u/GabrielMartinMoran

147 post karma · 10 comment karma · joined Jul 2022

  1. 1points

    I spent months building persistent memory for AI agents. v1.7 just reached 86.67% recall accuracy without embeddings.

    0 comments · source

  2. 1points

    I spent months building persistent memory for AI agents. v1.7 just reached 86.67% recall accuracy without embeddings. (self.AIMemory) [Resource]

    0 comments

  3. 1points

    Do AI agents actually need embeddings for good long-term memory? My open-source project scored 86.67% without them. (self.AI_Agents) [Resource Request]

    0 comments

  4. 2points

    I spent months building persistent memory for AI agents. v1.7 just reached 86.67% recall accuracy without embeddings. [Project]

    1 comments · source

  5. 1points

    I spent months building persistent memory for AI agents. v1.7 just reached 86.67% recall accuracy without embeddings. (self.LovingAIAgents) [New Launch]

    1 comments

  6. 5points

    Your OpenCode agent shouldn't rediscover the same decisions every session — Mind v1.7 is here (self.opencode)

    3 comments

  7. 6points

    Mind v1.7 is out — better persistent memory for OpenCode, now with 86.67% recall benchmark accuracy (self.opencodeCLI)

    2 comments

  8. 2points

    I built an open-source memory for coding agents — v1.7 just scored 86.67% on OmniMemEval without embeddings (i.redd.it)

    0 comments · source

  9. 0points

    I built an open-source memory for coding agents — v1.7 just scored 86.67% on OmniMemEval without embeddings (i.redd.it)

    10 comments · source

  10. 1points

    I built an open-source persistent memory layer for Codex — Mind v1.7 is now available

    0 comments · source

  11. 0points

    I built an open-source persistent memory layer for Codex — Mind v1.7 is now available (self.codex) [Showcase]

    4 comments

  12. 1points

    Mind v1.7.0 — MIT-licensed agent memory reaches 86.67% on OmniMemEval without embeddings

    1 comments · source

  13. 2points

    Mind v1.7.0 — MIT-licensed agent memory reaches 86.67% on OmniMemEval without embeddings (self.GoodOpenSource)

    2 comments

  14. 1points

    Mind v1.6.0 — MIT-licensed persistent memory for AI agents, fully local and inspectable (i.redd.it) [Resource]

    0 comments · source

  15. 11points

    Mind v1.6.0 — MIT-licensed persistent memory for AI agents, fully local and inspectable (self.GoodOpenSource)

    1 comments

  16. 0points

    I made a local memory system for AI agents — now it can sync, recover and connect their intentions (i.redd.it)

    0 comments · source

  17. 1points

    A few months ago I gave my agents long-term memory. Here’s what happened after 20M tokens (i.redd.it) [Resource]

    0 comments · source

GabrielMartinMoran

🤣

GabrielMartinMoran

Good question! I wouldn't say Mind is universally better than OpenViking. There's some overlap, but I think the main difference is in how they approach memory.

OpenViking focuses on organizing and retrieving broader agent context, while Mind focuses on how agents build, connect, maintain, and reuse knowledge over time.

A few things that make Mind interesting:

  • Connected memories: Memories aren't just isolated entries. They can have directional, meaningful relationships (depends_on, caused_by, implements, contradicts, etc.). When an agent retrieves a memory, it can also see its directly connected knowledge and understand why something happened, not just what happened.
  • Memory proximity and relevance: Mind uses three memory tiers (hot, warm, cold), inspired by cache hierarchies. Frequently used memories move closer to the active working set, while older knowledge remains searchable. This helps agents distinguish current context from historical information.
  • Multi-strategy retrieval: Full-text, phrase, substring, fuzzy, and optional semantic search work together to find relevant memories even when the query doesn't match the original wording exactly. Embeddings aren't required.
  • Knowledge that evolves: Agents can update memories, explicitly supersede outdated decisions, and maintain living references without losing the reasoning and history behind previous choices.
  • Cross-session continuity: Checkpoints, session summaries, and a shared memory-management protocol help agents resume work and carry important context across sessions and tools.

The distinction I'm aiming for is that Mind isn't just about storing and finding information. It's about maintaining a useful, interconnected body of knowledge that evolves as the agent works.

For example, if an agent fixed a bug three weeks ago, I don't just want it to retrieve the fix. I want it to recover the root cause, the architectural decision behind it, related discoveries, and whether that decision is still valid.

If your primary need is managing a broad collection of documents and resources, OpenViking might be a better fit. If you're looking for persistent, evolving knowledge across coding sessions, that's the problem Mind is specifically designed around.

Both are interesting approaches, and I'd love to explore a proper comparison eventually!

GabrielMartinMoran

That's a very fair question!

For smaller projects, AGENTS.md and a few well-maintained Markdown files can absolutely be enough. I'm not trying to replace those.

The main difference is how knowledge is accumulated, maintained, and retrieved over time.

With Markdown files, you generally need to decide what to document, keep those files updated, and make sure the agent reads the relevant parts.

Mind adds a structured memory layer on top of that:

  • Selective retrieval: Instead of loading large files into context, agents can search for relevant memories on demand through MCP.
  • Memory lifecycle: Memories have tiers, relationships, and mechanisms for updating or superseding outdated knowledge.
  • Session continuity: Checkpoints and summaries help preserve unfinished work and relevant decisions.
  • Cross-agent memory: Codex, OpenCode, Claude Code, and other supported agents can access the same local SQLite store.
  • Search: Full-text, fuzzy, and other retrieval methods work locally without requiring embeddings.

The idea isn't to replace your codebase or documentation as the source of truth. It's to preserve the knowledge acquired while working on them — decisions, failed approaches, debugging discoveries, and context that doesn't necessarily belong in permanent project documentation.

I'd say Mind becomes more interesting as projects grow, sessions accumulate, and you start repeatedly rediscovering things.

GabrielMartinMoran

For anyone interested in contributing: Mind is MIT-licensed, built with TypeScript/Bun and SQLite, and has a documented contribution workflow.

Useful areas include retrieval quality, integration testing, agent compatibility, accessibility, documentation, and benchmark improvements.

Contributing guide: https://github.com/GabrielMartinMoran/mind/blob/main/CONTRIBUTING.md

Technical reviews and small, well-tested PRs are very welcome.

GabrielMartinMoran

Well, that's another cool public project I created and maintain: https://github.com/GabrielMartinMoran/nova-agent-squad

There is no specific setup for mind in this harness, it just uses mind based on the instructions and the MCP.

GabrielMartinMoran

Thank you for giving it a try! I use it everyday, and it became a tool that is there for saving your back even if you forget about its existence. Just set-up and go...

GabrielMartinMoran

There are definitely a lot of agent-memory projects now, so the skepticism is understandable.

That said, Mind isn’t a vibe-coded weekend demo or a reaction to the current trend. It has been under active development since early 2025 and has gone through many releases since then.

It has its own persistence and retrieval model, migrations, tests, integrations, file synchronization, compaction recovery, and measurements from real multi-project usage.

Concrete criticism or comparisons are completely fair. Dismissing it without looking at the implementation isn’t really a technical critique, though.

GabrielMartinMoran

Not really. Mind tracks decisions, learnings, working and past progress to use that in future iterations, across different agents and to recover from compaction. It doesn't try to index your codebase, just to be a persistent brain (with lookup strategies) for your agents.