Preview. These maps are our reading of public sources and haven't been checked with the projects' maintainers yet.
Memori
SQL-native memory layer that wraps your LLM client and extracts facts, preferences and agent traces into structured memory, via Memori Cloud or your own database (BYODB).
memorilabs.ai/Active
memori 3.3.6 on PyPI 2026-05-28; commits through 2026-09-18 (release cadence slowed). Repo moved from GibsonAI/memori to MemoriLabs/Memori. (checked 2026-10-25)
In Agent memory
Where it stands apart
- Can it run entirely on your own device or infrastructure, without the maker's servers? partly (some features need the maker's servers) (14% of 21 others)
- Which shared standard or format does it mainly build on? a SQL database (Postgres, SQLite) (14% of 21 others)
- What is the main storage model for long-term memory? structured records: profiles, tiers, or tables (15% of 20 others)
What it shares with most of the community
- Is it actively maintained? active (67% of 21 others)
- How is the main thing licensed? permissive open source (MIT/Apache/BSD) (67% of 21 others)
- How is it mainly sustained? company with a paid product, tier, or ads (75% of 20 others)
- Who is it for? developers (76% of 21 others)
- In the default setup, who mainly decides what gets remembered? the system, by extracting memories from conversations or data in the background (67% of 21 others)
- How can the person the memories are about see and edit them? only through developer APIs or a developer dashboard (62% of 21 others)
- Does it work with models from different LLM vendors? yes, model-agnostic (86% of 21 others)
- Is there a first-party MCP server for it? yes (67% of 21 others)
- How is memory separated between users or tenants? built-in scoping by user, agent, session, or tenant IDs (62% of 21 others)
- Does it publish scores on public memory benchmarks (LoCoMo, LongMemEval, BEAM, etc.)? yes (62% of 21 others)
Not documented
The public sources we read don't say:
- By default, how do old or wrong memories mainly go away?
Nearest neighbors
- Honcho 81% of 16 shared answers match
- Mem0 81% of 16 shared answers match
- Cognee 75% of 16 shared answers match
Every answer
| Question | Answer | Source |
|---|---|---|
| Is it actively maintained? | active | source 2026-10-25 |
| How is the main thing licensed? | permissive open source (MIT/Apache/BSD) Apache-2.0 | source 2026-10-25 |
| How is it mainly sustained? | company with a paid product, tier, or ads Memori Cloud and Enterprise. | source 2026-10-25 |
| Can it run entirely on your own device or infrastructure, without the maker's servers? | stands apart partly (some features need the maker's servers) BYODB keeps data in your DB, but BYODB docs still use Memori API keys from app.memorilabs.ai; unclear whether augmentation runs fully locally. | source 2026-10-25 |
| Do you need an account to use it? | optional account Hosted augmentation works without an account (rate-limited); sign-up raises limits. | source 2026-10-26 |
| Who is it for? | developers | source 2026-10-25 |
| Which shared standard or format does it mainly build on? | stands apart a SQL database (Postgres, SQLite) 'SQL-native', bring your own database. | source 2026-10-26 |
| In the default setup, who mainly decides what gets remembered? | the system, by extracting memories from conversations or data in the background | source 2026-10-25 |
| How can the person the memories are about see and edit them? | only through developer APIs or a developer dashboard | source 2026-10-25 |
| By default, how do old or wrong memories mainly go away? | Not documented No documented decay or supersession policy found. | source 2026-10-25 |
| Where do memories live in the default setup the docs lead with? | in the vendor's cloud README leads with Memori Cloud; BYODB is the alternative. | source 2026-10-25 |
| Does it work with models from different LLM vendors? | yes, model-agnostic | source 2026-10-25 |
| What is the main storage model for long-term memory? | stands apart structured records: profiles, tiers, or tables Structured records and triples in SQL databases. | source 2026-10-25 |
| What shape does it mainly take (the one its docs lead with)? | library or API primitive you build into your app | source 2026-10-25 |
| Is there a first-party MCP server for it? | yes | source 2026-10-25 |
| How is memory separated between users or tenants? | built-in scoping by user, agent, session, or tenant IDs entity_id / process_id attribution. | source 2026-10-25 |
| Does it publish scores on public memory benchmarks (LoCoMo, LongMemEval, BEAM, etc.)? | yes LoCoMo 87%. | source 2026-10-25 |
Sources
This page was derived from:
- GitHub · github.com/MemoriLabs/Memori · read 2026-10-25
- GitHub · github.com/MemoriLabs/Memori/tree/main/docs/memori-byodb · read 2026-10-25
- Web page · memorilabs.ai/ · read 2026-10-25
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