The 6 best Supermemory alternatives in 2026
Supermemory ships two products under one name, and picking a replacement starts with knowing which one you were using.
The first is a memory API. It combines vector similarity with keyword search and advertises sub-300ms latency. Connectors cover Notion, Google Drive, Gmail, OneDrive, S3, and a web crawler, and the core is open source, self-hosting as a single binary. Supermemory argues this half on published evals, claiming state-of-the-art results on LongMemEval, LoCoMo, and ConvoMem, and it ships MemoryBench so you can rerun the comparison yourself. Those are the vendor's own numbers; the harness is the part worth using.
The second is Company Brain, a Slack-native agent carrying shared team memory. It answers questions, acts in tools like Linear and GitHub, and speaks up in a thread unprompted when it has something to add. It's self-serve, added straight to Slack with no waitlist on the current site. Supermemory publishes one price ladder, without scoping any tier to a specific half: a free developer tier, Pro at $19/month, Max at $100/month, and Scale at $399/month, which adds the full connector set with SOC 2 and HIPAA BAA coverage and a self-hosted option, then Enterprise by negotiation.
Those halves fail differently, so they have different replacements. We build Modem, the first entry below, and it competes with one half only.
“we need SSO before rollout”
“any update on single sign-on?”
“SSO came up twice on this call”
Linear issue, quotes attached
insight report in NotionThe short version
| Tool | What it is | Which half it replaces |
|---|---|---|
| Modem | Customer context graph fed by support, chat, and calls | Company Brain, for customer questions |
| Zep | Temporal knowledge graph that retires stale facts | The API, when correctness outranks speed |
| mem0 | Per-user memory API, Apache 2.0 core | The API, nothing else |
| Glean | Enterprise search and agents across 275+ work apps | Company Brain, at company scale |
| Letta | Runtime for agents that hold their own state | Neither; memory moves inside the agent |
| Vector database | The retrieval primitive underneath | The API, at the primitive level |
1. Modem — the customer half of the problem
Company Brain reads what your team wrote down. Modem reads what your customers said, across Slack, Discord, Zendesk, Intercom, Gong, and email, and maintains a customer context graph linking topics to the people and companies behind them with the original quotes attached. Teams reach it over MCP (claude mcp add --transport http modem https://mcp.modem.dev/mcp), through the @modem-dev/cli package, or by asking Modem in Slack. Paid plans start at $80/month with unlimited users on every tier and pay-as-you-go beyond the included usage, alongside a free tier.
Supermemory wins parts of the Slack overlap outright, and they're worth naming. Company Brain volunteers in threads; Modem replies to a DM or an @mention, and its unprompted work arrives as automations and PR-merge follow-ups. Supermemory's connectors also index Drive files, Notion pages, and Gmail, none of which Modem ingests, and its core self-hosts where Modem is SaaS only. What Modem has is a typed model over the sources those connectors miss, so "the top ten complaints this quarter, with the accounts attached" is a query against structured topics, not a similarity search, and closing the loop works because the graph already knows who asked.
Where it fits: teams whose agents give vague answers about customers because the specifics sit unread in Zendesk threads and call recordings. Where it doesn't: Modem holds no documents and no agent state. If the questions you need answered are "what did we decide about pricing last quarter" or "why is this file written this way," that's retrieval over your own team's material, and Supermemory or Glean is the better buy.
2. Zep
Retrieval benchmarks score whether a system finds the right passage. They say nothing about whether that passage is still true, and a confidently recalled stale fact is the more expensive failure. Zep is built around that gap. Its Graphiti engine timestamps each fact and retires it once the source moves on, so an answer carries an as-of date instead of whatever got indexed first. SOC 2 and HIPAA coverage come with the hosted tier.
Where it fits: platform teams that need governance and have the capacity to build the store. Where it doesn't: no Slack teammate ships with it, so if Company Brain was your daily surface, Zep replaces the wrong half.
3. mem0
mem0 is the plainer version of Supermemory's first half, memory infrastructure you call from your own application, Apache 2.0 at the core and among the most widely adopted options in the category. Moving here from Supermemory's API, the trade is concrete. You give up the managed connector set, the single-binary self-hosting story, and the hybrid keyword-plus-vector retrieval Supermemory benchmarks on. You gain a longer track record, more integrations already built, and a bill with no team-agent tier priced into it.
Where it fits: developers putting per-user memory inside a product they ship. Where it doesn't: it stores what you send under the key you choose, so anything that stitches one customer's identity across Zendesk, Slack, and a sales call is code you write and maintain.
4. Glean
Glean connects to 275-plus work applications by its own count, Slack, Drive, Jira, Confluence, SharePoint, GitHub, and Salesforce among them, and lets any department search that index or build agents on it. Company Brain starts inside a Slack workspace and reaches out; Glean starts from everything the company has written down and treats Slack as one source among many.
Where it fits: organizations rolling one search-and-agent layer across every department. Where it doesn't: it's built for company-wide deployment, so a ten-person team that wanted a sharp Slack bot ends up buying an indexing program it won't use.
5. Letta
Every other entry here stores memory belonging to a person, a team, or a customer base. Letta is the one where it belongs to the agent itself. Out of the MemGPT research line, it's a runtime where the agent decides what to page into its own working context and keeps that state across restarts, with an Apache 2.0 core. It replaces neither half of Supermemory. Teams shopping "memory" sometimes find this was the shape they wanted all along.
Where it fits: agents that run for months and have to remember being themselves. Where it doesn't: it isn't a shared store other tools query, so nothing else in your stack benefits from what one agent learned.
6. A vector database you run yourself
Underneath Supermemory's first half, and mem0's and Zep's, sits a vector database. Pinecone or Weaviate plus an embedding pipeline you write gets you the primitive directly, trading a subscription for engineering time. It replaces the API and nothing above it, so Company Brain's Slack surface would still be yours to build.
Where it fits: teams with a platform engineer to spare and a latency, cost, or residency constraint the vendors can't meet. Where it doesn't: how you chunk a document, what you extract from it, and how results get ranked all become permanently yours to maintain, which is most of what the managed layer was charging for.
How to choose
Answer the half question first, because it decides the shortlist.
If Company Brain was what you used, only two entries here put an agent in Slack at all, and they sit at opposite scales. Modem covers customer signal for one team; Glean serves every department across the company. If the API was what you used, pick on failure mode instead. mem0 has the longest track record, Zep guards against stale facts, and owning the vector layer answers a hard latency or residency constraint. Letta sits outside the split, because choosing it moves memory inside the agent.
One mismatch is worth guarding against, because better retrieval can't fix it. A team evaluates on recall benchmarks, buys well, then finds the question it needed answered was which customers are asking for something and what those accounts are worth. No index returns that from sources that never held it. That material lives in tickets and calls, which is what a customer context graph is built to hold.
