Agent Frameworks 2026: Eve, Flue, LangGraph, CrewAI and Factory…

Problem

The question “which agent framework do we use?” sounds technical. In reality it often hides a more uncomfortable decision: which part of the operation you want to govern with agents.

In 2026 we no longer talk only about LangGraph or CrewAI. Proposals such as Eve, Flue, Microsoft Agent Framework, OpenAI Agents SDK, Vercel AI SDK and Factory appear. Some are frameworks. Others are harnesses. Others are platforms. Others are software factories disguised as products.

If you compare them as if they were equivalent, you choose wrong. Not because they are bad tools, but because they solve different layers.

Thesis

An agent framework is not chosen for hype. It is chosen for the control layer missing in your system.

The correct decision starts with an operational question:

do you need a durable runtime?
do you need a programmable harness?
do you need state orchestration?
do you need fast multi‑agent?
do you need enterprise integration?
do you need a software factory?
do you just need to set up a first case with guardrails?

The tool comes later.

Framework

Think of the agent market in five layers:

Layer	What it solves	Correct question
Light SDK	tools, handoffs, guardrails, tracing	who controls the app and the state
Runtime/orchestrator	state, memory, branching, long tasks	how a complex flow is executed
Harness	channels, workflows, policies, durable cycles	how you package the agent as a system
Platform	hosting, sandbox, permissions, deployment, observability	where the agent lives and operates
Factory	SDLC, QA, review, flow learning	how the complete software work scales

The common mistake is buying the upper layer to solve a deficiency of the lower layer. Or the reverse: mounting a low‑level framework when the real problem is deployment, permissions, and responsibility.

Quick map

Option	Primary layer	Best for	Main risk	Don’t use it if
Eve	durable framework/platform on Vercel	backend agents with filesystem, sandbox, approvals and subagents	beta and lock‑in to Vercel stack	you need total infrastructure neutrality
Flue	durable TypeScript harness	programmable agents with workflows, channels and policies	young ecosystem	you lack the technical capacity to maintain the runtime
LangGraph	stateful runtime/orchestrator	complex flows, memory, human‑in‑the‑loop and granular control	over‑engineering	you only need a simple business case
CrewAI	high‑level multi‑agent framework	crews, flows and operational prototypes with clear roles	role theater if there is no evaluation	you still don’t know which decision each agent should make
OpenAI Agents SDK	light SDK	apps that already control their stack and want tools, handoffs and guardrails	your team maintains the operation	you need a full out‑of‑the‑box platform
Microsoft Agent Framework	enterprise framework	Microsoft/.NET/Python/Azure teams that need MCP, A2A and AutoGen/Semantic Kernel continuity	enterprise ecosystem dependency	your stack doesn’t live near Microsoft
Vercel AI SDK	AI product SDK	interfaces, tool calling and web apps with simple agents	does not replace an agent operating system	you need a complex durable workflow
Factory	software platform/factory	scaling development, QA and agent‑native software delivery	not a generalist framework	you are looking to build any business agent

The table does not decide for you. Avoid comparing things that do not belong to the same category.

Why it matters now

Eve sends a clear signal: Vercel does not present it as another chatbot. It is oriented to durable backend agents, filesystem‑first, with sandbox, workflows, approvals, subagents and evals. In other words, it wants the agent to have a workplace, not just a conversation.

Flue pushes another reading: the agent as a programmable harness. Its proposal revolves around TypeScript, tasks, workflows, channels, policies and runtime. Cloudflare positions it as a layer that can rely on primitives of its Agents SDK. The important word is not “agent”; it is “harness”.

LangGraph remains strong when you need state control, memory, persistence and complex loops. It does not compete with a pretty landing page or a simple wrapper. It competes against the chaos of workflows that need to be traceable and resumable.

CrewAI has another place: accelerating the construction of crews and multi‑agent flows with a more opinionated layer. It is useful when the team understands roles, responsibilities and outputs. It is dangerous when it is only used to assign human names to processes that no one has designed.

OpenAI Agents SDK and Vercel AI SDK are lighter. They serve when your app already has architecture and you want to incorporate tools, handoffs, guardrails, streaming or model calls without buying an entire platform.

Microsoft Agent Framework carries weight for another reason: enterprise continuity. If your organization lives in .NET, Python, Azure, Microsoft 365, AutoGen or Semantic Kernel, the decision is not only technical. It is about integration, compliance and internal support.

Factory plays a different league. It should not enter as an “agent framework” at the same level. It is a bet on software factories: agents and systems that observe, test and improve the delivery chain. It helps understand where the market is heading, but does not replace a general SDK for building business agents.

Anti-example

“Let’s try Eve, Flue, LangGraph, CrewAI and Factory and see which wins.”

That benchmark measures nothing. Eve and Flue compete more on how to run durable agents. LangGraph competes on state control. CrewAI competes on speed of modeling crews. OpenAI Agents SDK competes on lightness within an app. Microsoft Agent Framework competes on enterprise fit. Factory competes on transforming software delivery.

If everyone enters the same table without distinguishing layer, the decision is contaminated from the start.

Protocol (3 steps)

Define the work that must survive the prompt. Which state, permissions, memory, artifacts and evidence must persist when the conversation ends.
Choose the missing layer. SDK, runtime, harness, platform or factory. Don’t buy a platform if only a tool loop is missing. Don’t mount a runtime if the problem is ownership.
Run a test with a real case. The case should include input, tool use, failure, retry, supervision, evidence and closure criteria.

Decision	Question	If the answer is yes
durable state	the agent must resume work days later	look at Eve, Flue or LangGraph
web/product UI	the user interacts in a own app	look at Vercel AI SDK or OpenAI Agents SDK
Microsoft enterprise	data, permissions and teams live in Microsoft	look at Microsoft Agent Framework
fast multi‑agent	you need roles and visible flows soon	look at CrewAI, but with evals
software delivery	the problem is development, QA and review	look at Factory as a platform
low‑level control	you need to govern each transition	look at LangGraph
safe production	you need sandbox, approvals and evidence	require guardrails before choosing a vendor

Long decision guide

Eve: when the agent needs a workplace

Eve is interesting because it starts from the filesystem. That changes the mental model. The agent not only responds; it creates, modifies, executes and leaves traces in a work environment. For teams already close to Vercel, the combination with Workflows, Sandbox, AI Gateway and Connect can greatly reduce the gap between demo and production.

Caution: it is in beta. I would not sell it as a mature standard for any organization. I would test it when the Vercel stack already exists and the case needs durable backend agents.

Flue: when you want a programmable harness

Flue fits if the team wants to write the agent’s behavior as software, not as product configuration. Tasks, workflows, channels, policies and runtime give a clear structure for systems that must operate beyond a single request.

Caution: it requires engineering judgment. If the team is looking for “something that does everything”, Flue does not eliminate the need to design operations. It orders them.

LangGraph: when state matters

LangGraph remains one of the most serious options when the problem is state: memory, checkpoints, branches, human‑in‑the‑loop, retries and flows that do not fit in a linear sequence.

Caution: you could end up building a nuclear plant to light a bulb. If the case does not need complex state, LangGraph may be overkill.

CrewAI: when you need to model crews and flows

CrewAI is attractive because it lowers the friction of building multi‑agent systems. Roles, crews, flows, memory, knowledge and guardrails help turn an intuition into a rapid prototype.

Caution: multi‑agent seduces. Assigning a “researcher”, a “planner” and a “reviewer” does not create governance. It only creates theater if there are no evaluations, owners and exit criteria.

OpenAI Agents SDK: when the app drives

OpenAI Agents SDK makes sense when you already know your application will be the main container. The framework helps with agents, handoffs, guardrails, tracing and tool use, but does not aim to replace your operational architecture.

Caution: if you need complex persistence, multi‑team policies, durable runtime and long operations, you will have to design it or rely on another layer.

Microsoft Agent Framework: when the enterprise already lives in Microsoft

Microsoft Agent Framework matters less for novelty and more for continuity. It brings together the path of AutoGen and Semantic Kernel, with .NET/Python support and an enterprise narrative around MCP, A2A and multiple providers.

Caution: it is a great option when the enterprise environment justifies it. For a small team outside the Microsoft ecosystem it may be more infrastructure than needed.

Factory: when the product is not the agent, it is the factory

Factory should not be on the same line as LangGraph or CrewAI. Its promise is to transform software delivery: agents, droids, QA, evidence and flow learning. It is a decision about the engineering operating model.

Caution: if you want to set up a support, sales or internal operations agent, Factory is not the first place you would look. If you want to redesign how software is produced, yes.

BRTHLS matrix for choosing

Before choosing a tool, fill out this matrix:

Criterion	Low	Medium	High
criticality	reversible output	affects internal workflow	affects client, money or compliance
duration	seconds	minutes/hours	days or asynchronous processes
state	stateless	short history	persistent memory and artifacts
permissions	one tool	several internal tools	sensitive data and real actions
supervision	final review	approvals per stage	invocable human control
evidence	basic logs	step traces	reproducible audit

Simple rule:

Low: Light SDK.
Medium: harness or runtime.
High: platform, sandbox, observability, evaluation and ownership before autonomy.

Sources consulted

Next step

If you are choosing an agent framework, you don’t need another demo. You need to decide which operation you want the agent to execute, what permissions it will have, how it is audited, where it stops and what evidence it leaves.

At BRTHLS we can build it with you: decision map, minimal architecture, first productive case, guardrails, evaluation and a comparative table adapted to your real stack. Start by contacting us and bring a short list of processes where there is already repetitive work, decision risk or handoffs that burn margin.

Translated from the Spanish original with AI assistance and reviewed for accuracy. Read the original in Spanish.

Agent Frameworks 2026: Eve, Flue, LangGraph, CrewAI and Factory Don't Solve the Same Thing

Key Takeaways