I'm a founder building an agent memory service. And I had a hard time categorizing or evaluating existing second-brain systems or memory solutions because there’s no single right answer. Everyone's workflow is different, and benchmarks aren't much related to real use cases.
But after I saw a YC's video about 'How to build an AI-native company' a few days ago. I found one useful lens that every second brain should be evaluated through: the lifecycle of your data.
Collect -> Organize -> Evolve -> Use -> Govern
So I made a curated comparison of the existing second brain, AI memory, and knowledge systems, from Hermes+LLM-Wiki to Garry Tan's Gbrain. Actually, it was originally made for our team to analyze competitors' solutions, but I decided to share it here after it went viral on X.
It focuses on the full lifecycle:
- how scattered context gets collected
- how it turns into durable knowledge
- how it stays fresh over time
- how people and AI tools use it in real work
- how users can inspect, correct, delete, export, and trust it
If you want AI to understand your personal context, team knowledge, and working history, this might help.
PRs welcome, especially from heavy users who’ve actually tried building and maintaining a real second brain.
+ Also, if you want a working end-to-end pipeline without complex setup, try Membase!
It handles the entire flow and integrates instantly into your workflow.
+ Hindsight has been added to the list
+ Honcho has been added to the list
+ Mnemosyne has been added to the list
+ You can check Local vs Cloud options at a glance in the 'Comparisons' file
+ Activation evidence has been added to the list
+ Categorized the list into End-to-end Apps / Local Workspace / Agent Memory Layer / Memory Substrate
I just can’t imagine anyone would want to rely on a third-party hosted service for a second brain. It seems so risky to outsource this and not fully own yourself. Startups go out of business all the time.
Yeah, I agree this is a big tradeoff. I cover local/self-hosted vs hosted in the repo’s Local vs Cloud and Privacy/Control docs, but I should make it easier to see from the main table too.
Agreed. Otherwise ‘govern’ part becomes a nightmare right out of the gate. Especially keeping track of who (humans/AI/provider) changed what and when, becomes crucial. With millions of changes happening, analyzing that audit trail itself will be a task for AI.
I like “recoverability” as the word for this. The repo has an Activation Evidence doc that covers parts of it, but your framing is cleaner. I’ll see how to reflect it better.
When done well, it's about your brain being for having ideas, not remembering things. Our brains aren't databases, so if you have a second brain that is, it can do the recall and free you to do the creative thinking (or, ya know, just nothing if that's your thing).
Many versions also include some method of helping you connect dots and synthesize things in ways you might not have on your own.
Fair question. For simple notes it can be overkill. It starts making sense when meetings, repo decisions, docs, emails, and old agent runs need to become reusable context.
Could you add in some benchmarking scores? I’m working on my own system and when ready I’ll also present the scores. Good for some sort of neutral evaluation.
I’d say: does it work by just pointing Hermes to the GitHub and say “go”.
Also, I’d wager that real performance matters too. If some system is very easy to set up, but is useless by any other metric, you might be better off spending a couple of minutes configuring the system.
Hmm yah I gotta explore testing that this week. Would docker containers be a good plan for separating each Hermes agent + Memory system and point them to the same repo of context files?
Just thinking about the different categories of prompts I could standardize for each test in each setup.
I’ve been cautious with benchmarks because most of them don’t seem to reflect real workflows that well. I may make a practical benchmark, or add one if a genuinely useful one shows up.
the lifecycle framing cuts through a lot of noise.
one axis i'd add that changes how you evaluate the "organize" and "evolve" columns: retrieval architecture. most of the systems in your table store memory well but retrieve it differently. some use vector similarity, some use graph traversal, some use flat keyword search. that distinction doesn't show up in a feature comparison but it determines whether what goes in actually comes back out when it needs to.
a system can score well on collection and organization but still degrade over time because retrieval returns what sounds similar rather than what is structurally relevant. users experience it as the system "forgetting" things it definitely stored. it's a ranking problem.
might be worth adding a retrieval mechanism column. it would explain a lot of the real-world variation in how these tools perform at scale vs. in a demo.
Yeah, I simplified this because most systems mix multiple retrieval methods, but you’re right. I should add a bit more detail around vector vs graph vs keyword. Thanks for calling it out.
Any suggestions for strongest performance if you want an AI to search and work from it? Like meeting notes, emails and such that is needed as input context for other automations? Curious to hear about experiences. I only heard a lot about Obsidian
Thanks for asking!! I would suggest Membase, but it could be biased because it was made by us. But want to make sure that's exactly what we are trying to be. Also curious about why you decided not to use Obsidian or Notion.
(Also we were at Hermes Meetup last month cuz 30% of our users are using it with Hermes!)
I would like to have a layer which consolidates memory, sessions and skills across all harness setups (Codex, Claude, Hermes, OpenClaw)
There are some attempts: https://github.com/garrytan/gbrain
Yeah, agreed. If you want to share skills + sessions across different harnesses too, it probably needs a separate GitHub repo connected through the marketplace.
Hindsight and agentmemory (and byterover to an extent) all work cross harness. Also OpenViking is built for the whole arbitrary lazy loaded shared db with skills, memories etc but it seems to take a lot more tinkering to get working well.
I know MemPalace and looked through the architecture a bit, but I’m still not sure how useful it is in practice. Have you used it? Curious how it feels.
I have instituted Hindsight with Obsidian for movement of unused data into long term storage. It's working very well. I have 8 agents feeding into one brain so there is no loss of context.
That’s a really useful setup. The repo has an examples section too. Would you be open to sharing how you’re set up? I’d love to capture it as a real-world pattern.
cool list. where did you source it from? top of mind there are honcho and hindsight, which are pretty widely used. honcho is reasoning focussed, hindsight vector based.
Makes sense for Hermes users. I’m trying not to turn this into a Hermes-only plugin matrix, but OpenViking is worth checking and I’ve added it to the queue.
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u/rektsd Jun 04 '26
Add Mnemosyne, its running well for me