Hybrid search
Reciprocal Rank Fusion over pgvector cosine + Postgres full-text. Semantic recall that still nails exact terms.
Store, search and forget long-term memories over a single API. Hybrid vector + keyword search, multi-tenant, one line of code.
Get a key, store a memory, recall it semantically — that's the two calls below. When you need more, remember turns prose into evolving facts, add_many bulk-imports thousands at once, and ready-made connectors sync your existing tools. No schema, no infra — embeddings, hybrid search and isolation are handled for you.
from longmem import Longmem mem = Longmem(api_key="mv_…") # store a memory mem.add("User prefers dark mode", category="preference")
# recall it — semantically hits = mem.search("what theme?") print(hits[0]["text"]) # → "User prefers dark mode" (0.94)
Reciprocal Rank Fusion over pgvector cosine + Postgres full-text. Semantic recall that still nails exact terms.
Text in, vectors handled. OpenAI embeddings with a Redis cache so repeat content never costs twice.
API-key auth with hard tenant isolation and namespaced collections. One deployment, many agents.
Drop-in memory for agents over MCP, plus a one-line Python SDK. Recall and capture without glue code.
Delete by id or by semantic query. GDPR-friendly by design — memories leave when you say so.
Hosted in the EU by 11data, a German B2B data consultancy. Privacy-minded from the ground up.
Managed connectors sync Slack, Notion, Google Drive & Linear into memory server-side — connect by OAuth or token in the dashboard. Self-host recipes and a dlt destination cover any other source.
LongMem syncs your sources server-side, so every agent on the team draws from the same connected memory. Connect by OAuth or token in the dashboard — no pipeline to babysit.
Slack, Notion, Google Drive, Linear, Confluence, Gmail, and HubSpot pull in on their own and stay current — incremental, scoped to a collection, nothing to run.
Authorize with OAuth (Slack, Notion, Google Drive) or paste a token — right from the dashboard Connections panel. Credentials are encrypted at rest.
A self-host recipe for Postgres, plus a dlt destination and bulk import for any source you can script.
Voice notes, meeting recordings, PDFs, screenshots, plain files — drag them onto the drop zone (or record right in the browser). Audio gets transcribed, images get described, documents get read. Everything is searchable seconds later.
Supermemory charges $19/mo for comparable limits. LongMem Pro is $12/mo — same memory, 37% less.
| Feature | Supermemory | Mem.ai | LongMem |
|---|---|---|---|
| Price | $19/mo | $15/mo | $12/mo |
| Memories (Pro tier) | Unknown | Unknown | 50,000 |
| Hosted in Germany | |||
| Hybrid search (vector + FTS) | |||
| Self-hosting | |||
| MCP support | |||
| Open source |
Every LLM call starts from zero. Without memory, your agent re-asks what it already knew, forgets the user's preferences, and loses the thread between sessions. Bolting a vector store onto your app to fix that becomes its own project — chunking, embeddings, hybrid ranking, multi-tenancy, decay.
LongMem is that project, finished. One API to store, search, and forget long-term memories — hybrid vector + keyword search, graph relations, per-tenant isolation, in one line of code. pip install longmem-sdk, 1,000 memories free, no card.
It is open source and self-hostable: run it on our managed cloud or your own box, bring your own model and storage, and keep your data where it belongs. Built by 11data, a German B2B data consultancy, to European data-protection standards.
Years of experience building production data systems for German enterprises.
Built to German data-protection standards and hosted on Hetzner infrastructure in Germany.
Transparent development with open-source code and a public roadmap.