Every Agent Frameworks comparison and buyer's guide for building AI agents — 47 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
Every checkpoint a long-running LangGraph agent writes re-serializes its entire state. DeltaChannel, per-node timeouts, and the v2 stream in 1.1–1.2 are the runtime quietly admitting the naive durability model doesn't scale.
CrewAI 1.14 lets you swap the default memory, knowledge, RAG, and flow backends for your own. It reads like a config change. It's actually the framework conceding that batteries-included storage was a production liability.
The rename reads like marketing housekeeping. It isn't. Folding deploy into LangSmith and handing every deployed agent an MCP endpoint quietly reclassifies your agent from an application into a tool other agents can call.
CrewAI ships two orchestration models in one framework. Picking wrong is why your multi-agent demo worked and your production run didn't — and the fix is usually not choosing between them.
Independent 2026 benchmarks running the identical task on the identical model find the framework alone can double or triple the token bill. The number you can't see on the invoice is the one the framework spends on your behalf.
The usual framing is 'simple handoffs vs powerful graphs.' That's the wrong axis. One framework asks who is in charge right now; the other asks what shape the computation has — and they fail from opposite directions as you scale.
Claude Code proved the 'deep agent' pattern — planning, a filesystem, sub-agents, skills. A small cluster of Python repos now rebuilds that harness on Pydantic AI, so it runs on any model you own.
Alibaba's AgentScope hit 2.0 and calls itself production-ready; LangGraph has owned that word for a year. They converge on the same job from opposite origins — and the real choice is which failure you're more afraid of.
The headline in AI SDK 7 isn't a new agent class. It's that durability and human approval stopped being things you bolt on and became primitives — at the cost of an ESM-only, Node 22+ upgrade.
Code-execution agents always ran into the same wall — running model-written code safely is expensive. Hyperlight's sub-2ms micro-VM moves that wall, and changes what the pattern costs.
V2 went stable on June 23 after seven betas, then shipped four releases in nine days. The real news isn't the version bump — it's a bet that the winning agent abstraction is a harness, not a graph.
Most coding agents open with a ~10,000-token system prompt. Pi opens with under 1,000 and lets the model write its own tools. The bet underneath: the model already knows how to be an agent, and every instruction token is a task token you don't get back.
Every multi-agent framework now has a handoff primitive, and they all look the same in the demo. The difference that bites you in production is what rides along when one agent passes the baton to the next.
With ADK 2.0's GA, LangGraph, OpenAI's Agents SDK, Google's ADK, and Microsoft's Agent Framework all now run on a graph execution engine. The programming model war is over. It settled the easy question.
Microsoft and Google both now let you define an agent in YAML instead of code. The split isn't about simplicity — it's about whether your agent's logic lives in its wiring or in its decisions.
Both shipped the same six production features in 2026. The choice isn't capabilities — it's which half of your agent you're willing to lock to a vendor.
After a year of churn that made it a punchline, LangChain shipped a 1.0 whose headline feature is the thing frameworks never promise: that it will stop moving under you.
Vercel's new agent framework treats an agent as a directory of files. LangGraph hands you a portable graph. The decision isn't the loop they run — it's who owns the production stack wrapped around it.
Microsoft stopped shipping orchestration patterns and started shipping the runtime underneath them. The three Build 2026 launches are all below the framework — and one of them quietly retires the JSON tool-call loop.
Deep Agents isn't a fourth framework competing with LangChain and LangGraph — it's a preset of LangChain middleware on the same runtime. The choice is how much opinion you want pre-assembled.
Nous Research's Hermes is the agent everyone's calling self-improving. It is — but the part that improves isn't the model. It's the harness writing its own skills.
Prompt engineering tuned the words. Context engineering managed the window. The discipline that decides whether an agent ships is the deterministic code around the model — and it is older than it looks.
They ship the same orchestration patterns now, so stop comparing them on patterns. The real fork is where your production agent actually runs — in code you hold, or in a cloud you rent.
AWS's Strands lets the model plan its own path; LangGraph makes you draw the path first. The choice isn't graph versus no-graph — it's how much you trust the model to drive.
Both Java AI frameworks hit 1.0 the same week and both now do RAG, tools, MCP, and observability. The real choice isn't features — it's where your app's center of gravity already sits.
One framework makes you draw the control-flow graph up front; the other lets it emerge from events. Pick by whether your hardest requirement is durable recovery or flexible composition.
Google's Genkit is the framework that bundles the parts the others sell separately. The real choice isn't features — it's where your code runs and how much of your ops you want the framework to own.
They both promise durable, resumable agents — but one is a place to run code and the other is a way to structure it. Confusing the two is how teams end up with neither.
OpenAI shipped a drag-and-drop agent canvas in October, then posted its deprecation notice eight months later. The part that survived tells you which layer to build on.
Most teams assume LangGraph's checkpointer already makes their agents crash-proof. It doesn't — and the gap is architectural, not a missing setting. Here's exactly where it ends and where Temporal begins.
LangChain 1.0 reduced the agent to two lines and moved everything interesting into hooks. The quiet consequence: supervisor, swarm, and reflection stop being architectures and become middleware you stack.
Both will run the same agent. The real difference is altitude — ADK hands you an org chart of agents, LangGraph hands you the wiring and a roll of tape.
One hands you a finished application to configure; the other hands you parts to assemble. The choice isn't easy-vs-powerful — it's whether your product's hard part lives where the platform already decided.
Both let you wire an agent as nodes and edges, so they look like the same tool with different syntax. The real split is what each one lets you prove about the thing before it runs.
They share a name, a history, and a lot of code — but by 2026 'AutoGen' splintered into three projects, and the one you pip install decides whose roadmap you inherit.
The lightweight, type-first agent frameworks have arrived — and they quietly disagree about how much of your stack a framework should own. Pick on that, not on syntax.
Microsoft just deprecated its two most-starred agent frameworks to ship a third. If you're choosing today, the decision is already made for you — here's why, and where it still loses.
Since the 1.0 release, LangChain's agent helper runs on LangGraph's engine — so the real question isn't which to pick, but which layer of the same stack to write against.
All three converged on the same runtime shape, so the old 'which can build an agent' question is dead. What's left is a bet on which layer each treats as first-class — and one differentiator nobody can copy.
All three build Python agents, but they disagree on one thing — who owns the loop. That contract, not the benchmark, is what you live with for years.
The frameworks that get the most attention disagree on something basic — what an agent's action even is. One writes code, one wires a graph, one casts a team.
The second wave of agent frameworks is leaner, typed, and vendor-backed — and underneath the branding, they're quietly converging on the same idea.
The three names a JavaScript team keeps hitting when it tries to build an agent aren't competing for the same job. Two of them stack on top of the third.
One hands you Anthropic's production agent loop already wired up; the other hands you a blank graph and a state machine. The choice is less "which framework" than "how much of the loop do you want to own."
All three give you a drag-and-drop canvas for building AI agents. The choice that actually matters is hidden underneath: what each one thinks it's automating, and whether its license lets you ship it.
They started on opposite ends — one indexed your documents, one chained your calls. In 2026 they've converged. The real choice is which abstraction you want to debug at 3am.
All three claim to build multi-agent systems. The real question isn't features — it's who owns the control flow, and the answer changes which one is the right call.
Not buyer's guides — the news, teardowns, and explainers behind this topic.