TL;DR: Mastra, LangChain, crewAI, Agno, Google ADK, and the Vercel AI SDK are six of the frameworks developers reach for most when building production AI agents. Mastra leads this list for TypeScript teams because typed tools, built-in memory, and workflows all ship in one framework. LangChain wins on integration breadth, crewAI and Google ADK on multi-agent orchestration, Agno on lightweight speed, and the Vercel AI SDK on shipping an agent inside an existing web app.

AI agents have moved past the single LLM call. The systems teams build now reason about a goal, call tools, hold on to what they learned along the way, and work through a task over many steps. Building that from scratch means solving a set of problems most teams would rather solve once, including how the agent remembers past steps, how it decides what tool to call next, how several agents coordinate, and how the whole thing gets deployed.

That's the job of an agent framework. Rather than catalog every agent library available today, this guide covers six frameworks that come up most often in production work. The list isn't exhaustive, but it spans the major architectural approaches teams are evaluating as agentic applications mature.

What is an AI agent framework?

An AI agent framework is a library that gives a large language model the scaffolding to act on its own. It wraps the model in a loop that reasons about a goal, decides which tools to call, carries memory across steps, and keeps going until there's a finished result rather than a single completion.

A raw call to an LLM API returns one response to one prompt. An agent framework wraps that call in state, tracking what the agent has already tried, which tools it can call, and how it knows the work is done. Depending on the framework, that might mean a single agent with tools and memory, a graph of steps with conditional branches, or a team of specialized agents that hand work off to each other.

How do you evaluate an AI agent framework?

Most frameworks can wire an LLM to a few tools and call it an agent. The differences show up once the application runs continuously in production, with real users and real failure modes. Five dimensions do most of the separating.

Orchestration model

Some frameworks give you one agent loop and stop there. Others build in graphs, planners, or teams of agents that delegate to each other. The right shape depends on whether your task is one reasoning loop or a pipeline of specialized roles.

Memory and state

Can the agent recall what happened three steps ago, or in a previous run entirely? Multi-step and multi-turn tasks need durable memory that outlives a single prompt window.

Developer experience

Low-level primitives maximize control. Higher-level abstractions cut boilerplate and cost you some flexibility in return. Where you land depends on how much of the loop your application needs to steer.

Tool-calling and integrations

How easy is it to give the agent a new capability, and how large is the ecosystem of existing integrations you can pull from before writing your own?

Production readiness

Observability, evaluation tooling, and a clear deployment story start to matter the moment an agent moves out of a notebook and in front of real users.

The 6 best AI agent frameworks

Mastra leads this list for its combination of typed tools, built-in memory, and a TypeScript-first developer experience. The other five each fit a different orchestration style or ecosystem, and all six have active open-source communities.

1. Mastra

Best for: Teams building production AI agents in TypeScript who want typed tools, memory, and workflows in one framework.

Mastra is a TypeScript framework for building AI agents, workflows, and tools with a unified interface across model providers. Agents are defined with instructions, a model, and a set of tools, each tool carrying a typed input and output schema so the agent's calls are validated at runtime against a real contract.

import { createTool } from '@mastra/core/tools';
import { z } from 'zod';

export const weatherTool = createTool({
  id: 'get-weather',
  description: 'Get current weather for a location',
  inputSchema: z.object({
    location: z.string().describe('City name'),
  }),
  outputSchema: z.object({
    location: z.string(),
    temperatureCelsius: z.number(),
    conditions: z.string(),
  }),
  execute: async ({ location }) => {
    return {
      location,
      temperatureCelsius: 21,
      conditions: 'sunny',
    };
  },
});

// src/mastra/agents/index.ts
import { Agent } from '@mastra/core/agent';
import { weatherTool } from '../tools/weather-tool';

export const weatherAgent = new Agent({
  id: 'weather-agent',
  name: 'Weather Agent',
  instructions: 'You answer questions about current weather using the weatherTool.',
  model: 'openai/gpt-5.5',
  tools: { weatherTool },
});

Mastra also bundles the pieces most teams end up building separately. Memory persists to a real datastore, workflows handle multi-step processes with branching and retries, evals score agent output, and a local dev server lets you exercise an agent before deploying it.

Why you might choose Mastra

• Typed tools with Zod schemas, so every agent tool call is validated against a real input and output contract
• Built-in memory with a pluggable storage backend, so an agent can carry state across a multi-step task or between separate runs
• Workflows for deterministic, multi-step processes that sit alongside the agent loop
• TypeScript-first, which matches how most production agent codebases are built today
• A local dev server (mastra dev) and Mastra Cloud for testing and deploying an agent without standing up your own infrastructure

Tradeoffs

Mastra is TypeScript-first, so teams standardized on Python will find a smaller ecosystem here than LangChain or crewAI offer today.

2. LangChain

Best for: Teams that want the largest ecosystem of integrations and are comfortable composing lower-level building blocks.

LangChain is one of the earliest and most widely adopted frameworks for building applications on top of LLMs. Its create_agent function builds a minimal, configurable agent harness from a model, a set of tools, a system prompt, and optional middleware. For workflows that mix deterministic and agentic steps, its sibling project LangGraph models an agent as a graph of nodes and edges, giving explicit control over branching, retries, and human-in-the-loop checkpoints.

from langchain.agents import create_agent

def get_weather(city: str) -> str:
    """Get weather for a given city."""
    return f"It's always sunny in {city}!"

agent = create_agent(
    model="openai:gpt-5.5",
    tools=[get_weather],
    system_prompt="You are a helpful assistant",
)

result = agent.invoke(
    {"messages": [{"role": "user", "content": "What's the weather in San Francisco?"}]}
)
print(result["messages"][-1].content_blocks)

Because LangChain has been around since the early days of the LLM app ecosystem, its catalog of pre-built integrations runs deeper than anything else here, covering vector stores, document loaders, model providers, and a long tail of community-contributed tools.

Why you might choose LangChain

• The largest integration ecosystem of any framework on this list, covering most vector stores, model providers, and data sources
• create_agent gives a minimal, composable harness you tailor with a system prompt, tools, and middleware
• LangGraph gives explicit, graph-based control over an agent's steps, branches, and retries for more advanced orchestration
• Large community, extensive documentation, and a long track record in production

Tradeoffs

The breadth of LangChain's abstractions is also its most common complaint. Newer frameworks trade some of that flexibility for a more opinionated, lower-boilerplate developer experience.

3. crewAI

Best for: Teams building multi-agent systems where specialized agents with distinct roles collaborate on a task.

crewAI is a framework built specifically around multi-agent collaboration. You define a crew of agents, giving each one a role, a goal, and a backstory that shapes how it reasons, and crewAI handles the delegation between them. For workflows that need more deterministic control, crewAI also offers Flows, an event-driven layer for orchestrating steps precisely, with agents invoked at specific points rather than reasoning freely throughout.

from crewai import Agent, Task, Crew

researcher = Agent(
    role="{topic} Senior Data Researcher",
    goal="Uncover cutting-edge developments in {topic}",
    backstory="You're a seasoned researcher who finds relevant information and presents it clearly.",
    verbose=True,
)

research_task = Task(
    description="Conduct thorough research about {topic}. Use web search to find recent, credible information.",
    expected_output="A markdown report with clear sections: key trends, notable tools, and implications.",
    agent=researcher,
    output_file="output/report.md",
)

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

result = crew.kickoff(inputs={"topic": "AI agent frameworks"})
print(result)

Freeform crews for open-ended collaboration and structured Flows for deterministic control make crewAI a natural fit for work that decomposes cleanly into specialized roles, where one agent researches, another writes, and a third reviews.

Why you might choose crewAI

• Purpose-built abstractions for role-based, multi-agent collaboration
• Flows add deterministic, event-driven control for the parts of a workflow that shouldn't be left to agent judgment
• A straightforward mental model of roles, goals, and backstories that maps onto how human teams already split work
• Active open-source community with a growing library of example crews

Tradeoffs

crewAI is optimized for multi-agent collaboration specifically. A task that only needs a single agent with a few tools may be simpler to build in a framework designed around one agent at a time.

4. Agno

Best for: Teams that want a lightweight, fast Python framework for single agents or small teams of agents.

Agno is a Python framework for building multi-modal agents with memory, knowledge retrieval, and reasoning built in, without a heavy layer of abstraction on top. Agents are model-agnostic, so switching providers is a configuration change rather than a rewrite, and Agno supports composing multiple agents into a Team when a task needs more than one specialized role.

from pathlib import Path
from agno.agent import Agent
from agno.tools.workspace import Workspace

folder = Path(__file__).parent

sorting_hat = Agent(
    name="Sorting Hat",
    model="openai:gpt-5.5",
    tools=[Workspace(root=str(folder), allowed=["read", "list", "search"])],
    instructions=(
        "Walk the folder, figure out what's there, and propose a clean organization. "
        "Decide the categories yourself. Return a tidy summary, a category breakdown, "
        "and a folder tree."
    ),
)

sorting_hat.print_response("Organize this folder", stream=True)

Agno's design goal is speed and simplicity. Instantiating an agent should take microseconds, and the framework avoids the deep abstraction stacks that make some agent libraries slower to learn and debug.

Why you might choose Agno

• Lightweight core with fast agent instantiation, useful for latency-sensitive or high-throughput agent workloads
• Model-agnostic by design, so swapping providers rarely requires touching agent logic
• Built-in memory, knowledge retrieval, and reasoning without needing extra packages
• Supports both single agents and small multi-agent Teams in the same framework

Tradeoffs

Agno's ecosystem and community are smaller than LangChain's or crewAI's, so less-common integrations may need to be written by hand.

5. Google ADK

Best for: Teams building hierarchical multi-agent systems who want a model-agnostic framework with a clear path to production deployment.

The Google Agent Development Kit (ADK) is an open-source framework for building and orchestrating agents. It's optimized for Gemini but supports other model providers, and it deploys to Vertex AI Agent Engine, Cloud Run, or your own infrastructure.

from google.adk.agents.llm_agent import Agent

# Mock tool implementation
def get_current_time(city: str) -> dict:
    """Returns the current time in a specified city."""
    return {
        "status": "success",
        "city": city,
        "time": "10:30 AM",
    }

root_agent = Agent(
    model='gemini-flash-latest',
    name='root_agent',
    description="Tells the current time in a specified city.",
    instruction="Use the get_current_time tool to answer questions about the time in a city.",
    tools=[get_current_time],
)

ADK's strength is composing agents hierarchically. A root agent delegates to sub-agents, each with its own tools and instructions, which suits tasks that break down into a coordinator plus specialists. It ships with a built-in tool ecosystem and evaluation tooling for testing agent behavior before you release it.

Why you might choose Google ADK

• Native support for hierarchical multi-agent composition, a root agent coordinating specialized sub-agents
• Model-agnostic despite Gemini optimization, so you aren't locked into a single provider
• Built-in evaluation tooling for testing agent behavior systematically across a suite of cases
• A clear deployment path to Vertex AI Agent Engine or Cloud Run for teams already on Google Cloud

Tradeoffs

ADK's deepest tooling and fastest inference path assume Gemini and Google Cloud. Teams standardized on other providers or clouds can still use it, with a bit more setup.

6. Vercel AI SDK

Best for: TypeScript teams building agentic features inside a web application who want a unified API across model providers.

The Vercel AI SDK is a TypeScript toolkit for building AI-powered applications, with a unified API across model providers, streaming responses, structured generation, and tool calling. Its ToolLoopAgent class wraps the tool-calling loop so a model can decide which tools to call and when to stop, without you hand-rolling the loop.

import { ToolLoopAgent, tool } from 'ai';
import { z } from 'zod';

const weatherAgent = new ToolLoopAgent({
  model: 'xai/grok-4.5',
  tools: {
    weather: tool({
      description: 'Get the weather in a location (in Fahrenheit)',
      inputSchema: z.object({
        location: z.string().describe('The location to get the weather for'),
      }),
      execute: async ({ location }) => ({
        location,
        temperature: 72 + Math.floor(Math.random() * 21) - 10,
      }),
    }),
    convertFahrenheitToCelsius: tool({
      description: 'Convert temperature from Fahrenheit to Celsius',
      inputSchema: z.object({
        temperature: z.number().describe('Temperature in Fahrenheit'),
      }),
      execute: async ({ temperature }) => {
        const celsius = Math.round((temperature - 32) * (5 / 9));
        return { celsius };
      },
    }),
  },
});

const result = await weatherAgent.generate({
  prompt: 'What is the weather in San Francisco in celsius?',
});

console.log(result.text); // agent's final answer
console.log(result.steps); // steps taken by the agent

The SDK is provider-agnostic and framework-native to Next.js and other Vercel-deployed apps, which makes it a natural fit for teams that want an agent as one feature inside a larger product rather than a dedicated standalone agent service.

Why you might choose the Vercel AI SDK

• A unified API across OpenAI, Anthropic, Google, and other model providers, so switching models is a config change
• First-class streaming and structured generation for typed, reliable output
• ToolLoopAgent handles the model-and-tools loop, context management, and stopping conditions
• Deploys naturally as part of a Next.js or other Vercel-hosted application, with no separate agent infrastructure to run

Tradeoffs

The Vercel AI SDK is a toolkit for a single agent loop rather than a dedicated multi-agent orchestration framework. Complex systems with delegation and hierarchy are more naturally expressed in crewAI or Google ADK.

How do these 6 frameworks compare?

Mastra and the Vercel AI SDK lead on TypeScript-native tooling, LangChain leads on ecosystem breadth, and crewAI and Google ADK lead on multi-agent orchestration.

Which AI agent framework should you choose?

Choose Mastra if you're building in TypeScript and want typed tools, memory, and workflows in one integrated framework.

Choose LangChain if you want the largest integration ecosystem and explicit, graph-based control over an agent's steps via LangGraph.

Choose crewAI if your task naturally decomposes into specialized roles that need to collaborate, with the option to add deterministic control through Flows.

Choose Agno if you want a lightweight, fast Python framework for single agents or small teams, without a heavy abstraction layer.

Choose Google ADK if you're building hierarchical multi-agent systems and want a model-agnostic framework with a clear production deployment path.

Choose the Vercel AI SDK if you're adding agentic features inside an existing TypeScript web application and want a unified API across model providers.

What is an AI agent framework?

An AI agent framework is a library that gives an LLM the scaffolding to act on its own. It provides the loop that lets a model reason about a goal, call tools, carry memory across steps, and work its way to a finished result.

Why can't I just use a single LLM call?

A single call returns one response to one prompt, with no memory of what came before and no way to take an action and observe the result. Agent frameworks add the loop, memory, and tool-calling that let a model work through a multi-step task on its own.

What is the difference between an agent framework and a model provider?

A model provider (OpenAI, Anthropic, Google) supplies the underlying LLM. An agent framework sits on top of one or more model providers and adds the orchestration around them, including memory, tool calling, multi-step reasoning, and in some cases multi-agent coordination.

Which framework is best for production AI agents?

That depends on your language and orchestration needs. TypeScript teams that want typed tools and memory built in tend to land on Mastra or the Vercel AI SDK. Python teams building multi-agent systems usually start with crewAI or Google ADK, and teams that care most about integration breadth pick LangChain.

Can I combine multiple agent frameworks?

Yes. Many teams use more than one framework across different services, or use a framework for orchestration alongside a separate tool library for a specific capability.

When should I add a framework instead of building from scratch?

Once an agent needs more than a couple of tool calls and a single reasoning pass, a framework usually pays for itself by handling the memory, retries, and tool-call validation you would otherwise build and maintain yourself.

Can I switch agent frameworks later?

Usually, yes, though the amount of work depends on how tightly your application logic is coupled to a specific framework's abstractions. Keeping tool definitions and business logic separate from the orchestration layer makes a future migration easier.

Start with whichever framework matches your language and orchestration needs, build a small end-to-end agent, and see how it holds up against a real task before you commit further.

Last updated July 2026.