handoffs

Handoffs and agent-as-tool patterns are the main delegation mechanisms across AI agent frameworks. The OpenAI Agents SDK makes handoffs and agents-as-tools its two first-class orchestration patterns. LangGraph routes work through graph edges and subgraphs. CrewAI assigns tools to roles and lets agents delegate within a crew. LlamaIndex treats full query engines as tools, so an agent can use a RAG pipeline like any other function. PydanticAI validates tool arguments through Pydantic and can defer a tool call for human approval. ADK 2.0 runs tools as nodes in its workflow graph.

Every framework in this list can connect to external systems through the Model Context Protocol — if you are new to it, our MCP explainer covers how tool connections and their credentials work.

Human approval and guardrails

Human-in-the-loop shows up in every one of these AI agent frameworks, but with different mechanics. LangGraph interrupts let you inspect and modify agent state at any point. The OpenAI Agents SDK documents input, output, and tool guardrails plus human-in-the-loop flows. CrewAI supports guardrails, callbacks, and human-in-the-loop task triggers. ADK 2.0 includes automatic human-in-the-loop pauses and retry handling in the runtime. PydanticAI's deferred tools wait for explicit approval. Microsoft Agent Framework supports a don't-ask-again tool approval flow and human-in-the-loop in workflows.

Across AI agent frameworks, guardrails validate behavior; they are not a substitute for authorization. An output guardrail can block a pattern before it leaves the system, but deciding which tools an agent may call — and with what credentials — is still your job.

Data and retrieval

Only LlamaIndex is a data framework at its core, but every other option in this AI agent framework comparison integrates retrieval as a tool or service. LangGraph, CrewAI, the OpenAI Agents SDK, and PydanticAI can each call an existing retrieval pipeline, vector store, or search API as one tool among many. Microsoft Agent Framework and ADK document integrations with their cloud data services. The practical pattern in 2026: choose a retrieval stack you trust, then expose it to whatever framework you pick through a tool or MCP connection.

Observability and debugging

The OpenAI Agents SDK ships built-in tracing. Microsoft Agent Framework integrates OpenTelemetry for distributed tracing. PydanticAI documents evals plus observability via Logfire or any OpenTelemetry backend. ADK includes tracing and evaluation tooling. LangGraph integrates with LangSmith, LangChain's observability and evaluation platform. LlamaIndex has instrumentation and eval tooling across its stack. Tracing matters operationally because it is how you catch prompt injection, runaway loops, tool failures, and cost spikes in production — in practice, observability is the easiest capability to skip when comparing AI agent frameworks, and the hardest to bolt on after launch.

Real 2026 Development Scenarios

These patterns are deliberately framework-neutral, because most AI agent frameworks can express them. The four scenarios below are illustrative architectures — patterns you can build with the documented capabilities above, not reports of named production deployments.

1. Customer-support agent with approval gating. A support agent retrieves account information, drafts a response, and pauses for human approval before it is sent. In the OpenAI Agents SDK this maps directly to documented primitives: a tool that queries account state, an output guardrail, and a human-in-the-loop flow. In LangGraph it maps to an interrupt at the "send" node where a human inspects and approves state.

2. Coding workflow with specialist handoffs. A triage agent inspects a repository, then hands off to a specialist — one agent for refactoring, another for tests. CrewAI's hierarchical process models this as a manager with specialist agents; LangGraph models it as a subgraph or a routing edge; the OpenAI Agents SDK models it as a handoff from a triage agent.

3. Document-processing pipeline with exception routing. Invoices are parsed, field values are extracted against a schema, valid records are written to a data store, and anything that fails validation routes to a human inbox. This fits LlamaIndex's LlamaParse and extraction workflow combined with PydanticAI's typed output validation, or a MS Agent Framework workflow with a conditional edge to a human review step.

4. Long-running workflow that survives interruption. A multi-hour pipeline writes progress to a checkpointer after every step, so when infrastructure restarts, work resumes where it stopped instead of restarting. This is precisely the durable execution LangGraph's checkpointer, PydanticAI's Temporal/DBOS/Prefect integrations, and Microsoft Agent Framework's restartable workflows are built for.

Browser-based and tool-use systems are a fifth common pattern in 2026 — several frameworks document browser and computer-use tools for agents that operate web interfaces — and if that is your use case, our AI browser agents guide covers the landscape.

Do You Actually Need an Agent Framework?

No. A single model call with one tool, or a fixed pipeline you control entirely in code, does not need one of these AI agent frameworks. Many teams build agents by hand against a model API for a long time, and that is often the right call — the loop is simple, and you keep full control.

AI agent frameworks earn their place when the plumbing becomes the actual problem: multiple tools across providers, state that must survive crashes, approval gates, several agents that coordinate, or production observability. That is also the moment the AI agent framework choice commits you to its abstractions, so it is worth treating as a product decision rather than a popularity contest.

One cost reality: every AI agent framework on this list is open source, but open source is not free to operate. You still pay for model APIs, infrastructure, vector storage, observability, deployment, and the human time spent reviewing agent output and approvals. Budget for the running system, not just the license.

Security and Reliability Considerations

The AI agent frameworks above provide real building blocks. Verified from official documentation: the OpenAI Agents SDK documents input/output/tool guardrails, sandbox agents for isolated execution, and an encrypted session option. PydanticAI documents a sandboxed interpreter for model-written code and deferred tool approval. ADK 2.0 documents automatic retries and human-in-the-loop pauses. Microsoft Agent Framework documents middleware, OpenTelemetry, and checkpointing. LangGraph documents state checkpointing and human-in-the-loop interrupts. CrewAI documents guardrails and human-in-the-loop task triggers.

What no AI agent framework removes is your responsibility. Deployed agents connect models to tools, APIs, files, and external systems, so tool permissions, least privilege, secret management, and per-environment isolation stay in your hands. Model output can be untrusted — especially tool output and fetched web content — so prompt injection and malicious tool results must be handled with the same discipline as user input. Sandboxing where you run code the model wrote, validating both inputs and outputs, and logging everything are not optional extras; they are the security model.

We are not making product-specific security attestations here, and no certification claims are implied. All seven of these AI agent frameworks are open source and auditable — but auditable code is a starting point, not a security certification. Before choosing a framework for regulated or sensitive workloads, read its security documentation, review how it persists state, and confirm the deployment isolation options for your infrastructure. For a deeper look at the threat model, see our AI agent security guide.

How to Evaluate a Framework Before Committing

Run your shortlist of AI agent frameworks through these dimensions before choosing:

• Language and ecosystem — Python is universal here; TypeScript narrows it to LangGraph, LlamaIndex, and the OpenAI SDK; Go, Java, or Kotlin points at ADK; .NET points at Microsoft Agent Framework.
• Control model — do you want a graph (LangGraph, ADK 2.0, MS workflows), a crew (CrewAI), an event-driven flow (CrewAI Flows, LlamaIndex Workflows), or a simple loop with guardrails (OpenAI SDK, PydanticAI)?
• State and durability — can the system checkpoint and resume after a failure, and how is that state stored and secured?
• Human approval — does the framework give you clean mechanics for pausing before side effects, and can approvals be logged?
• Tools and MCP — how easily do your existing APIs, retrieval stacks, and external services become tools?
• Observability — built-in tracing versus bolt-on, and whether evals are supported.
• Deployment target — Google Cloud, Azure, your own containers, or serverless.
• Provider flexibility — how locked in you are to one model vendor.
• License and maintenance status — MIT vs Apache-2.0 matters less than whether a project is still shipping and where its roadmap is heading.
• Operational complexity — what you will run and pay for in production, from session storage to durable-execution infrastructure.

Then prototype the same small, representative task on your top two candidates. The framework's docs will not tell you which one your team can actually ship — the prototype will. And if you are choosing ready-made agent applications rather than building on a framework yourself — comparing Manus AI, Devin AI, and Genspark, or pairing coding agents like Claude Code and Cursor with research tools such as NotebookLM — that decision sits one layer above the framework layer.

Frequently Asked Questions

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 AI agent frameworks handle the agent loop, tool calling, state or memory, human oversight, and multi-agent orchestration so you don't build all that plumbing yourself.

Do you need a framework to build an AI agent?

Not necessarily. You can build a working agent loop by hand against any model API. A framework earns its place once you need multiple tools, persistent state, guardrails, durable execution across restarts, or several agents that coordinate. Start with a plain loop until the plumbing becomes the actual problem you are solving.

Which languages do the major AI agent frameworks support?

All seven AI agent frameworks have Python support. OpenAI Agents SDK, LangGraph, and LlamaIndex also ship TypeScript builds; Google ADK covers Python, TypeScript, Go, Java, and Kotlin; and Microsoft Agent Framework treats .NET (C#) and Python as first-class languages with a separate Go SDK. CrewAI and PydanticAI are Python-focused.

What is the difference between an agent framework and an agent runtime?

An agent framework gives you abstractions for building agents — loops, tools, state, and orchestration patterns. An agent runtime executes that code and adds operational guarantees such as durable execution, retries, checkpointing, and restart after failure. The two often ship together, and LangGraph and PydanticAI both describe themselves as runtimes, but the distinction matters when you evaluate production claims because abstractions alone do not give you resumable execution.

Are AI agent frameworks secure by default?

No framework makes your system secure on its own. They provide useful building blocks — guardrails, sandboxed execution, tool-approval prompts, and checkpointing — but the security of a deployed agent depends on how you configure tool permissions, secrets management, least privilege, input and output validation, and monitoring. Review each framework's security documentation and how it stores state before relying on it for sensitive workloads.

How should developers choose an AI agent framework?

Match the framework to your team's language, the control model that fits your workflow (graph, crew, event-driven flow, or a simple loop), your data and retrieval needs, the model providers you rely on, and the infrastructure you deploy to. Then prototype the same small task with your top two candidates before committing — the fastest way to learn the real trade-offs is to build.