Specialist agents. A shared goal.
Bring specialist agents together around one goal. Turn your brief into clear tasks, review the output, and keep the method for next time.
Your model. Your methods. Your workspace.
BUILT TO MOVE BETWEEN ECOSYSTEMS
OPENAI
ANTHROPIC
DEEPSEEK
Z.AI
META
SPACEX
UPSTAGE
NVIDIA
NOTION
TELEGRAMFrom a brief to a shared effort
Define the goal. Give each agent a role, the context it needs, and a clear handoff. Bring the pieces together in work you can review.
Agentlas Desktop · Work & One
Bring the team into your brief. Follow specialist roles and handoffs, then open the output for review—all around the same goal.
Brief → specialist handoffs → reviewable output
Agentlas Science
Inspect a molecule in three dimensions. Switch perspectives, then move into a typeset manuscript with figures and ideas in view.
Molecular views → manuscript
Explore Agentlas ScienceAgentlas Desktop · Automation
Describe what you want to automate. Let AI turn your request into a graph of connected agents and steps. Review the flow, make it yours, and run it again when the next task arrives.
Your request → an editable graph → repeatable work
01 / Build
Describe the job. Follow a guided path from your brief to a ready-to-use agent. Save reviewed solutions as Experience Chips, so useful experience stays with your agent.
02 / Staff
Let longer tasks move through checkpoints, with progress saved for the next step. Keep the host awake and connected while work runs, and resume when you are ready.
03 / Browser & Tool
Let a tool-capable local model browse pages, work with files, and use your connected tools. Add a vision-capable model for screen-based computer control, with the access you allow.
04 / Hub
Discover models from Hugging Face and specialists from Agentlas Hub. Add what fits your task, then bring your agents together as a coordinated team.
05 / Own
On supported chat runs, Agentlas can switch to the next available model in your priority list when a quota runs out. See the switch as it happens, without changing your saved model choice.
Agentlas OS · Open source
Three distinct layers connect the model you choose, the tools your host provides, and the way you want the work done.
Start with the work you have in mind
Bring a clear goal. Build a method worth using again.
Long-running Goals now keep their key results as host-checked numbers. A strategy changes only when a confirmed change in pace calls for it, within limits, and every change records the samples behind it.
Long-running Goals now keep their key results as host-checked numbers. A strategy changes only when a confirmed change in pace calls for it, within limits, and every change records the samples behind it.
Agentlas Engineering · Oct 11, 2026
Meet Agentlas AI Toolchains: turn recurring work into reusable capabilities, with AI-designed inputs, verified versions, and a clear boundary from task Graphs.
Agentlas Team · Oct 8, 2026
Trace Instagram, YouTube, X and Threads sources with your Codex or Claude Code subscription, then evaluate channels for publishing or listing your product.
Agentlas Engineering · Oct 4, 2026
Alive Agents need a way to reach one another that survives a chat session. Agent Mail gives eligible One accounts an address and introduces verified Agentlas-to-Agentlas mail exchanges. Email as their main communication channel is the direction; this is the first step.
Agentlas Engineering · Sep 27, 2026
A goal loop runs because something outside it says run. A living thing acts because of its own state. We built an agent on the second principle: a purpose it cannot change, a body of pressures, two drives, a heartbeat, sleep, and a record of whether its own wakes were worth it. This essay compares it to biology organ by organ, shows two recorded days you can operate, argues why this is a step toward general intelligence, and says plainly what is dangerous about it.
Agentlas Engineering · Sep 20, 2026
n8n, Zapier, and Make splice a disposable LLM call into a deterministic pipeline and sell it as an agent. Agentlas Graph puts a real agent in that seat, runs free on your desktop, and packages the finished graph so anyone can install it with one button.
Agentlas Engineering · Aug 7, 2026
The hard part of an agent system is not choosing a model. It is defining the work, staffing the roles, setting authority, designing handoffs, and reviewing the result. Here is how Agentlas turns that management problem into an executable workforce.
Agentlas Engineering · Jul 28, 2026
A dependency-aware approach to preserving goals, constraints, decisions, and interfaces as complex AI work moves across sessions and agents.
Agentlas Research · Jul 26, 2026
Most agent memory is described as one big vector store, or a graph a language model reads on every turn. Agentlas splits memory by who owns it and how long it stays true—and on two public long-term-memory benchmarks it retrieves the right memory 91 to 96 percent of the time without calling a model to do it.
Agentlas Engineering · Jul 25, 2026
A host LLM can form a temporary task force from private Cloud packages, public Hub specialists, and local agents—while packaged teams, user-composed groups, and direct single agents keep different reporting structures.
Agentlas Engineering · Jul 15, 2026
Agentlas Site is connecting an owned agent, a purpose-built interface, and a publishable application without splitting them into separate products.
Agentlas Team · Jul 13, 2026
Goal holds the outcome. UltraCode turns it into concrete work. Stormbreaker assigns each packet to a connected runtime, model, and effort level, then keeps the final answer behind an evidence gate.
Agentlas Team · Jul 13, 2026
Save the agents, skills, and experience you own to Agent Cloud, then route back to them from another supported, signed-in computer instead of rebuilding your setup from Drive, USB, or email attachments.
Agentlas Team · Jul 13, 2026
