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Basic Memory

Basic Memory

Technology, Information and Internet

Austin , TX 35 followers

Build knowledge that grows with you Transform AI conversations into interconnected knowledge.

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About us

Basic Memory builds memory infrastructure for human-AI collaboration. We help organizations and individuals transform ephemeral AI conversations into persistent, owned knowledge that compounds over time. Our open-source, privacy-first platform integrates with any AI tool - Claude, ChatGPT, local models - while keeping your data under your control. Our approach: Work with AI, not for it. See into the black box. Own your intelligence. We're not building another corporate AI platform. We're building the memory layer that works with existing AI tools, giving you genuine ownership of your insights and conversations. Start free with our FOSS version, add additional cloud features to enable access from anywhere. Services: - Product: Local-first AI memory infrastructure - AI-Enhanced Software Engineering: Persistent context for complex codebases, architectural decisions that stick, and technical knowledge that compounds across projects - Consulting: Help organizations implement AI workflows that persist knowledge - Custom integrations: Build AI memory into existing systems - Strategy: Navigate AI tools without vendor lock-in We're not talking about "vibe coding" - we're talking about AI that remembers your entire technical context, understands your codebase evolution, and helps you build better software by learning from every decision you've made. Your knowledge belongs to you. AI should amplify human agency, not replace it. Simple tools that compose into powerful capabilities you can understand and control. Your memory travels with you across platforms and providers. Memory is too important to forget.

Website
https://basicmemory.com
Industry
Technology, Information and Internet
Company size
2-10 employees
Headquarters
Austin , TX
Type
Privately Held
Founded
2025

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  • Basic Memory reposted this

    View profile for Drew Cain
    Drew Cain

    I think we finally figured out who Basic Memory is for, and it's not who I expected. We built it for people like me. AI power users who want memory that survives across tools, sessions, and projects. Then an MSP showed up in our community and used it in a way I didn't see coming. Not for chat context. For client operations. Matching records, reviewing client history, catching things that fall through cracks. It clicked for me. MSPs live inside the exact problem we built for. Client knowledge scattered across tickets, tools, and people's heads. Every closed ticket is a small act of forgetting, and somebody pays to re-learn it later. But one user is a clue, not an answer. Before we build anything for MSPs, we need to actually understand how their world works. Which means talking to more of them. Over the next month I'm going to write about what I find, and about what we're building for MSPs, in public, including the parts that don't work. If you run an MSP, or know someone who does, let’s talk. I’d love hear how you handle this today.

  • View organization page for Basic Memory
    Basic Memory

    35 followers

    Basic Memory Cloud is now available in both the ChatGPT Plugin directory and Claude’s Connectors Directory. This makes setup much simpler: find Basic Memory Cloud in the directory, connect your account, and start using the same persistent knowledge base from either tool. No custom server URLs or developer settings required. Your notes, decisions, research, and project context can follow you between conversations, and between AI tools, instead of being trapped in one model. Setup guides: → ChatGPT: https://lnkd.in/g_GBPks9 → Claude: https://lnkd.in/gh_BJjyg It looks like a small integration update, but it represents something we care deeply about: your knowledge should belong to you and remain useful everywhere you work with AI. We’d love to hear what you build with it.

  • View organization page for Basic Memory
    Basic Memory

    35 followers

    Thanks to Tigris Data for featuring Basic Memory in a case study!

    View organization page for Tigris Data
    Tigris Data

    2,187 followers

    Most AI memory keeps your context locked to one platform. Switch tools and your notes don't come with you. Basic Memory takes the opposite approach. Your knowledge graph lives as plain Markdown files in your own Tigris bucket. Edit them in any text editor. Point Claude, ChatGPT, Cursor, or any MCP tool at the same files and your context is right there — no proprietary format, no exit tax. We talked with Paul Hernandez on how a four-person team built the cloud product: per-tenant buckets provisioned in 5–20 seconds via the Tigris Partner Integration API, bidirectional rclone sync that stays viable because of zero egress, and bucket snapshots as point-in-time restore for users' notes. The pattern is worth a look even if you're not building memory products. Markdown-as-objects, one bucket per tenant, MCP as the universal read/write API — it's a clean answer to "what does portable AI context actually look like?" Read the blog 👇

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  • View organization page for Basic Memory
    Basic Memory

    35 followers

    One of our users, Pelle Wessman presented how he used Basic Memory to claude code session into a knowledge graph via skills. Super cool!

    View profile for Jacob Langvad Nilsson
    Jacob Langvad Nilsson

    Slides from the first Malmö Claude Code meetup are ready. 91 pages from five practitioners running Claude Code in production. We covered opinionated tooling, personal knowledge management, enterprise-scale agentic engineering, legacy refactoring, and multi-agent systems. This community runs on sharing what we learn, so the full deck goes to everyone, not just the room. Five talks, in the order they were given: ▪️ Adam Tornhill (CodeScene). Legacy refactoring. Benchmarks showing Claude Code becomes 2-5x more impactful at structural refactoring when paired with deterministic Code Health feedback as a guardrail. ▪️ Pelle Wessman (040, Mocha, Claude for Open Source). Personal knowledge management. A custom plugin of 14 skills, 4 agents and 5 hooks that turns 20 years of bookmarks into a 1,400+ node knowledge graph that grows from every Claude session. ▪️ Olga Safonova (Tieto). Opinionated tooling. A working Claude Code stack across two machines. Custom Go MCP servers, automatic Dependabot triage, and the case for having strong opinions in your config. ▪️ Oskar Hane (Neo4j). Enterprise-scale agentic engineering. Strict specs as the new way of programming. Soft links for sharing skills across teams in a monorepo. PRD to tasks to loop, where features land at 90% before a human steps in. ▪️ Rickard Hansson (Gainable). Multi-agent systems. Twelve agents in a pipeline turning Excel into web apps. Hard-won lessons on model selection, prompt caching ($40 to $0.40), and why the answer to a drifting agent is rarely a better prompt. What struck me listening to them back to back: Not a single talk was about the model. Every talk was about the infrastructure around the model. Skills. MCPs. Knowledge graphs. Specs. Plugin marketplaces. Validators. Caching. The model itself was a given. The work was everywhere else. A second pattern, sharper than the first. Every speaker identified something the LLM should not be doing, and engineered around it. Adam moved code quality scoring out of the LLM into a deterministic Code Health metric, then fed that back as a guardrail. Rickard moved CRUD scaffolding out of the LLM into pure code, because the LLM kept getting it wrong and a generator never does. Oskar moved planning out of “ask the agent” into spec documents written and reviewed before any code runs. The pattern is the same. Take the brittle parts out of the agent. Surround what is left with structure. Which gets you to the line that closed Rickard's talk and could have closed any of them. When an agent keeps drifting, the fix is almost never a better prompt. It is more structure. Tighter context. A validator. A spec. A pipeline boundary. You could also see a clean progression across the five talks. Adam's organisational legacy code problem. Pelle's personal memory. Olga's individual stack. Oskar's team and monorepo. Rickard's full product built on agents. Different scales. Same lesson. Next Malmö meetup is in the works. Follow if you want to see it first.

  • Basic Memory reposted this

    View profile for Avido Studios
    Avido Studios

    I recently created a short product demo / promo video for Basic Memory. The goal was simple: explain the product clearly, show real value fast, and keep the visuals clean, no unnecessary fluff. I’m seeing more founders realize that how you explain your product matters as much as what you build. If you’re working on a SaaS or digital product and think a clear demo video like this could help, happy to chat, feel free to DM. #SaaS #Startups #ProductMarketing #Founders #BuildInPublic

  • View organization page for Basic Memory
    Basic Memory

    35 followers

    Unpopular opinion: ChatGPT's memory feature won't solve your problem. Here's why. The convenience of AI creates a seductive illusion: as long as you arrive at the final answer, the reasoning doesn't matter. But the reasoning is what matters when you return to the problem, when circumstances change, or when adjacent questions arise. The answer itself is brittle. A transcript is almost as useless. What you actually need is the equivalent of "comments in code"—context that explains the constraints, tradeoffs, and assumptions that shaped your solution. Without that, even your own work becomes a mystery. AI has become the primary site where thinking unfolds. The continuity of that thinking is now a genuine competitive advantage. Here's what we learned building this:

  • View organization page for Basic Memory
    Basic Memory

    35 followers

    Good engineers leave comments in code. Not as courtesy to teammates. As survival. You can look at your own code six months later and have no idea why it's structured that way. The code runs, sure. But the chain of reasoning has evaporated. Comments are where you document the constraints, the tradeoffs, the failed attempts. Without that context, even your own work becomes a mystery. Now everyone working with AI has the same problem. Your future self needs to know WHY it worked, not just that it worked. But there's no comment field in ChatGPT. We wrote about the solution here:

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