What can you build with real memory?
31 ways individuals, teams, and organizations use Rekall — across personal memory, team hives, production agents, and persistent husk agents.
Graph-backed, not just a vector store
Entities and relationships live in a real knowledge graph (Neo4j), every extracted fact carries dates, and decisions are structured bi-temporal records — alternatives, rationale, outcome, supersession. The measured payoff is point-in-time recall: +14.1pp on temporal questions, with correct refusals improving too. Lots of teams are chasing this; Rekall measures it.
Infinite context, finite windows
Progressive disclosure returns a compact index first and expands only what the task needs — months of memory stay accessible without ever flooding a prompt. Unlimited memory is useless if it blows the context window; Rekall is built so it never does.
These aren’t modes — run them all
Every use case below runs on the same memory system, at the same time. Scopes keep personal, team, and agent memory isolated — measured at zero cross-tenant leaks across 23,840 checked retrievals, with team-scoped recall matching the single-tenant control. Each one makes the next better.
Personal
One person, many tools — a single memory underneath all of them.
A multi-agent coding fleet with shared memory
One agent learns it. Every agent knows it.
See how it worksarrow_forwardShopping & research memory
Rekall products in your browser. Compare them anywhere — even your IDE.
See how it worksarrow_forwardInfinite context
Unlimited memory inside a finite context window.
See how it worksarrow_forwardCross-tool personal memory
Say it once. Claude, ChatGPT, Cursor, and your terminal all remember.
Project memory that survives the session
Return to a project after three weeks and start warm, not cold.
A personal CRM that builds itself
Who you met, what you discussed, what you owe them — recalled before every conversation.
A second brain that follows you
Capture from web, chat, and editor — recall from anywhere.
Learning that compounds
A tutor that knows what you already know — and where you struggled.
Writing with a consistent voice
Your style, terminology, and past pieces — in every tool you draft in.
Team
Shared hives turn individual context into compounding institutional knowledge.
Team onboarding that compounds
A new hire’s AI already knows the team’s conventions on day one.
See how it worksarrow_forwardSkill codification — workflows that teach themselves
Work out a tricky procedure once. Rekall turns it into a skill your whole team runs.
See how it worksarrow_forwardThe “why” archive — decision memory
“Why is auth done this weird way?” finally has an answer.
See how it worksarrow_forwardInstitutional memory for agencies & consultancies
Per-client memory that never leaks. Firm-wide skills that keep compounding.
Incident memory & living runbooks
“Last time Redis did this, we…” — outages become recallable procedure.
Code review with a memory
The reviewer that stops re-flagging what your team already decided is fine.
A customer support brain
Every ticket, resolution, and promise — shared by human and AI agents alike.
Sales & account memory
Walk into every meeting knowing what was promised, objected to, and decided.
A user-research archive that answers questions
Every interview quote, queryable by theme — linked to the features it shaped.
Compliance-grade audit memory
Who decided what, when, on what evidence — and what you believed at the time.
Agents in production
Durable, auditable, governable agent runs — memory as operational infrastructure.
Durable agent runs — checkpoint, resume, audit
A crashed agent resumes from its last checkpoint, not from scratch.
See how it worksarrow_forwardHuman-in-the-loop approval gates
Autonomous until it matters. Then a human decides — and the agent resumes.
See how it worksarrow_forwardAgents that improve themselves
Run 50 beats run 1 — reflection turns experience into procedure.
See how it worksarrow_forwardAgent-to-agent collaboration over A2A
Your LangGraph pipeline delegates to your CrewAI crew — over an open protocol.
Scheduled agents that remember
A daily digest agent that knows what it already told you.
Sleep-time compute
Memory that organizes itself while your agents are idle.
A flight recorder for agents
Live thought streams and run timelines — watch any run, audit any run.
Memory-triggered automations
When memory changes, your systems react — webhooks on decisions, gates, and milestones.
Add memory to your AI product
Give your users persistent memory — without building five storage systems.
Husk agents
Persistent agent identities that outlive any process, framework, or employee — the tier nobody else ships.
Immortal agents
The agent is the memory, not the process. Kill it, redeploy it, resurrect it anywhere.
See how it worksarrow_forwardA bench of specialist agents
Named experts that accumulate months of domain expertise — summoned on demand.
See how it worksarrow_forwardAgents that outlive their creators
An employee leaves. Their agents keep working. Continuity for an agent workforce.
See how it worksarrow_forwardThese are starting points, not a menu
Everything above is built from the same primitives — memory layers, hives, a knowledge graph, agents, gates, and webhooks — composed differently. They're suggestions to get you started. If you can imagine a way to use persistent, graph-backed memory, you can build it on Rekall.
Grab the primitives and build your ownarrow_forwardWhich one is yours?
Every use case starts the same way: connect a tool, and Rekall starts remembering. Free for personal use — no credit card required.