AI agent frameworks in 2026: which to use, and why it's a decision you live with
Pick the wrong one and you don't just swap a library. You rewrite your orchestration, your state handling and your evaluation harness — usually six months in, usually under deadline. This page gives you the current map, the part of the stack that actually decides whether you ship, and an honest answer on whether you should build it yourself at all.
Which of these is actually your problem?
Six ways a framework decision goes wrong. Find yours — the fix is different for each, and none of them is “read another comparison post”.
Recognize yours in that list? Good — every one of them has a name and a fix, and none of the fixes is “go read another comparison post”.
“We picked one and now we’re stuck”
Six months in, the framework fights the workflow instead of carrying it.
The fix: pull your logic out of its classes first — that alone makes the move cheap. See what a rewrite costs.
“The prototype works, production doesn’t”
It demos beautifully and falls over the moment real traffic and real edge cases arrive.
The fix: almost always the missing evaluation layer, not the framework. That is the 64% blocker.
“Nobody can tell us which one to use”
Every comparison post recommends whatever the author sells, and half are out of date.
The fix: four questions, in order. See how to choose in ten minutes.
“We’re on AutoGen or Semantic Kernel”
Both went quiet in April and nobody told you.
The fix: stop new work there, plan the move on your schedule. See the migration path.
“It works but we can’t prove it”
Someone asks how you know the agent is right, and there is no good answer.
The fix: evaluation and observability as a line item, not an afterthought — it is 25% of our build cost for a reason.
“Which model do we even run on it?”
The framework question is settled and the model question is wide open — and the bill scales with it.
The fix: see the model and cost comparison below.
What is actually inside the AI agent frameworks you are choosing between?
All AI agent frameworks are six parts in a trench coat. Once you can name them, the marketing stops working on you — because you stop comparing brands and start comparing which of the six a framework does well, and which it quietly hands back to you.
AI agent frameworks bundle six moving parts into one package: a reasoning core, a control model, state and memory, a tool surface, human checkpoints and tracing. Five of those six stay swappable later. One does not, and that is the one worth choosing on.
THE LOOP YOUR FRAMEWORK IS ACTUALLY RUNNING
PLAN
Decides the next step from the goal and what it knows
CALL
Reaches into your systems through the tool surface
CHECK
Validates the result before anything is committed
REMEMBER
Writes state forward so step nine still knows step two
Approval gates sit between CHECK and CALL wherever you want them · every step replayable
Illustrative — the loop every agent framework orchestrates, not a specific client’s system.
The reasoning core
Where the model thinks. Nearly every framework treats this as pluggable — which is why “which framework” and “which model” are two separate decisions, and why anyone bundling them is selling you something.
The control model
Graph, role, or loop — the agent orchestration model. This is the one part you cannot change later without a rewrite, and it is the reason a framework choice outlives the project that made it.
State and memory
What the agent still knows on step nine. Thin memory is invisible in a demo and fatal in production, because demos are three steps long.
The tool surface
How the agent reaches your systems. Since MCP, this is the part most likely to survive a framework switch — build it as a server, not as glue.
Human checkpoints
Where a person approves before money moves. Frameworks differ enormously here and almost nobody scores them on it.
Observability
Whether you can see why it did that. The layer teams skip in month one and rebuild in month four, usually during an incident.
Five of the six are replaceable. Pick for the one that is not.
AI agent frameworks, SDKs, runtimes or platforms — which are you shopping for?
People shopping for AI agent frameworks use four words as if they mean one thing. They do not. Buying the wrong category is a more expensive mistake than picking the wrong brand inside the right one, because you cannot fix it by switching vendors.
| What it is | What you get | What you still own | Pick it when |
|---|---|---|---|
| Library | Open source AI agent framework building blocks, no opinion | Everything | You have strong engineers and a strange problem |
| Framework | An opinion about control flow | Hosting, evaluation, on-call | The opinion matches your workflow |
| Runtime / harness | Somewhere for it to actually live | The agent logic itself | Orchestration is solved but operations are not |
| Platform | The whole thing, hosted | Your data — and the exit risk | Speed matters more than control |
Decide the category first. The brand argument is downstream of it.
What changed in April 2026, and why are the popular lists wrong?
Two frameworks that almost every 2026 comparison still recommends stopped being actively developed. If your shortlist came from one of those posts, it is already stale.
“Choose between AutoGen and Semantic Kernel”
Presented as two live, competing options you weigh against LangGraph and CrewAI — exactly as they were in 2025.
Both moved to maintenance mode on 3 April 2026
Consolidated into Microsoft Agent Framework 1.0 — .NET and Python, native MCP and A2A, long-term support.
Pick from a stale list and you inherit a framework nobody is steering. That is what you are actually gambling on.
Get the wording right — most write-ups don’t
Not “merged”
Nothing was folded together. A new SDK was built and the capabilities were consolidated into it.
Not “deprecated”
Microsoft has not formally deprecated either project. Your code keeps running.
Maintenance mode
Still there, still working — just no longer where the roadmap goes (InfoWorld reads it the same way).
Already on one of them? You are not in trouble.
Nothing broke. Production systems on both are still running. The honest advice is simply: stop starting new work there, and move on your schedule rather than during an incident. Here is the order that keeps it cheap.
Freeze new work
Stop adding features on the old runtime. Every feature you add there is a feature you migrate twice.
Lift your logic out
Move business logic out of the framework’s classes into plain functions you own. Usually a week — and worth doing even if you never migrate.
Run both, prove parity
Stand the new framework up beside the old one and run both against the same evaluation set, so you can show behavior did not drift.
Cut over, one at a time
Switch one workflow, watch it, then the next. Never a big-bang weekend.
Skipping step two is what makes step four expensive. Almost every painful migration we read about skipped step two.
Most single-agent moves land inside the range in the cost table below. Larger multi-agent estates take longer, though rarely for the reason people expect: permissions and approvals eat more calendar than code does.
Does the framework you choose decide whether the project succeeds?
Mostly, no. Framework choice decides how painful your second year is. What kills projects in the first year sits one layer above the framework, and almost nobody comparing LangGraph to CrewAI mentions it.
Roughly 88% of agent pilots never graduate to production, according to 2026 Forrester and Anaconda data. Here is what the teams themselves blamed:
Read that list again. Not one of those is an orchestration problem. You can build all three on any framework on this page — and skip all three on any framework too, which is exactly what a demo-quality prototype does before it dies in review.
The framework gets the blame. The missing evaluation layer did the killing.
So here is the thing nobody selling you a framework will say: proving the agent is right is the expensive part, and it is the part that decides if you ship. When we quote a build, that layer is a line item, not an afterthought. You will see it in the worked example further down.
Which AI agent frameworks actually matter in 2026?
Twelve are worth knowing. Most teams will pick from four. Each row says who it is genuinely best for — not which is "best", because that question has no answer without your workflow.
Four names actually matter here. The rest of the list you have been handed is noise, and ignoring it costs you nothing.
| Framework | Orchestration model | Best for | Status |
|---|---|---|---|
| LangGraph | Graph — nodes and edges, durable state | Stateful production pipelines that need to survive restarts and human approval | Active |
| CrewAI | Role-based crews and tasks | Fast prototypes of team-shaped work; the lowest barrier to a first result | Active |
| Microsoft Agent Framework | Unified workflows, native MCP + A2A | .NET shops and Azure-committed enterprises; the successor path from SK/AutoGen | GA Apr 2026 |
| OpenAI Agents SDK | Explicit handoffs between agents | Teams already committed to the OpenAI ecosystem wanting minimal glue | Active |
| Claude Agent SDK | Tool-use loop with long context | Long-running research and document work with heavy tool calling | Active |
| Google ADK | Agent Development Kit, A2A-native | Google Cloud / Vertex estates and agent-to-agent topologies | Active |
| Pydantic AI | Type-safe, validation-first | Python teams who want structured outputs they can actually trust | Active |
| LangChain DeepAgents | Planning + sub-agents over LangChain | Longer autonomous tasks inside an existing LangChain codebase | Active |
| Strands Agents | Model-driven loop | AWS-leaning teams wanting a light, opinionated runtime | Active |
| Mastra / Vercel AI SDK | TypeScript-first, app-embedded | Product teams shipping agents inside a web app, not a backend service | Active |
| Agno / Smolagents | Minimal, code-first loops | Small scopes and learning; deliberately little abstraction | Active |
| Superseded by Agent Framework 1.0 | No new builds — migrate on your own schedule | Maintenance |
Status verified 11 Aug 2026. We re-check this table monthly, because it has already changed once this year.
LangGraph vs CrewAI — which should you pick?
Pick LangGraph when the work has state worth protecting. Pick CrewAI when the work is a small team of roles passing a task along and you want something running this week.
Neither one is better. One is better for the shape of your work — which makes this a question about your process, not about code.
Choose LangGraph when…
- the process has real steps that must survive a crash or a restart
- a human has to approve something in the middle
- you need to replay exactly what happened, later, under scrutiny
- branches and loops matter — retries, escalation, conditional paths
The cost: more upfront design. You will think about state before you write much agent code.
Choose CrewAI when…
- the job splits naturally into roles — researcher, writer, reviewer
- you want a working prototype in days, not two weeks
- the run is short and a failure just means running it again
- you are validating whether the idea is worth building properly
The cost: less control when you outgrow it. Plenty of teams prototype here and rebuild on LangGraph.
And that last sentence is not a criticism. Prototyping in CrewAI and moving to LangGraph is a legitimate, cheap path — as long as you decide it deliberately instead of discovering it in month six.
Do the benchmark numbers actually change which one you should pick?
Someone finally benchmarked AI agent frameworks instead of asserting. The results are worth reading — and worth putting in proportion, because the spread they found is real but it is not where your money goes.
Mostly no. Measured framework overhead differs by roughly a tenth on the same task, which is a rounding error next to the model tokens your prompts and retrieved context actually consume. Use them to rule out the genuinely heavy option, not to rank the top three AI agent frameworks.
Figures published by AgentMail’s framework benchmark (8 July 2026), which installed and ran each framework on the same task. We did not run this test; we are citing theirs and adding the reading they leave out.
Why an 11% token gap is not your problem
Run the arithmetic on a real workload before you optimize for it. At a million agent turns a month, eleven percent of the orchestration overhead disappears next to the context you are actually paying for. Teams tune the 11% because it is measurable, and leave the far larger number sitting in an unpruned context window.
What no benchmark can measure
No harness can time the thing that kills projects: whether you can tell why the agent did that, six weeks after the engineer who built it moved teams. Install weight is measurable. Debuggability is not — and debuggability is the one that decides.
Measure what you can. Just do not mistake it for what matters.
How much should MCP and A2A support matter to your choice?
Enough to be a tie-breaker. MCP standardizes how an agent reaches tools and data; A2A standardizes how agents talk to each other. A framework with neither will cost you adapter code you did not plan for.
Protocols are cheap to add now and expensive to retrofit later. It is a fair argument, and it holds up.
MCP — vertical (agent → tools & data)
The connective layer between your agent and everything it needs to read or do. Adoption has passed 9,400 public servers, with 50+ enterprise partners implementing it through 2026. If your agent touches internal systems, this is the plug you want it to speak.
A2A — horizontal (agent → agent)
How separate agents — sometimes from different vendors — hand work to each other. In production at 150+ organizations as of April 2026. Matters most if you expect to coordinate agents you do not own.
They are complementary, not competing. Microsoft Agent Framework and Google ADK ship both natively; LangGraph and CrewAI integrate MCP well. Best for: if your agent lives inside one company and calls internal tools, MCP is the one to care about. A2A becomes interesting the moment another company's agent enters the workflow.
Do you actually need a multi-agent system?
Probably not yet. Only about one in five production deployments runs three or more agents together. Most working systems are one well-built agent with good tools, and they are cheaper to run and far easier to debug.
Most teams reach for a multi-agent framework one project too early, then spend the next quarter debugging conversations between robots.
What actually multiplies when you split one agent into three
Not the capability. Multi-agent looks impressive in a demo because the diagram looks impressive. Here is what the diagram does not show you.
Straight arithmetic on a three-agent design, not a benchmark. Your numbers will differ; the direction will not.
Four gates before you split
Work down them in order. Most teams stop at the first one.
Does one context window hold it?
If you can describe the whole job to a new hire in one paragraph, one agent will do it. Stop here — most of you can.
Different tools and different permissions?
Different prompts is not a reason to split. Different permissions is — when one role must never be able to issue the refund the other one can.
Do separate teams own the pieces?
An org boundary is a legitimate seam. If two teams need to ship without waiting on each other, split along that line, not along a whiteboard.
Still yes? Split one seam.
One seam at a time, at the place that actually hurt. Never straight into the five-box diagram from the conference talk.
If you stopped at gate one, that is a win, not a shortcut. You just saved yourself a quarter of debugging.
Framework, platform, or delivery partner — which route is right for you?
Three ways to end up with a working agent, and they suit very different situations. The framework is only the first of them, and it is the most work.
Sometimes the honest answer is “buy the platform”. Better you hear that from us now than find it out in month four.
| Route | Best for | What it really costs | You end up owning |
|---|---|---|---|
| Build on a framework yourself | Teams with Python/.NET engineers and time to learn agent-specific failure modes | Engineering months + the eval layer nobody budgets for | Everything, including the maintenance |
| Buy an agent platform | Standard workflows, no engineering capacity, need it live this week | Per-seat or per-run fees forever; their roadmap, not yours | A subscription |
| Delivery partner (us) | You know the workflow, want it in production in weeks, and want to own the result | $8K–$35K typical; 2–4 weeks to a working agent | Code, prompts, orchestration, evals — handed over |
We don't resell a framework. We have no partnership that pays us to recommend one, which is the only reason our recommendation is worth anything. When a platform tool is genuinely the better fit, that is what we will tell you.
What we can show you: we run our own agents in production on this site — a sales agent, an appointment-booking agent and an email agent. We would rather show you those working than hand you a case study.
What does it actually cost to change framework later?
Between two and eight engineering weeks for a working single-agent system, depending on how much of your logic leaked into framework-specific code. The framework itself is rarely the expensive part.
This is the number nobody puts in the proposal. So here it is, before you commit to anything.
| What moves | Effort | Why |
|---|---|---|
| Prompts and tool definitions | Days | Mostly portable if you kept them out of the framework's classes |
| Orchestration & state model | 1–3 weeks | The real rewrite — graphs, crews and handoffs do not map onto each other |
| Integrations | Days–1 week | Cheap if they sit behind your own interfaces; painful if not |
| Evaluation harness | 1–2 weeks | Often rebuilt from scratch, because it was tied to the old runtime |
| Re-earning trust internally | Unbudgeted | The one nobody puts in the estimate, and the one that hurts |
How we keep that number small on builds we run: business logic lives in your code, not in the framework's abstractions. Tools sit behind plain interfaces. Evals run against the agent's behavior, not its internals. None of that is clever — it is just refusing to marry the framework.
HOW A FRAMEWORK CHOICE QUIETLY BECOMES A REWRITE
MONTH 1
The demo works. Three steps, happy path, everyone delighted
MONTH 3
Real users do things the demo never did
MONTH 5
It failed and the trace does not go deep enough to say why
MONTH 6
The rewrite. Not a bad framework — an unseeable one
The exit is at stage 2 — instrument it before the edges arrive, not after
Illustrative — the failure pattern behind the 88% figure, not a specific client’s project.
How do we pick the framework for your build?
Four steps, and the first has nothing to do with frameworks. We look at the shape of the work, then at what has to be provable, and only then do we choose.
Match the task shape
Linear pipeline, branching workflow, or a set of roles? That one question eliminates most of the table above before we discuss anything else.
Decide what must be provable
What will you have to show a customer, an auditor, or your own board? That sets the evaluation design — and it often overrides the framework preference.
Build, integrate, deploy
A working agent on your data in 2–4 weeks, wired into your real systems rather than a sandbox copy.
Hand it over
Code, prompts, orchestration and evals become yours. Keep us for iteration if it helps — the agent runs either way.
You are never buying a black box from us. Step four is the point of the whole thing.
THE SWAP TEST · FIVE PARTS MOVE, ONE DOESN’T
Every decision must be reconstructable later
Shortlist: graph-based control
Specialists hand work to each other
Shortlist: role-based orchestration
It must pass your security review
Shortlist: whatever your platform already runs
Retrieval is the product
Shortlist: document-pipeline engines
The reasoning model, the tools and the hosting stay swappable in all four branches
Illustrative — our selection logic, not a ranking. We do not resell a framework.
How much does it cost to build an agent on one of these frameworks?
A focused production agent runs $8K–$35K to build, plus a monthly run cost. The framework is free; the engineering, integration and evaluation work is what you are paying for.
A representative $22,000 stateful agent (LangGraph)
Representative scope for planning, not a past client invoice. Run cost lands around $400–$2,500/month depending on volume and model choice.
The cheapest quote you get will be the one that quietly dropped the evaluation line.
Notice which line is third-largest. Evaluation and observability is the piece cheap quotes leave out, and it is the same piece that 64% of failed pilots named as their blocker. When a quote comes in suspiciously low, that is usually the line that went missing.
Which LLM should the agent actually run on?
The framework is the wiring; the model is the engine, and it is where nearly all of your running cost goes. Every framework on this page is model-agnostic, so this is a separate decision — and a reversible one, if you build it that way.
The framework is a decision you make once. The model is a bill that turns up every single month.
| Provider | Where it is strongest | Where it costs you |
|---|---|---|
| Anthropic (Claude) | Long multi-step tool use, following a policy exactly, honest “I don’t know” behavior | Top tier is the most expensive per token on this table |
| OpenAI (GPT) | Broadest ecosystem and tooling; the cheapest small models by a wide margin | The research-grade tier is dramatically more expensive than everything else |
| Google (Gemini) | Cheapest flagship-class option; natural fit if you are already on Google Cloud | Fewer mature agent integrations than the other two |
| Open-weight (Llama, Mistral, Qwen) | No per-token cost, full data control, runs on your own hardware | You pay in GPUs and engineering time instead — rarely cheaper below serious volume |
What it costs per million tokens
| Model | Input | Output | Context |
|---|---|---|---|
| Claude Opus 5 | $5 | $25 | 1M |
| Claude Sonnet 5 — our usual default | $3 | $15 | 1M |
| Claude Haiku 4.5 | $1 | $5 | 200K |
| GPT-5.5 | $5 | $30 | — |
| GPT-5 mini | $0.25 | $2 | — |
| Gemini 3.1 Pro | $2 | $12 | — |
| Gemini 3 Flash | $0.50 | $3 | — |
Claude figures from Anthropic’s published pricing; OpenAI and Google figures from 2026 pricing aggregators. List prices move — we re-check them at every build, and so should you before budgeting.
See the range, then see where you'd land
Same anchors as every LoopHawk agent build. No per-seat license, no lock-in.
No discovery fee buried in the footnotes, no per-seat surprise later. You see the range before you ever speak to us.
Starter agent
- One job, one framework
- Working demo on your data
- ~1–2 weeks
Production agent
- Durable state & retries
- Evaluation + observability
- Integrations & handover
- Live in 2–4 weeks
Custom / multi-agent
- Coordinated agents, A2A
- Deep integration work
- Security review included
Ongoing support from $200/mo. Migrations off a maintenance-mode framework are quoted from the table in what a rewrite costs.
Do the right AI agent frameworks change with the size of your team?
More than your industry does. The AI agent frameworks that survive a two-person team are not the ones that survive a platform team. What breaks a two-person team and what breaks a fifty-person team are different failures, and the frameworks are not equally good at surviving either one.
Two engineers and no platform team
Pick the one with the least to operate. Every hour spent running orchestration is an hour not spent on the thing customers pay for. A managed runtime or a low-code layer is not a compromise at this size — it is the correct call.
A product team with real traffic
Now the control model matters, because you have enough concurrent runs to meet the edge cases a demo never reaches. This is where graph-based control earns its complexity, and where you can no longer skip the tracing layer.
Platform team, security review, compliance calendar
Choice narrows to whatever your security team will sign off, whatever your existing telemetry can already read, and whatever survives an audit. That is a far shorter list than the internet suggests.
Match the framework to whoever has to run it at 2am.
Does your industry change the answer?
Less than vendors imply. Industry rarely changes which of the AI agent frameworks wins. It changes which of the six parts you are not allowed to get wrong.
| Industry | What changes | What it rules out |
|---|---|---|
| E-commerce | Live order state — the agent must read the truth, not a cached copy | Anything that cannot call your order system mid-conversation |
| Healthcare & finance | Every decision must be reconstructable months later | Loops you cannot replay step by step |
| Logistics | Long-running jobs that outlive the request | Frameworks with no durable checkpoint |
| Professional services | Document pipelines carry the value | Thin retrieval layers |
| SaaS | Multi-tenant isolation and per-customer config | Anything with global mutable state |
Your industry does not pick the framework. It picks the part you cannot compromise on.
Should you build it yourself or have us build it?
If you have Python or .NET engineers with time to learn agent-specific failure modes, build it yourself — the frameworks are open and good. Hire out when the deadline is real and the learning curve is not the thing you are being paid to climb.
We turn work down on this basis. If your team can do this in two weeks, we would rather say so than sell you a project.
✓ Have us build it if
- you need it in production in weeks, not quarters
- nobody in-house has shipped an agent before
- a wrong answer has a real cost — money, compliance, trust
- you're stuck on a maintenance-mode framework
- the prototype works and production doesn't
✗ Go DIY if
- your team already runs LLM systems in production
- the scope is small and the blast radius is tiny
- learning the stack is the goal
- you have more engineering time than budget
What should you look for in whoever builds this for you?
At some point the question stops being which of the AI agent frameworks and starts being who is going to run this in production. Six things worth checking — and the last one is the only one that cannot be faked in a sales call.
- They name a framework before they scope your problemThat is a reseller, not an advisor. The recommendation should arrive after the requirements, never before them.
- They can explain the control model in your wordsIf the graph-versus-role difference only survives in jargon, they have not built enough of them.
- They price the build, not the licenseThe framework is free. What costs money is proving it works, wiring it in, and the on-call rota afterwards.
- They tell you when not to buildThe honest answer to “do we need multi-agent” is usually no. A partner who never says no is optimizing for scope.
- They hand over something you can operateIncluding the tracing layer, the evaluation set and the runbook — not just a repository.
- They show you a working one before you payEverything above is a claim until something runs on your data.
Five of these are checkable in one call. The sixth is why we lead with a demo.
How do you choose a framework without a three-week evaluation?
Answer four questions in order. The first one that gives you a clear answer usually settles it, and you can stop.
Ten minutes on the right four questions beats three weeks of proof-of-concepts. We have watched both play out.
1. Does the work need to survive a restart?
If a run can take hours, needs approval partway, or must be replayable for an auditor, you want durable state. That points at LangGraph, or Microsoft Agent Framework if you are a .NET shop. If a failed run just means running it again, skip to question two.
2. What language does your team actually ship in?
Python teams have the widest choice. .NET teams should look hard at Microsoft Agent Framework, because the alternative is running Python services your team will not enjoy owning. TypeScript product teams are usually happier with Mastra or the Vercel AI SDK than bolting on a Python backend.
3. Are you already inside one vendor's ecosystem?
Deep in Azure, AWS or Google Cloud? The native option removes a category of integration work — Microsoft Agent Framework, Strands, or Google ADK respectively. The convenience is genuine. So is the trap underneath it. Trade it knowingly.
4. Are you still proving the idea?
Then optimize for speed to first result, not for architecture. CrewAI or a minimal loop like Agno will tell you in days whether the idea holds. Rebuild properly once it does — that rebuild is cheaper than the six months you would spend designing for a product nobody wanted.
What should you check before you build on any framework?
Seven checks, each one learned the expensive way. Run them before the first sprint, not after the demo goes well.
Print it. Take it to every vendor you are talking to — us included — and make them answer each line.
| Check | What good looks like | The risk if you skip it |
|---|---|---|
| Release cadence | Meaningful commits and releases in the last 90 days | You inherit a project in maintenance mode without being told |
| State & durability | Runs survive a process restart without losing context | Every crash becomes a support ticket and a manual replay |
| Human-in-the-loop | A first-class way to pause for approval and resume | You bolt it on later, badly, under compliance pressure |
| Observability hooks | Traces you can read without a vendor dashboard | The 64% blocker — you cannot prove the agent is right |
| MCP support | Native or a maintained adapter | Custom glue for every internal tool you connect |
| Escape hatch | Your logic sits in your code, not in their classes | A framework change becomes a rewrite (see the cost table above) |
| License & hosting | Permissive license, self-hostable, no phone-home | A procurement surprise two weeks before launch |
Run those seven against any framework on the map and the shortlist collapses to one or two fast. It is the same checklist we run on our own builds — there is no secret method behind it.
AI agent frameworks — your questions
What is an AI agent framework?
The code layer that controls how an agent plans, keeps state, calls tools and hands off to a human. Most 2026 shortlists come down to four names: LangGraph, CrewAI, Microsoft Agent Framework and the OpenAI Agents SDK.
Is there a single best AI agent framework?
No single one wins. LangGraph suits stateful production work, CrewAI suits fast role-based prototypes, Microsoft Agent Framework suits .NET and Azure estates, and the OpenAI Agents SDK suits teams already inside that ecosystem.
Is AutoGen still worth using?
Not for new builds. AutoGen moved into maintenance mode on 3 April 2026 when Microsoft Agent Framework 1.0 shipped. What you already run keeps running. Anything new should begin on the successor.
What happened to Semantic Kernel?
Same story as AutoGen. Its capabilities were consolidated into Microsoft Agent Framework 1.0 and the original moved to maintenance mode. It was not deleted, and it was not technically merged.
LangGraph or CrewAI — which should I start with?
CrewAI if you want something running this week and the run is short. LangGraph if the process has state worth protecting, needs human approval, or has to be replayable later.
What is an LLM agent framework, and is it different?
Same thing. "LLM agent framework" and "AI agent framework" describe the orchestration layer around a language model that lets it use tools and take multi-step action.
Do I need MCP or A2A support?
MCP matters if your agent calls internal tools and data — over 9,400 public servers now speak it. A2A matters when agents you do not own need to hand work to yours.
Do I need a multi-agent system?
Usually not at first. Around 22% of production deployments run three or more agents. One well-built agent with good tools is cheaper to run and much easier to debug.
What does a framework-based agent build actually cost?
Typically $8K–$35K for a production build, with a representative stateful agent around $22,000 and roughly $400–$2,500 a month to run. The frameworks cost nothing; the engineering around them is the spend.
How fast can a framework agent reach production?
Two to four weeks for a focused production agent, including integrations and the evaluation layer. Multi-agent systems take longer, mostly because of permissions and testing.
What does switching framework later cost?
Two to eight engineering weeks for a single-agent system. Orchestration and the evaluation harness are the expensive parts; prompts and tools usually port easily if you kept them out of framework classes.
Do we own what you build?
Yes. Code, prompts, orchestration and evaluation harness hand over to you. We build on open frameworks and resell nothing, so there is no license to lose.
What happens after you get in touch?
Four steps. You see it working at step three, before any commitment.
HOW WE PICK YOURS — AND PROVE IT BEFORE YOU PAY
MAP THE WORKFLOW
Your real process, including the parts nobody documented
NAME THE LOCKED PART
The one thing that must not change in month six
BUILD A LIVE DEMO
On your data, free, before any commitment
HAND IT OVER
Repo, eval set, tracing and runbook — you own it
You own the build, the evals and the runbook · no license, no lock-in
Illustrative — our delivery sequence, not a specific client’s engagement.
You will have seen it run on your own data before you decide anything.
Skip the framework gamble — see it built on your workflow
Tell us what the agent has to do. We’ll name the framework we’d use and why, then build a working version on your real process so you can judge it before paying. If your team should build this in-house, we’ll tell you that instead.
Custom AI Agent Development
The full build service — scoped, integrated, handed over.
Explore →Multi-Agent Systems
When one agent genuinely isn't enough — and when it is.
Explore →AI Agent API
Build a portable core so a provider deprecation can't break you.
Explore →AI Agents for E-commerce
Order tracking, returns and cart recovery on your live store data.
Explore →AI Agent Consulting
Honest advice on where agents pay off — and where they don't.
Explore →Enterprise AI Agents
Governed agents with audit trails that clear security review.
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