You deserve an assistant as persistent as you are.
Crater is a long horizon assistant that , recovers its own pathing, shows its work and only asks for human judgment at the moments where it actually matters.
Persistent state
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Context: Inbox, calendar, browser, docs
Mode: Persistent; event-driven; supervised
Horizon: Minutes to days
Behavior model
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Tool choice surfaces stay available.
Long-horizon execution survives pauses.
Clarification happens only at decision boundaries.
Persistent memory
Stays with the work.
Keeps constraints, unfinished branches, and clarifications alive across hours, sleeps, and restarts.
Compositional tooling
Uses the right surface.
Browsers, APIs, files, and native integrations get chained together without losing intent in the middle.
Corrective adaptation
Learns on the next pass.
Every confirmation, correction, and preference becomes a sharper default the next time the task appears.
Adaptive and Expressively Adequate.
Crater will do whatever it takes, compositionally and with extreme creativity.
Tool-native orchestration
Browser. API. Filesystem. Repeat.
Recovering the path automatically
Maps the right tools to the job instead of forcing one interface onto everything.
Composes integrations together, backtracks when one path fails, then resumes from the strongest branch.
Carries the new route forward so the next attempt starts closer to correct.
Constantly learning the right path to go
Crater is trained to use the right tools and compose integrations.
It can use the browser, your connected apps, and local files in one continuous loop without dropping the objective.
When something breaks, Crater recovers, finds the next viable route, and keeps the task moving instead of stalling at the first failure.
Long-running execution
Suspended; event-driven; persistent.
Waiting on a trigger; state preserved
Background machines stay alive across long waits, delayed inputs, and external events.
Crater can suspend itself, wake back up, and continue from exactly the right checkpoint.
Long horizon work feels continuous instead of constantly starting over.
Built for super long-running agents
Crater is persistent and relentless.
It will do whatever it takes to stay with the problem, even when the work needs to pause, wait on an event, or stretch over a much longer horizon than a single request cycle.
Persistent machines keep their place in the task, so progress accumulates instead of getting lost in resets and handoffs.
Whatever you want.
Whatever you want.
Human steering
On confirmation / clarification.
Asked once; resumed cleanly
Crater asks for confirmation only when the task crosses a boundary that should stay human.
Clarifications arrive as clean checkpoints instead of dumping the entire state machine back on you.
Once you answer, the agent continues with the surrounding context still intact.
Interfaces that stay focused on the single next decision
On confirmation / clarification.
Human input stays narrow, deliberate, and well-timed. Crater surfaces one crisp question when approval or nuance is needed, then returns to autonomous execution.
The interface is adaptive and corrective, so each interruption is smaller, clearer, and easier to resolve than the last one.
Proactivity
Continual learning, focused on what matters.
Watching for the right trigger
Crater learns from what you reinforce, what you ignore, and what you repeatedly correct.
Intuitive triggers can wake agents proactively around the problems you care about most.
That means less prompting, less babysitting, and more useful initiative over time.
Crater Point turns taste into action
Continual learning; focused on what matters.
It develops a better sense of your priorities over time, then uses that learning to fire off the right agents proactively.
Crater Point is about deeply internalizing your problems so the system can act earlier, with better instincts, and with less ceremony.