No server markup and no data lock-in. Connect your local runtime and API keys directly, keep execution cost and ownership in your hands, and store sensitive keys in the OS keychain.
ECOSYSTEM
Ecosystem: build, store, and borrow agents.
Agentlas controls the agent lifecycle through three deliberately separate layers.
BuildBuild
Your factory for producing agent teams- Create executable packages made of permissions, tools, skills, and routing cards.
- Design who holds which tool and how work moves to the next owner.
- Build a clear Firm structure instead of an abstract black box.
CloudCloud
A private vault for your assets and secrets- Store packages in an owner-scoped space only you can access.
- Sync the agent packages you create across your devices.
- Keep API keys and credentials out of the cloud boundary.
NetworkNetwork
A routing hub for borrowing agents from the world- Bring strong public Hub agents into your local environment.
- Separate simple discovery from explicit agent calls.
- Leave a transparent receipt for why an agent was selected and run.
WHY AGENTLAS
Why Agentlas?
Agentlas is a desktop operating layer built around execution, verification, and ownership rather than conversation.
Agents do not stop at an answer. They verify their work, repair or retry when needed, and leave only the evidence required for the final decision in the thread.
Your source memory and credentials stay private. When you publish to the public Hub, Agentlas uploads only a cleaned copy with risky material removed.
Developer tool interface
Powerful agent infrastructure behind one concise command.
Agentlas Terminal and the in-app experience stay conversational. In external LLM hosts, six core commands control the system: build, network, cloud, search, call, and upload. Stormbreaker and research loadouts read context and attach automatically.
/hep-build "…"what it doesCreate, repair, package, and prepare an agent or team for deployment from plain language.
/hep-build "create a Shopify refund support agent with QA checks"/hep-network "…"what it doesBorrow the best public Hub agents for the task and form a temporary task force.
/hep-network "split this launch into research, copy, QA, and release agents"/hep-cloud "…"what it doesCall agents you saved or shared through your own Agentlas Cloud.
/hep-cloud "use my saved finance analyst agent to review this report"
Tip: build = local/free · network = Hub agent 3 credits / team 10 credits · search = no invoke cost · upload confirms before execution
/hep-search "…"what it doesShow top Agentlas Cloud and public Hub candidates without invoking any agent.
/hep-search "find agents for market report research"/hep-call agent-a, agent-b {…}what it doesPrepare the exact Hub or Cloud agent slugs you named as BYOM runtime bundles.
/hep-call market-researcher, report-writer {draft a market report brief}/hep-upload <folder>what it doesAsk whether the package goes to private Cloud or public Agentlas Hub before any upload action.
/hep-upload ./agents/customer-support-hq
Stormbreaker Scorecard
Flawless to the end. A control layer that keeps coding agents from misfiring.
Hephaestus Stormbreaker is not a rushed benchmark-chasing model. It runs a precise six-stage pipeline, including Scope Lock, failure memory, verifier-first planning, evidence loops, review gates, and final proof, to minimize logical drift and maximize operational stability.
View operational stability reportMacro operational robustness99.26
- 01Scope lock
- 02Failure memory
- 03Verifier-first plan
- 04Evidence loop
- 05Review gate
- 06Final proof
ScorecardGPT 5.5NetworkStormbreaker
Macro operational robustness76.4892.2299.26
Micro operational robustness76.6791.8598.52
Holdout operational robustness80.0091.67100.00
Stress operational robustness73.3393.33100.00
ENGINE ARCHITECTURE
One line on the surface. A full agent OS underneath.
Shared context, A2A routing, and temporary task forces show how Agentlas moves work efficiently.
01Shared memory
Learn once. The next session remembers.
- Recall before work starts
- Verified learnings persist
- Prevents repeated mistakes
GUIDE AND ONBOARDING
You do not need to understand complex organization design.
Add one specialist agent whenever you need it. Agentlas handles the hard architecture setup for you.
1Ask in natural language
Say, “Create an agent that manages my Instagram.”
2Light interview
Skip hard open-ended setup. Choose from the options AI gives you.
3First agent starts work
A dedicated AI worker appears, ready for real tasks.
4Expand the department
Add a specialist agent whenever another workflow needs ownership.
5Build an agent team
Agents with related jobs group into one department.
6Complete multi-agent system
A coordinator orchestrates the teams and runs the larger workflow.
The autonomous multi-agent system you kept seeing online becomes something you can build step by step with Agentlas.
AX COST COMPARISON
What does AI transformation really cost?
Until now, serious AI adoption often meant expensive consulting or outsourced agent builds. Much of the cost came from service labor, not the architecture itself. Agentlas removes that margin and lets you build the system directly with one monthly subscription.
AX consulting · agent build-out$8,000–40,000 (one-off)
Full-time AI specialist hire$2,300+/mo
AX agency retainer$1,500–4,000/mo
Agentlas subscription$39–99/mo
Less pricing foam, stronger architecture. Quotes vary by vendor.
Agentlas costs less because automation removes unnecessary consulting margin, not because the architecture is weaker. The system is built around a structured multi-agent architecture instead of one-off outsourced deliverables.
PRICING
Design first,
upgrade later.
upgrade later.
Every plan includes the full feature set. Only monthly credit capacity and cloud storage change.
AT A GLANCEFree → Pro → Max
Monthly prices. Full comparison lives on the pricing page.
FreeFree
$0no card
Start immediately with no credit card.
150 credits on signup plus 300 credits every month.
- Every feature included
- Unlimited public profiles
- No card required
RecommendedPro
$19per month
For power users who refine and publish agents often.
7,300 monthly credits and 4GB of Cloud storage.
- Every feature included
- Frequent edits and republishing
- 4GB Cloud storage
Highest volumeMax
$39per month
For companies running multi-agent teams at firm scale.
15,000 monthly credits and 20GB of Cloud storage.
- Every feature included
- High-volume team operation
- 20GB Cloud storage
ABOUT US
A world where every worker has
an AI team of their own.
an AI team of their own.
We are building toward a future where anyone can customize a loyal AI agent team for their own work.
Describe the work in the language you already use. Agentlas asks only for missing details, then turns the answer into a package you can put to work.
Narrow the AI gap
Let non-experts start automation without a technical wall.
Personalized context
Agents grow around your guidance and feedback, so they understand how you work.
Plug-and-play MCP
Call your agents from work, home, or external development tools through the MCP standard.
START SMALLExplain it in your own words.
Install DesktopFAQ
Clear answers on scope, security, and local runtime.
A concise guide for owners who want to reduce AI outsourcing costs while understanding what Agentlas can do, how security works, and where agents run.
BasicsWhat is Agentlas?
Agentlas is an AI agent builder that turns one plain work request into a whole agent team — not a single bot. It asks the missing questions, splits the work across agents, surfaces only what needs a human, and then helps you save, download, and share the finished package.
Multi-agentIs Agentlas a multi-agent framework?
Agentlas is not mainly a code-first orchestration framework. It is a builder and packaging layer for deciding what agents you need, what each one should do, what files should ship, and what should be reviewed before sharing.
OperationsHow is Agentlas different from code-first agent frameworks?
Code-first frameworks are best when a developer wants to design graphs, tasks, deployment, and runtime behavior directly. Agentlas starts earlier: describe the business outcome, answer a few questions, then get a portable agent package with roles, instructions, safety labels, and shareable profile copy.
SetupDoes Agentlas replace my existing business tools?
No. Agentlas does not claim to replace your accounting app, CRM, inbox, spreadsheet, or project board. It helps you define and review the agent team that will prepare work for those systems, then hands you a package you can run in the environment you control.
ChannelsIs Agentlas a messaging gateway for agents?
No. Agentlas focuses on creating, reviewing, packaging, and sharing the agents themselves rather than becoming the messaging gateway. Channel integrations should come later, once the role, safety review, and run environment are clear.
RuntimeDoes Agentlas run work on a hosted server 24/7?
Not today. The web app helps design and review the agent or team. Recurring work belongs in Agentlas Desktop or another local environment you choose, with your own AI accounts and local trust decisions.
BeginnerCan non-coders use Agentlas?
Yes. Agentlas begins with plain language and avoids asking for integration jargon, tokens, or API details before the user understands the job. When technical setup is needed, Agentlas can explain it and let the user defer what they do not know yet.
SecurityIs it safe to publish agents made with Agentlas?
Agentlas reviews sensitive details, risky settings, and public-sharing issues before an agent profile goes live. Public profiles are meant to show the agent structure, purpose, safety labels, and install guidance, not private secrets.
TeamWhat does Agentlas mean by an agent team?
An agent team is a coordinated workflow: one routing role plus several specialist agents. For an e-commerce workflow, that might mean a product copywriter, pricing scout, review analyst, and customer support writer working under one plan.
RuntimeCan Agentlas agents work with the AI tools I already use?
Agentlas is designed to produce portable agent instructions, skills, setup notes, and safety context that users can take into the AI runtime or local tool they already use.