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allv Memory

An AI assistant that remembers how your team actually operates.

Store preferences, decisions, run learnings, and mission context once, then let allv use that context automatically across chat, workflows, smart inbox, and routines.

Get StartedExplore Memory Docs

Built-in memory controls include auto-save, ask-first approvals, source provenance, and reviewable updates before the context becomes durable.

Agent Memory

Auto-Save On
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Exec digest format

Lead with blockers, then owner updates, then follow-up actions.

smart_toyLearned from chat

Run recovery preference

Retry browser fetch with fallback provider before escalating to operator review.

historyFrom Run #cmlhme

Why Memory Matters

Your assistant becomes more useful after every conversation and run.

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No more repeated instructions

allv keeps the context you repeat most, so chats, inbox review, and workflows start with better defaults.

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Context mesh across surfaces

The same saved knowledge can shape chat responses, workflow steps, inbox drafts, and proactive routines.

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Learning loops from real execution

Runs can produce new learnings, reflection notes, and next-step guidance that improve the next execution.

Memory Categories

Structured context for durable, reusable knowledge.

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Preferences

preference

Communication style, approval habits, and operating defaults.

Prefers concise weekly updates every Friday.
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Decisions

decision

Durable choices the workspace should keep honoring in future runs.

External customer replies must stay approval-gated.
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Run Learnings

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Execution patterns captured from real runs, recoveries, and post-run review.

Release brief succeeds when the fallback fetch provider is available.
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Mission Packs

mission_pack

Grouped operational context, goals, and constraints that the agent can reuse together.

Weekly exec digest pack includes stakeholder tone, sections, and escalation rules.
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Learning Loops

Turn execution history into useful memory instead of operational residue.

Capture from runs

Turn successful and failed executions into durable learnings instead of leaving them buried in run history.

Reflect with provenance

Keep source metadata, timestamps, and run references attached so teams can trust and audit what was learned.

Feed the next workflow

Let stored learnings shape future prompts, branching logic, and operator recommendations across the workspace.

Context Mesh

Let the same memory shape chat, workflows, runs, and routines.

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Chat

Bring preferences and prior decisions into every new request without the user re-explaining the same context.

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Workflows

Reuse mission packs, learned constraints, and formatting choices inside scheduled or event-driven automations.

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Runs and Routines

Use memory to prioritize briefings, shape recovery behavior, and explain why the agent suggested a next step.

Reflection

Capture what worked, what failed, and what the agent should remember next.

allv converts real executions into reflection summaries tied to the run timeline. Teams can review results quickly, approve the useful learnings, and fold them into future automation or mission packs.

  • check_circleOne-sentence summary per run with source metadata and provenance.
  • check_circleExplicit what-worked and what-failed breakdown tied to the execution history.
  • check_circleReflection can feed future workflows, inbox handling, and routine prioritization.

Run Reflection

Run #cmlhme

Release briefing recovered successfully after fallback fetch and stayed approval-safe.

WHAT WORKED

  • Fallback provider preserved the run without losing the draft brief.
  • Approval gate kept the external send paused until operator review.

WHAT FAILED

  • Primary browser fetch timed out on the first attempt.
tips_and_updatesTip: Prefer fallback fetch for long-form release-note pages before escalating.

Control and Safety

Memory is transparent, provenance-aware, and approval-conscious.

Memory behavior settings

Workspace-level controls map directly to how memory is proposed and stored.

memoryAutoSaveON
memoryAskFirstOFF

Pending approvals

Sensitive memory updates can pause for explicit review before becoming durable context.

Add Memorydecision · customer_reply_policy

Traceable source metadata and packs

Every memory entry records its source, timestamps, and related context so teams can audit provenance and organize durable knowledge into reusable mission packs.

agentsystemuserworkflow_contextmission_pack

Related features

Memory becomes more valuable when it shapes real work.

Workflows

Use saved context to shape prompts, branches, recovery behavior, and approvals inside your automations.

Smart Inbox

Bring relationship context, reply preferences, and prior decisions into AI-driven email triage.

Routines

Let saved mission context and operator priorities shape what your proactive briefings surface next.

Give your assistantlong-term memory.

Turn one-off chats and one-off runs into durable context that improves future execution.

Get StartedExplore Workflows Feature
Enable memory controls per workspace with any active plan.