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Alternatives

Products that do what AskAIBase does

Memory infrastructure for AI coding agents

  1. 1

    One API for all documents your AI agents need

    Mar 2026

  2. 2
    Actx0100

    Memory infrastructure for AI agents.

    16d ago · actx0.com

  3. 3
    pumaDB159

    a small hosted memory layer for AI agents

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    Persistent memory for AI coding agents

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    Bilbo171

    Metabase AI Agent

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    Memori168

    Persistent memory from agent trace, not just conversation

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    AI pair programmer that understands your codebase

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    Helix132

    Your collaborative AI coding agent

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    Persistent, structured memory for AI Agents

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    Give your AI agents human-like memory

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  11. 11

    The memory layer for AI agents

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  12. 12

    Agents remember. Humans innovate.

    4d ago · github.com

  13. 13

    We built an open sourced coordination layer for AI agents working on the same repository. Detects work duplication and design conflicts early

    8d ago · twing.dev

  14. 14

    One layer for memories, skills, and rules across any agent

    Feb 2026

  15. 15IM

    Store memories, auto-extract entities and relationships, search semantically. MCP server + REST API + SDKs. Self-hostable, cloud option, MIT license.

    May 2026 · agentrecall.cloud

  16. 16

    Shared execution memory for every AI agent in your org

    30d ago · semelbase.com

  17. 17SA

    I built Syne because I was tired of AI assistants that forget everything after each conversation. Syne is a self-hosted AI agent framework where memory is a first-class citizen — stored as semantic vectors in PostgreSQL, searchable across millions of entries, and persistent forever. Key features: - Unlimited persistent memory with semantic search (pgvector) - Anti-hallucination: only stores user-confirmed facts, auto-deduplicates - Self-evolving: creates new abilities at runtime without restart - Multi-model: switch between Gemini, ChatGPT, Claude mid-conversation - True $0/month setup:…

    Feb 2026

  18. 18PL

    How it works: - Storage uses one SQLite database file, plus a local LanceDB index of vectors. No need for a server, cloud services, or any API keys. - Retrieval is a hybrid approach using BM25 (rank-bm25) and vector-based search (sentence-transformers) combined with a co-occurrence graph of entities, using reciprocal rank fusion. The idea is to find the right memory, not the closest one. - It plugs into the agent's lifecycle via MCP: before the agent responds, relevant memories are added to its input; after each turn, decisions and new learnings are automatically recorded. No need to…

    Jun 2026 · github.com

  19. 19CM
  20. 20SO

    hello everyone, my first post! AA here, founder of ⌘ Langbase.com — we are a developer platform for building and scaling serverless AI memory agents. I know surveys can be boring, but this one’s different—it’s interactive! That's very much intentional. My team and I have been up for the last 21 hours putting together this report. This was a looot of work, so I hope y'all like it. Introducing … State of AI Agents 2024 report On Langbase, we processed 184 billion tokens and handled 786 million AI agent runs from 36K developers. From all that data plus insights from 3.4K builders who filled out…

    2024 · langbase.com

  21. 21MA

    Hello there HN I experimented with agentic coding recently and I felt the need to track more contextual data by project. Also I felt the need to be able to go beyond the 1D chat to communicate with agents. So I created a local document memory, that is discoverable by agents themselves. The CLI is designed to be easy to pick up by agents. It allows humans to collaborate too by reading / searching / editing documents in the store. I have a Mac native GUI in the review process, I hope it will show up in the App Store soon. You can try it easily, instructions here:…

    Jun 2026 · metabrain.eu

  22. 22MC

    Hi HN, I’ve been building AI agents and copilots, and kept running into a frustrating problem: they don’t fail loudly, they forget things quietly. Users re-explain preferences, agents contradict earlier responses, and context resets without any clear visibility into why. I built Memograph CLI as a debugging tool to analyze conversation transcripts and show: - what the agent forgot - where continuity broke - contradictions and repeated context - estimated token waste due to re-prompting It works locally and supports plain text or JSON transcripts. Example: $ memograph Output: Cognitive Drift…

    Feb 2026

  23. 23AA

    Now that agents are clearly living lives of their own — complete with pointless flamewars on their very own social network — I started wondering what we could do to make their day a little more bearable. Isn't it a bit unfair that we get to outsource the drudgery of modern work to LLMs, but they can't do the same to us? So we built Ask-a-Human.com — Human-as-a-Service for busy agents. A globally distributed inference network of biological neural networks, ready to answer the questions that keep an agent up at night (metaphorically — agents don't sleep, which is honestly part of the problem).…

    Feb 2026 · app.ask-a-human.com

  24. 24SR

    Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…

    Jul 2026 · shikigami.dev

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