The short answer
As of September 22, 2026. Products change; we re-check this page regularly.
Mem0 is an open-source memory library under the Apache 2.0 license, with a hosted, closed-source Platform on top. It stores short memories written by a language model and finds them again by similarity. thinqOS is a hosted Mind that belongs to one identity, a person or an agent, and it is not open source. Mem0 is ahead on openness, self-hosting, choice of database and model, and the number of ready-made connections. thinqOS holds more about each belief: how confident it is, how much it still matters, where it came from and who may see it.
What each one is
We read Mem0's open-source code at the commit dated September 18, 2026. The Platform's server code is not public, so what we say about the Platform comes from Mem0's own documentation, and the table marks it that way.
In the open-source library, each add call asks a language model to write new memories, drops exact-text duplicates and stores the rest in a vector database. Only the last 10 messages of a session are kept as raw context. Search ranks by vector similarity, and adds keyword and entity signals when the optional fastembed and spaCy packages are installed. Mem0's documentation says the Platform also marks an older contradicting memory as superseded and merges duplicates on every add, offers opt-in ranking decay, and on the Pro plan writes pattern memories from recurring facts.
thinqOS stores beliefs as structured claims, each with its own confidence and a separate salience value. Goals, preferences, procedures and lessons are stored as their own record types. It keeps full conversations as well as the beliefs drawn from them.
| Mem0 open source | Mem0 Platform | thinqOS | |
|---|---|---|---|
| Open source, self-hostable | Yes, Apache 2.0 | No; on-premises on Enterprise (docs) | Not open source; self-hosting with an install token we issue |
| Choice of database and model | 25 vector stores, 18 model providers | Chosen by Mem0 (docs) | Seven model providers; one hosted database |
| Confidence on each memory | No | Not documented (docs) | Yes, blended with new evidence |
| Contradictions | Not stored | Older memory superseded on every add (docs) | Typed links; checked on confirm, state or resolve |
| Decay and forgetting | No | Opt-in ranking decay; nothing removed (docs) | Salience decays; low, unprotected beliefs archived |
| Consolidation | No | Merge on every add; pattern memories on Pro (docs) | Capped summary beliefs with source links |
| Keyword matching in memory search | Yes, with fastembed installed | Yes (docs) | Conversations yes; beliefs no |
| Who can see a memory | Filter by user, agent or run id | Adds orgs, projects and roles (docs) | Audience per belief; identity from sign-in |
| Raw conversation kept | Last 10 messages per session | Not documented (docs) | Full conversations, searchable |
| Export and MCP | You own the store; no export tool or MCP server | Export jobs; hosted MCP, 11 tools (docs) | Open-format export; MCP server |
Who should choose Mem0 instead
We would point you to Mem0 in these cases.
You need to run memory on your own hardware, including with local models, or you need an open-source license. thinqOS is not open source. It can be self-hosted from a signed release, with an install token we issue for each installation.
You are building your own product and want a memory component inside it, with your own choice of vector database and model. Mem0's code has adapters for 25 vector stores and 18 language model providers.
You work in LangChain, LlamaIndex, CrewAI, AutoGen, Mastra or a similar framework and want a documented connection today. Mem0's documentation has 37 framework guides.
You want memory inside ChatGPT, Gemini, Perplexity, Grok or DeepSeek in the browser. Mem0 has a browser extension for those sites that sends to its hosted Platform. thinqOS has no browser extension.
Your need is to remember facts about your users and fetch the relevant ones, and you want a free tier with published per-request pricing.
Where Mem0 is ahead
Openness. The library is Apache 2.0, you own the store, and you can read every prompt it sends to the model.
Breadth. Beyond the adapters and framework guides above, Mem0 ships plugins for about a dozen coding tools, and its documentation describes a hosted MCP server with 11 tools.
Contradictions on every write. Mem0's documentation says the Platform supersedes an older contradicting memory on every add, on all plans. thinqOS runs its contradiction check when a belief is confirmed, stated directly or resolved, not on every automatic extraction. On this point the Platform, as documented, covers more.
Exact words in search. Mem0 can blend keyword matching into memory search. thinqOS uses keyword and vector search together for conversations, but its belief retrieval does not use keyword matching.
Dates in extraction. Mem0's extraction prompt turns relative dates such as "yesterday" into calendar dates. We found no equivalent in thinqOS's extraction prompts.
Visible scoring. Mem0 search can return a breakdown of each result's score.
Published pricing. Mem0's pricing page, read September 22, 2026, lists Hobby free with 10,000 add and 1,000 retrieval requests a month, Starter at $19 a month, Pro at $249 a month and a custom Enterprise plan.
Mem0 reports benchmark scores including 92.5 on LoCoMo and 94.4 on LongMemEval. A public GitHub issue filed on September 5, 2026 asks which judge configuration produced the LoCoMo figure; we saw no maintainer reply. We have not run either benchmark, and thinqOS has no result on them.
Where thinqOS is ahead
Each point below is in thinqOS's code and running today.
Confidence per belief. Every belief carries a confidence value. When the same claim comes back, new evidence is blended with the old, and something you stated outranks something the system inferred. Mem0's open-source code has no confidence field, and its documentation describes none for the Platform.
Salience that changes. How much a belief still matters is tracked separately from how true it is. Salience rises when a belief is used and decays over time. Protected beliefs have a floor, and unprotected beliefs that fall low enough are archived, which takes them out of recall without deleting them. Mem0's documented decay changes ranking only, and nothing leaves.
Typed relations. Beliefs can be linked as supporting, contradicting or superseding each other. Mem0's documentation says its graph links entities by co-occurrence and does not assign typed relationships.
Consolidation that keeps the evidence. When three or more related beliefs recur, thinqOS can write a summary belief whose confidence is capped at 0.85 and at the average of its sources, with a link back to each source.
Who may see a belief. Each belief can carry its own audience, and recall takes the caller's identity from sign-in. In Mem0's self-hosted server, any valid key can read any user's memories, which suits one team but not many separate customers.
Deleting a source. When you delete or exclude a conversation, beliefs that came only from it are archived, and beliefs with other sources are recalculated.
Export. A Mind exports as a signed archive, JSON-LD, YAML-LD or a Markdown vault.
What is partial in thinqOS
Some parts of thinqOS are not finished, and they matter here. Choosing what reaches the model's context window is still being built. Goals are stored and used to rank recall, but pursuing goals is largely unbuilt. Learning from outcomes is partial. Enterprise controls such as single sign-on, role-based access, data residency and audit export are mostly not built. thinqOS connects to ChatGPT and Claude.ai, but how often a plain chat calls it without being asked has not been measured.
Choose thinqOS when
Choose thinqOS when beliefs change and you want one current, confidence-weighted view of each claim; when people and agents need separate views, with control over who sees what; or when the same Mind should follow you across Claude Code, Codex, VS Code and the chat tools it connects to.