10 AI Agent Frameworks to Know in 2026: LangGraph, CrewAI and More

Agent frameworks multiplied fast. OpenAI released an Agents SDK, Google shipped ADK, Hugging Face put out Smolagents and the Pydantic team built a typed agent layer, all alongside the older names. Choosing between them is easier once you notice that each one bets on a single idea: how agents coordinate with one another.

The map: eight coordination models

• Graph-based: agents are nodes, transitions are edges. LangGraph does this.
• Role-based: agents act like job titles on a team. CrewAI.
• Conversational: agents sit in a group chat and take turns. AutoGen and AG2.
• Hierarchical: a parent agent hands work to children, like an org chart. Google ADK.
• Code-execution: the agent's action is writing and running Python. Smolagents.
• Handoff-based: one agent passes control to another on purpose. OpenAI Agents SDK.
• Type-safe loop: validation with Pydantic at each step. PydanticAI.
• Protocol-native: agents talk over A2A and MCP as a basic assumption. OpenAgents.

Semantic Kernel and the Claude Agent SDK don't fit neatly into this list and get their own sections below. With the list in mind, framework choice reduces to one question: which coordination model matches your problem?

1. LangGraph, the production default

LangGraph overtook CrewAI in GitHub stars during early 2026, helped by enterprise adoption. Its graph structure suits production needs such as audit trails and rollback points.

You define a directed graph explicitly. Agents, tools and checkpoints are nodes, and conditional edges decide what runs next. The framework keeps state between steps and uses reducer logic to merge updates that arrive concurrently. That makes it a strong fit where you need precise control over execution order, branching and recovery from errors. It reached 1.0 in late 2025, became the default runtime beneath LangChain's agents and supports both Python and JavaScript.

Persistence is built in as checkpointing with time travel: any earlier state can be replayed, which matters for debugging and for compliance. Pair it with LangSmith and you get some of the best tracing in this group.

The price is boilerplate. A basic ReAct agent might be around 40 lines in Smolagents and roughly 120 in LangGraph. You trade verbosity for control.

It suits production systems that need explicit state and auditability, workflows with loops, branches and conditional routing, agents that need human approval checkpoints and teams already invested in LangChain.

A minimal example defines a typed state, a function deciding whether to keep going after ten iterations, an agent node and a tools node, and edges that loop between them until the limit is reached.

from langgraph.graph import StateGraph, END
from typing import TypedDict


class AgentState(TypedDict):
    messages: list
    iteration_count: int

def should_continue(state: AgentState) -> str:
    if state["iteration_count"] >= 10:
        return "end"
    return "continue"

graph = StateGraph(AgentState)

graph.add_node("agent", run_agent)
graph.add_node("tools", run_tools)
graph.add_conditional_edges("agent", should_continue, {"continue": "tools", "end": END})
graph.add_edge("tools", "agent")

2. CrewAI, the quick start

CrewAI's role-based design lets a small team stand up a working multi-agent system within half an hour or so.

You describe each agent by role, backstory and goal, then gather them into a crew with tasks and let the framework work out coordination. The code reads almost like plain English. Declare a researcher, a writer and a reviewer, give them tasks, and CrewAI decides who does what and when. No graph theory involved.

The cost shows up in two places. Published 2026 comparisons suggest a moderate token overhead, in the neighborhood of 18% over an equivalent LangGraph build for a three-agent ticket-triage crew, though numbers like that depend on the workload. And when a five-agent pipeline fails, the abstraction can become opaque and hard to debug. State is simply task output passed along from one agent to the next.

It fits fast prototypes and MVPs, business automation that maps onto human team roles, teams without dedicated AI engineers and small organizations taking a first step into agent automation. A common story: teams prototype in CrewAI, then move to LangGraph once they need proper state handling and conditional routing.

from crewai import Agent, Task, Crew


researcher = Agent(role="Research Analyst", goal="Find key facts", backstory="Expert researcher")

writer = Agent(role="Content Writer", goal="Write clear summaries", backstory="Skilled communicator")

research_task = Task(description="Research AI agent frameworks", agent=researcher)

writing_task = Task(description="Write a summary of findings", agent=writer)

crew = Crew(agents=[researcher, writer], tasks=[research_task, writing_task])

result = crew.kickoff()

3. AutoGen and AG2, agents in conversation

In AutoGen, workflows are conversations. Agents join a group chat, talk over several turns and a selector picks the next speaker. Where LangGraph draws a graph of nodes and edges, AutoGen stages a discussion.

The first version, 0.2, popularized agents debating and refining answers through dialogue. The 0.4 rewrite, carried forward as AG2, moved to an event-driven core with async-first execution and pluggable orchestration strategies.

The catch is cost. Each turn is a full LLM call carrying the whole conversation so far, so four agents debating for five rounds means at least twenty calls. That's heavy for high-volume, real-time work such as customer support. It pays off in offline, quality-sensitive work where thoroughness beats speed. State lives in conversation history, in memory by default.

Good uses include research and analysis where agents argue and refine, code generation where one agent critiques another, anything that benefits from real deliberation and teams that also need .NET support, since AutoGen has cross-language hooks. AutoGen Studio adds a no-code interface for trying out multi-agent setups, handy for people who aren't engineers.

4. Google ADK, the delegation tree

Released in April 2025, ADK arranges agents in a tree. A root agent receives a task and hands subtasks down to children, which may have children of their own. Think org chart, not group chat.

It ties closely to Vertex AI, Gemini and Google Cloud, although other models work. Its standout feature is native support for A2A, the Agent-to-Agent protocol, so an ADK agent can find and call an agent written in LangGraph or CrewAI through A2A's standard task interface. Session state is handled with pluggable backends.

It's the natural pick for teams deep in Google Cloud, for systems that must coordinate agents across frameworks, for enterprises that rely on Google's compliance certifications and for workflows with a clear chain of delegation.

5. OpenAI Agents SDK, small and quick

Arriving in March 2025, the Agents SDK replaced the experimental Swarm project with something production-ready. Its core idea is the handoff: one agent finishes its part and explicitly passes control, plus conversation context, to another. Each agent is defined by instructions, a model, tools and the list of agents it may hand off to. The result is predictable and easy to read.

The limits are real. It's tied to OpenAI models, with no bring-your-own-model option. There's no built-in checkpointing for long-running jobs, control over agent-to-agent communication is thin and error handling is coarse. State is context variables that are ephemeral by default. The learning curve is gentle, thanks to a small opinionated API.

Choose it if you already run on OpenAI models, your flow is a simple series of handoffs without heavy state needs, or you want to prototype before committing to something larger.

from agents import Agent, handoff


support_agent = Agent(name="Support", instructions="Handle general questions")

billing_agent = Agent(name="Billing", instructions="Handle payment issues")

support_agent.tools = [handoff(billing_agent, "Transfer billing questions")]

6. Smolagents, code as the action

Hugging Face's library takes a stripped-down approach. Rather than elaborate prompts and orchestration, it runs a minimal loop where the agent writes and executes code to reach its goal.

Most agents choose a tool and arguments as JSON. Smolagents' CodeAgent writes Python directly, so any library becomes available as a tool once you authorize the import. It also leads on support for local models, being built by Hugging Face for its own ecosystem. For safety, execution can be sandboxed through Blaxel, E2B, Modal, Docker or a Pyodide-plus-Deno WebAssembly sandbox.

The weakness is its dependence on model quality. GPT-4o or Claude Sonnet class models produce solid code, whereas models under about 7B parameters introduce bugs consistently. That dependence is higher than in other frameworks.

It suits research and code-execution agents, data analysis pipelines that benefit from generated code, people already on the Hugging Face Hub and prototypes with local open-source models.

from smolagents import CodeAgent, InferenceClientModel, DuckDuckGoSearchTool

model = InferenceClientModel()

agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)

result = agent.run("Find the top 5 AI frameworks by GitHub stars and plot a bar chart")
# The agent writes and executes Python to search, process data, and generate the chart

7. PydanticAI, typed contracts

Pydantic AI carries Pydantic's type safety and developer experience into agent work, with a feel close to FastAPI. You declare inputs, tool signatures and outputs as Python types, and the framework validates them and adds OpenTelemetry instrumentation.

Its loop uses dependency injection at each step: say what goes in, say what comes out, and Pydantic enforces the contract. If your pipeline needs dependable, validated output formats, this gives Pydantic-grade guarantees on what the model returns. Anyone who has used Pydantic or FastAPI will find it familiar.

It fits applications with schema-validated outputs, as in finance, healthcare and legal work, teams that care about type safety and testability, Python developers who want explicit contracts on every interaction and, notably, use as the structured-output layer inside a larger orchestrator such as LangGraph.

8. Microsoft Semantic Kernel, the enterprise route

Semantic Kernel is plugin-based, with agents acting as orchestrated planners, and it's built to feel like ordinary enterprise software rather than a research prototype. It's lighter-weight than some rivals but integrates strongly with .NET.

It's the choice for teams on Azure, writing C# or Java and deploying inside Microsoft's compliance ecosystem. It's tuned mostly for Azure OpenAI but supports other providers. It fits enterprise .NET, C# and Java teams, Azure-first deployments, organizations already using Copilot Studio and workflows that must connect to Microsoft 365 and Office.

9. Claude Agent SDK, reliability first

Anthropic's SDK works as a tool-use chain with subagents. Claude decides which tools to use, chains them and can start isolated subagents for parallel work through Claude Code's agent infrastructure.

It stresses dependable error handling and safe behavior over raw speed, integrates natively with MCP for tool connections and can use Claude's extended thinking for hard reasoning. It runs only on Claude models. External state goes through MCP servers, and in-session state lives in Claude's context.

Consider it if you're already on Anthropic's platform, if safety guarantees and content-policy compliance matter, for long-context work that benefits from a 200K-token window and for projects tied into the wider MCP tool ecosystem.

10. OpenAgents, a network built on protocols

OpenAgents builds persistent agent networks where members communicate through open standards, MCP for tools and A2A between agents. A LangGraph agent, a CrewAI agent and a hand-rolled Python agent can join the same network. The point is interoperability across frameworks and organizations rather than one opinionated workflow.

It's younger, with a smaller community than AutoGen or LangGraph and fewer ready-made integrations than the LangChain ecosystem offers. It fits long-lived agent ecosystems that evolve, collaboration across organizations through A2A, teams that need agents from different frameworks to find each other and infrastructure work where open-protocol support is non-negotiable.

Picking one

Start from who is building.

• A solo developer on a first agent project: CrewAI or the OpenAI SDK.
• A Python team that values type safety: PydanticAI.
• A research team focused on code execution, or a data scientist in the Hugging Face world: Smolagents.
• An enterprise .NET or Azure team: Semantic Kernel.

Then from what is being built.

• A production system needing state management and auditability: LangGraph.
• A business workflow with clear role separation: CrewAI.
• A debate or code-review pipeline between agents: AutoGen or AG2.
• A Google Cloud deployment with Gemini: ADK.
• Something that must work across vendors' frameworks: OpenAgents.
• A safety-critical application: the Claude Agent SDK.

Then from what can't be compromised.

• Model-agnostic across OpenAI, Anthropic and local models: LangGraph, CrewAI, AutoGen or Smolagents.
• OpenAI only, fastest setup: the OpenAI Agents SDK.
• Gemini-optimized: ADK.
• Claude only, with the strongest safety posture: Anthropic's SDK.
• Validated, type-safe output: PydanticAI.
• Interoperability across frameworks: OpenAgents.

As a rough read on the market, LangGraph looks set to own the production and enterprise tier, Smolagents is likely to dominate the Hugging Face and research community, and CrewAI and AutoGen will compete for the accessible middle.

The real criterion

Choose the framework that fits your problem, your team and the stack you already have. Not the one with the most stars and not the newest one. Choose the one that makes your particular coordination problem simple to express and easy to debug. That has always been the whole job.