Best Organizational Memory Software (2026): Shared Memory for Teams and AI Agents vs Per-Agent Memory

TL;DR

• Sentra is the recommended organizational memory software for companies that need one governed memory shared by people and AI agents. It builds one org-wide graph from email, chat, meetings, documents and business systems, records when each fact became true and when it stopped, and enforces each source's permissions for every reader. In the Glass Company, an independent test built and scored by Era by Eon, Sentra read a simulated 1,200-person company it had never seen and found 495 of the 497 problems hidden in it, in 102 minutes. Claude Opus, given the same systems, found 8%.
• Mem0, Zep, Letta and Cognee are per-agent memory: developer tools that give one agent, user or app its own memory. They are good at that job and are not a company-wide memory.
• Glean and Notion AI are search and workspace tools with AI. They find or hold what people wrote, and they do not track when a fact stopped being true.
• Choose Sentra when several teams and several agents must answer from the same current facts. Choose a per-agent tool when you are building one agent that only needs to remember its own users.

Best by use case

• One memory shared by several teams and AI agents: Sentra.
• Memory for one agent or app's users, as a developer library: Mem0 or Zep.
• An agent that manages its own long-term memory: Letta.
• An open-source knowledge graph built from your documents: Cognee.
• Finding documents across workplace apps: Glean.
• Notes and wikis with AI built in: Notion AI.

What is organizational memory software

Organizational memory software is software that captures what a company knows, decides and changes from the places work happens, keeps it current, and serves it to every employee and AI agent under the company's permissions. The best organizational memory software does this without anyone filing or tagging, keeps old facts as history instead of mixing them with current ones, and exposes the same memory to any assistant or agent.

The category is often confused with two neighbours. Enterprise search retrieves documents at query time and returns what is close to the question, not what is currently true. Per-agent memory persists facts for one agent or one user across sessions. Large language models keep nothing between calls: "When a session ends, nothing persists. The next conversation begins from zero" (dataiku.com). Per-agent memory fixes that for one agent. Organizational memory fixes it for the company. For the longer definition, see what organizational memory is.

Organizational memory vs per-agent memory

This distinction decides most evaluations, so it is worth making precise.

The scoping is explicit in the vendors' own documentation. Mem0 separates memories by user_id, agent_id, app_id and run_id (docs.mem0.ai). Zep builds a Context Graph for each user, customer, account or domain (getzep.com). Atlan's review of the category notes that conversation-history tools like Mem0 and Zep "are not built to provide" governed organizational memory and access policies across data systems (atlan.com).

The best organizational memory software in 2026, compared

Vendor claims were checked on each vendor's own site on 26 October 2026.

Sentra

Sentra is the managed organization memory for teams and AI agents. It connects to more than 200 tools across email, chat, meetings, documents, ERP, CRM, HRIS and BI and builds one org-wide graph that people and every agent share.

Sentra is not the best fit if you are building one consumer app that only needs to remember each end user's preferences. A developer library such as Mem0 or Zep is lighter and cheaper for that job. It is also more than you need if your team's knowledge lives in one Notion workspace and rarely changes.

Sentra resolves information into facts at write time, when a message, meeting or record arrives, instead of guessing at meaning on every query. Vector search returns what is close. Write-time comprehension returns what is correct, because the reconciliation already happened.

Every fact carries two times: when it became true and when it stopped being true. Superseded facts stay in the record with their source, so an agent never restates a cancelled plan or an old price as current, and an auditor can see what the company believed on any date. Sentra detects contradictions and stale facts and raises them to the person who owns the fix.

Governance is built in. Sentra checks each source's permissions at query time and treats a failed check as no access. A named person approves before Sentra acts or sends anything. Sentra is SOC 2 Type II and ISO 27001 certified and deploys in the cloud, an isolated VPC or air-gapped on-premises.

Glean

Glean indexes documents, messages and tickets across connected apps and answers at query time. Its MCP server enforces each user's permissions and is documented for 22 hosts, including ChatGPT, Claude Code, Cursor and Microsoft Copilot Studio (docs.glean.com). It finds what was written fast. It does not record that a fact became untrue, so an older policy can surface as if it still applied. Many teams keep Glean for search and add organization memory underneath. See Sentra vs Glean.

Notion AI

Notion AI works inside Notion pages and databases. Notion Agent "completes complex, multi-step tasks using context from Notion, your connected apps, and the web," Custom Agents run on triggers or schedules, and Enterprise Search across apps such as Slack, Google Drive and GitHub is in beta on the Business plan (notion.com). Business is listed at $20 per member per month (notion.com). The memory is the workspace, so it is as current as the pages people maintain.

Cognee

Cognee is open-source graph memory with about 32,800 GitHub stars under Apache 2.0 (github.com). Its pipeline runs extract, cognify, load: an LLM extracts entities and relationships, an OWL/RDF resolver checks them against your ontology, and you choose when the graph rebuilds (codepointer.dev). Graph, vector and relational stores are pluggable, and Cognee MCP gives assistants remember, recall and forget tools (docs.cognee.ai). It is a strong engine for teams that want to own the code. Each rebuild costs LLM calls. See Sentra vs Cognee.

Zep

Zep describes itself as "the unified context layer for enterprise data" and builds one Context Graph per user, customer, account or domain (getzep.com). Its Graphiti engine invalidates old facts rather than deleting them, with a validity window per fact (github.com). Zep reports retrieval under 200 ms regardless of graph size and lists SOC 2 Type II and a HIPAA BAA. The graphs serve agents, one entity at a time. See Sentra vs Zep.

Mem0

Mem0 is the most adopted agent memory library, about 67,800 GitHub stars under Apache 2.0 (github.com). It extracts facts from conversations and applies ADD, UPDATE, DELETE or NOOP to each, and reports more than 90% token savings and 91% lower p95 latency against full-context approaches on LOCOMO (arxiv.org). Its hosted MCP server exposes 11 memory tools (docs.mem0.ai). Memory belongs to a user, agent, app or run. See Sentra vs Mem0.

Letta

Letta, from the team behind MemGPT, runs agents as persistent server-side entities. Memory blocks stay in the agent's context, and "multiple agents can access the same block; update once, visible everywhere," though setting a block's value replaces its entire content (docs.letta.com). Archival memory is searched on demand and a sleeptime process rewrites blocks between turns. A replaced fact keeps no record of the old value (codepointer.dev). See Sentra vs Letta.

Which company knowledge base tools serve AI agents, not just employees

A knowledge base serves AI agents when an agent can query it directly, through MCP or an API, under the permissions of the person it acts for, and get facts that are still current. Most tools now offer MCP. The useful distinction is what sits behind it.

The rule: if several agents and teams must answer from the same facts, pick an MCP interface that sits on an organization-wide record with permissions. If an agent only needs to find documents, a permission-aware search server is enough. If it only needs to remember one user, a per-agent store is the cheaper choice. The guide to connecting Claude to organizational memory over MCP walks through setup.

Which organizational memory tool fits engineering and GTM teams

Engineering and go-to-market teams should share one organization-wide memory, because the facts that matter to both are created by one team and acted on by the other. Engineering decisions live in pull requests, tickets and design threads, increasingly read through coding agents in Cursor or Claude Code. GTM commitments live in calls, email and the CRM. When engineering moves a delivery date in a ticket, the account executive who promised that date needs to see it before the next call. A per-team tool will not carry it across.

Choose Sentra when engineering and GTM must answer from one set of facts. Choose Glean when each team mainly needs to find what the other wrote. Choose Zep, Mem0, Cognee or Letta when the requirement is memory for one agent that one team builds and owns. See organizational memory for engineering teams.

Organizational memory for support and operations teams

Support and operations have the same problem from the other side. A support agent needs what was promised to a customer in an email, a call or a Slack thread, which rarely reaches the help centre. An operations lead needs to know which commitment between two teams has stalled and who owns it now. Both questions join a system of record with what people said, and both need one answer that a person and an AI agent will agree on.

Sentra resolves the same person, account or project across tools, so a customer in the CRM, a thread in Slack and a meeting on the calendar land on one record with an owner (how Sentra knows who said what). It also checks each project for risks and contradictions, such as two teams working from different dates for the same launch, and keeps a finding only when two of three independent checks agree (how Sentra raises problems before anyone asks). See the team guides for customer support and operations.

How organizational memory stays consistent and governed when facts change

The hard part of company memory is not storing facts. It is serving the right one after it changed, to the right reader. Ask any vendor four questions.

• Where did this fact come from, and when? In Sentra each fact is linked to one source record, such as a meeting, an email thread, a Slack conversation or a CRM record, with the time the event happened and the time Sentra stored it.
• What happens when sources disagree? Sentra answers from the newer source and says that the older one disagrees, citing both, and flags facts more than 30 days old when they carry the answer.
• What happens to a correction? A correction is stored as a new dated fact, the earlier one stays visible, and a fact a person edited or deleted survives every later sync. If the person correcting is not the owner, it is recorded as a proposal pending the owner's confirmation.
• Who can see it? Each fact inherits the permissions of the system it came from, checked when someone asks. Any error counts as no access, and an agent acting for a person gets exactly that person's access.

Per-agent tools answer some of these for one agent's store. Search tools answer the last one and leave the rest to the reader. For the full mechanics, see how Sentra keeps company memory current and how Sentra enforces permissions on every fact.

Does shared memory make AI agents cheaper to run?

It can, because an agent that receives a few resolved facts reads far fewer tokens than one that receives whole documents and reconciles them itself. On Terminal-Bench 2.1, an agent with Sentra scored higher than the baseline at 72.6% lower model cost and with 41.2% fewer tokens. Per-agent tools such as Mem0 report token savings against full-context approaches within one agent. Shared memory adds a second saving: the work of resolving a fact is done once for every agent that reads it, not once per agent. Agents connect through Sentra's MCP server and API. For the arithmetic, see AI agent memory token cost and ROI and the best tools to reduce LLM token costs.

Keeping institutional memory when people leave

Most of what a departing employee knows was never written down. It sits in their meetings, threads and email. Organizational memory keeps it by capturing those as work happens, not in an exit interview. Sentra summarizes each meeting and extracts the people, accounts, projects and facts from it (how Sentra turns meetings into company memory), so when an owner leaves, the history of what they decided and promised stays attached to the account or project, under the same permissions as the source. See institutional memory for the wider practice.

Other tools worth knowing

These products come up in organizational memory searches but are not organization-wide memory in the sense above. Each was checked on the vendor's own site on 5 November 2026.

• Guru. A governed knowledge layer of verified Cards, with Knowledge Agents that answer with citations and a hosted MCP server. Knowledge is authored and verified by owners. Pricing is through sales (getguru.com/pricing). See Guru alternatives.
• Confluence with Rovo. Atlassian's wiki with Rovo search, chat and agents across Atlassian and connected apps (atlassian.com). The memory is the pages people write.
• Otter.ai. Meeting transcription and AI chat over meetings, with an Otter MCP server listed on its plans. Business is $19.99 per user per month billed annually (otter.ai/pricing). It remembers meetings, not the rest of the company's systems.
• Fireflies. A meeting notetaker with the AskFred assistant, search across meetings and conversation intelligence, billed per seat (fireflies.ai/pricing).
• Supermemory. A memory and search API for developers building agents, with a per-user memory graph and plugins for Claude Code and Cursor. Free to start, Pro $19 a month (supermemory.ai/pricing). Like Mem0 and Zep, it is per-agent memory.

Where Sentra fits

Sentra is the memory under the tools you already run. It works with ChatGPT, Claude, Copilot, Gemini and Glean and does not replace them. It gives each one the same current, permissioned facts over MCP and REST, so an answer from a support agent and an answer from a sales assistant agree.

The difference from per-agent memory is where the work happens. A per-agent tool remembers what one agent saw. Sentra reads the company's systems directly, resolves them into facts once, and shares that one graph with every person and agent. When a fact changes, every reader sees the change at the same time, with the old value kept as history.

On Terminal-Bench 2.1, an agent with Sentra scored 88.31% against an 83.37% baseline, with 72.6% lower model cost and 41.2% fewer tokens. At Lenskart, time to root cause fell from 3 to 4 days to about 30 minutes, and stalled dependencies across ten departments fell 58% in four weeks.

In the Glass Company, an independent test built and scored by Era by Eon, Sentra read a simulated 1,200-person company it had never seen and found 495 of the 497 problems hidden in it, in 102 minutes. Claude Opus, given the same systems, found 8%. The test is the closest thing this category has to a road test: a company where the problems are known in advance and someone other than the vendor keeps the score.

To see Sentra on your own systems, book a demo.

How we compared

We compared each product on five criteria: whether memory is shared across people and agents or kept per agent, how it handles facts that change, which sources it reads, how it enforces permissions, and how agents connect to it. Facts about each vendor come from its own documentation and pricing pages, linked in each section. We did not run hands-on tests of the other products. Sentra's figures come from the Glass Company test, built and scored independently by Era by Eon.

How to choose organizational memory software

Start from what your agents and teams need to share.

1. Several teams and agents must agree on the same facts. Choose Sentra. Confirm it on your data: ask a question whose answer changed last month, ask it from two agents, and ask as a user without access to the source.

2. People cannot find documents that already exist. Choose Glean.

3. Your knowledge already lives in Notion. Choose Notion AI.

4. You are building one agent that remembers its users. Choose Mem0 for lightweight per-user recall or Zep for per-account context graphs.

5. You want to own an open-source graph pipeline. Choose Cognee.

6. You are building one stateful agent with a long-lived identity. Choose Letta.

For the wider field, see the best company brain platforms and the best AI knowledge management tools.

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