Organizational memory: Private Beta
Your Company Brain, Built on Mem0
Agents shouldn’t start from zero every session. Organizational memory captures decisions once so every agent, every team, can retrieve them forever.
847 teams already on the waitlist , spots close soon
Example memories , illustrative
“Auth service migrated to OAuth2 , legacy tokens deprecated Q3.”
src: github · eng
88% conf.
“Refunds on annual plans: full refund within 14 days, prorated after.”
src: slack · finance
verified
The Problem
Why your AI agents keep
starting from scratch?
A company brain is supposed to be the one place your organization’s decisions live.
Without it, here’s what actually happens instead:
Case 01 · Threads
Slack threads die the moment the conversation scrolls past.
Someone solves a hard bug or makes a pricing call in a thread, and three weeks later a different agent , or a different person , hits the same question with zero access to that context. The decision existed. It just isn’t anywhere an agent can query it.
Case 02 · Onboarding
New hires (and new agents) re-learn everything from zero.
Onboarding a person takes weeks of shadowing and tribal-knowledge transfer. Onboarding an agent is worse: it has no shadowing option at all. Without a shared memory layer, every new agent instance starts as ignorant as the first one you ever deployed.
Case 03 · Consistency
Different agents give different answers because there’s no shared source of truth.
Your support agent says one thing about a refund policy. Your sales agent says another. Not because either is wrong , because they’re drawing from different, disconnected context windows, with no common memory to reconcile them.
Root cause: institutional knowledge quietly walks out the door every time a Slack thread scrolls off-screen, a contractor’s contract ends, or an agent session closes , and the company brain never gets the chance to form.
Features
What organizational memory
actually does?
Captures decisions, not just documents
Most systems index docs and wikis. Organizational memory captures the decision itself — who decided what, why, and when — pulled straight out of conversations, tickets, and code reviews as they happen.
Shared memory across every agent
One memory layer, many agents. A decision captured by your support agent is immediately queryable by your sales agent, your internal copilot, and your on-call engineering assistant — no manual sync required.
Governed, scoped, auditable
Memory is scoped by user_id, agent_id, and team, so nothing leaks across boundaries it shouldn’t. Every read and write is logged, so you can answer who knew what, and when — on demand.
Plugs into your existing stack
No pipeline rewrite. Mem0’s SDK drops into your current agent framework — LangChain, LangGraph, CrewAI, custom stacks — in a few lines of code, with the same API whether you’re on cloud, private VPC, or air-gapped infrastructure.
How it works
Capture → Structure → Retrieve
The same loop running continuously, across every team and every agent.
Comparison
Retrieval isn’t memory
Vector databases and proprietary graph tools solve pieces of this. Mem0 covers everything a real company brain needs.
Why this matters: Retrieval isn’t memory. A vector database will return three conflicting versions of the same fact with equal confidence and leave you to sort out which one’s true. Proprietary graph tools fix part of that , but lock your company brain into their platform, on their terms. Mem0 gives you the retrieval, the deduplication, and the governance, without the lock-in.
Capability
Vector Databases
Proprietary Memory Graph Tools
Mem0
Retrieval
Basic similarity search
Proprietary graph, locked to their platform
Unified retrieval across vector + graph + key-value
Deduplication
None , near-duplicate chunks pile up
Partial, vendor-dependent
Built-in, continuous
Contradiction handling
None , conflicting facts both get returned
Limited
Automatic contradiction resolution
Governance
Bolt-on, if any
Vendor-controlled
user_id / agent_id scoping, SOC 2, HIPAA
Portability
Depends on the DB you pick
Locked to one platform
Same API on cloud, private cloud, or air-gapped
Auditability
Rare
Limited
Every read/write logged
Who it’s for
Built for every team
that talks to an agent
Engineering
Stop re-explaining architecture decisions to every new agent session. Org memory holds the why behind past design calls, so coding agents stop suggesting patterns your team already rejected.
Support
One memory layer means every support agent — human or AI — answers from the same policy facts, instead of three different versions of “it depends.”
Ops
Vendor decisions, incident postmortems, and process changes get captured once and stay retrievable, instead of living in someone’s head until they leave.
Sales & RevOps
Every objection, commitment, and pricing exception made across a long sales cycle stays attached to the account, not buried in a rep’s inbox.
Why now
This isn’t a trend argument.
It’s a benchmark one.
92.5
LoCoMo benchmark
94.4
LongMemEval
−91%
p95 latency
−90%+
token cost
These numbers come from the Mem0 architecture paper (arXiv:2504.19413, “Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory,” ECAI 2025), benchmarked against full-context approaches on LoCoMo and LongMemEval , the two standards the field uses to test whether an agent actually retains and retrieves facts across long conversations. Retrieval at that accuracy, at that cost, is what makes a company brain viable at company scale instead of a research demo.
Trust & governance
Organizational memory only works if teams trust what’s in it
By Taranjeet, CEO, Mem0 , and trust that it can’t leak.
Data isolation.
Every memory is scoped by user_id and agent_id. An agent can only retrieve what it’s explicitly permitted to see , there’s no default “everything is visible to everyone” mode.
Encryption everywhere.
Data is encrypted at rest and in transit, full stop. No plaintext memory sitting in a database somewhere.
Deletion that actually deletes.
Right-to-be-forgotten requests under GDPR and CCPA are honored at the memory level, not just the document level , when a memory is deleted, it’s gone from every index it touched.
Full audit trail.
Every read and every write is logged with who, what, and when. If a compliance team asks “what did this agent know, and when did it know it,” we can answer in seconds, not weeks.
We built this the way we’d want it built if it were our own company’s memory on the line , because it is.
Join the private beta
Ready to stop rebuilding context every time an agent starts a new session?
Frequently asked
Questions worth answering plainly
What is organizational memory?
Organizational memory is a shared, governed memory layer that captures decisions, facts, and context from across your company — Slack threads, tickets, code reviews, calls — and makes them retrievable by every AI agent your teams run, not just one bot in isolation.
How is this different from a vector database?
Is my data secure and governed?
When does it launch?
Does it work with Claude, GPT, and other LLMs?
How do I give AI agents organizational memory?
Is a company brain the same thing as organizational memory?