Agentic AI Frameworks in 2026: What to Use and What Changed

The agentic framework landscape changed materially in the last year, and the most important update is a subtraction.

Microsoft's AutoGen is in maintenance mode. Its GitHub README states it plainly: "AutoGen is now in maintenance mode. It will not receive new features or enhancements and is community managed going forward," and directs new users to Microsoft Agent Framework instead. Existing projects keep working — this is maintenance mode, not a shutdown — but any 2026 comparison still recommending AutoGen for greenfield work is out of date.

The rest of this covers what actually separates these frameworks (it isn't features), which control model fits which problem, and the honest case for using no framework at all.

The thing that actually differentiates them

Every framework gives you agents, tools, and memory. The real difference is how much control you have over the sequence — and that single axis predicts almost everything else.

The mistake teams make is picking on the demo, which always favors role-based and conversational models because they look impressive with five lines of code. Production tends to pull the other way — toward explicit graphs, because that's what gives you retry points, state you can inspect, and a transcript you can audit.

The main options

LangGraph models agents as a graph of nodes with explicit state. More upfront work than the alternatives, and the payoff is that you can see and control where the thing is. It's become the common answer for teams putting agents into production rather than demos.

CrewAI gives you role-based agents — a researcher, a writer, a reviewer — that collaborate on a task. The fastest way to get a multi-agent prototype running, and the abstraction that most often needs replacing when you need precise control.

OpenAI Agents SDK is the lightest path when OpenAI is your default provider. Minimal ceremony — and it isn't locked to OpenAI: the model docs cover pointing it at any OpenAI-compatible endpoint, with best-effort LiteLLM and Any-LLM routing for wider provider coverage.

Claude Agent SDK does the equivalent for Anthropic models, and inherits the tool-use and computer-use primitives directly.

Microsoft Agent Framework is where AutoGen users are being pointed, aimed at enterprise .NET and Azure estates.

Smolagents is deliberately tiny — agents that write and run code as their action space. Worth knowing because it makes the underlying loop obvious in a way the bigger frameworks hide.

A note on version-level comparisons: this category ships fast enough that specific release numbers go stale within weeks. Check the repository's own activity — last commit, open issue response, release cadence — rather than trusting any published comparison, including this one.

When you don't need a framework

This section is missing from most framework roundups, and it's the one that saves the most time.

An agent loop is genuinely simple:

1. Send the model the goal, the conversation so far, and a list of tools
2. The model returns either an answer or a tool call
3. If it's a tool call, run it, append the result, go to step 1
4. Stop on an answer, a step limit, or a spend limit

That's maybe eighty lines. Both major model APIs support tool calling natively, and for a single agent with a handful of tools, writing the loop yourself gives you complete visibility into what happened — which is exactly what you want the first time something goes wrong.

Frameworks earn their place when you need:

• Multiple agents with genuinely different roles coordinating on shared state
• Durable execution — pausing, resuming, surviving a restart mid-task
• Human-in-the-loop checkpoints with state that persists across the wait
• Swappable model providers behind one interface

If none of those apply, the framework is mostly a layer between you and the bug. Start with the loop, adopt a framework when you feel a specific absence.

Choosing without regretting it

Four questions, in order:

1. One agent or several? One agent with good tools solves more than people expect. Multi-agent adds coordination cost that's real and often unrewarded.
2. Prototype or production? Prototypes favor role-based speed; production favors explicit graphs.
3. Locked to one model provider? If yes, the vendor SDK is the shortest path. If no, you need the abstraction layer.
4. Does it need to survive a restart? This one quietly eliminates most options — durable execution is the hardest thing to add later.

The interoperability layer matters more than the framework choice long-term. The Model Context Protocol standardizes how agents reach tools and data, which means tool integrations built against it survive a framework migration. Prefer that boundary where you can.

For the architectural picture underneath all of these, agentic AI architecture covers the components, and ReAct agent covers the reasoning loop most of them implement.

Worth separating the developer question from the user one. If what you actually want is to run agent setups other people built rather than build your own, no framework helps — the blocker is environment setup. Taku mirrors a working AI setup into a desktop workspace and runs it there, so you can use a power user's configuration without reproducing their environment first. The free app library shows what's available to mirror. Taku is in Beta, and the Mac app is available now.

FAQ

What are agentic AI frameworks?

Libraries that provide the scaffolding for AI agents — the loop, tool calling, memory, and coordination between agents — so you don't rebuild it each time. They differ mainly in how much control you keep over the sequence.

Is AutoGen still maintained?

It's in maintenance mode. Microsoft's README says it will not receive new features and is community managed, and points new users to Microsoft Agent Framework. Existing deployments continue to work.

Which agentic AI framework is best?

Depends on the control model you need. Explicit graphs for production and auditability, role-based crews for fast prototyping, and vendor SDKs when you're committed to one model provider.

Do I need a framework to build an AI agent?

No. A single-agent loop with tool calling is about eighty lines against either major API. Frameworks pay off for multi-agent coordination, durable execution, human checkpoints, and provider portability.

How do I keep up when frameworks change this fast?

Check the repository directly — commit recency, release cadence, issue response — rather than trusting comparison articles. And build tool integrations against a standard like MCP so they survive a framework switch.

Key points

• AutoGen is in maintenance mode; Microsoft points new projects at Microsoft Agent Framework.
• Frameworks differ by control model, not feature list — that axis predicts production fit.
• Demos favor role-based crews; production tends to pull toward explicit graphs.
• Most single-agent tasks don't need a framework at all.
• Build tool integrations against MCP so they outlive your framework choice.