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Dev tools · alternatives · 2026

24 alternatives to CortexDB

Single-file AI memory and knowledge graph for agents

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier.

  1. 1
    cognee▲382

    Memory for AI Agents in 5 lines of code

    2025 · cognee.ai · its alternatives →

  2. 2

    Hey HN, we want to share HelixDB (https://github.com/HelixDB/helix-db/), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://screen.studio/share/szgQu3yq. Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g.…

    2025 · github.com · its alternatives →

  3. 3
    Cortexfree▲306

    Turn API specs into docs, SDKs, and MCP servers

    Sep 2026 · cortexdocs.dev · its alternatives →

  4. 4

    We have been using graph DBs more and more at work. I found them painful to work with locally and decided to try and build something better.

    Aug 2026 · github.com · its alternatives →

  5. 5

    One API for all documents your AI agents need

    Mar 2026 · querymemory.com · its alternatives →

  6. 6
    Graphiti▲307

    Build personalized AI agents that learn from dynamic data

    2025 · github.com · its alternatives →

  7. 7
    PMB▲211

    PMB gives Claude Code, Cursor, Codex and Zed a real memory. Decisions, lessons and facts in one SQLite file on your disk. Offline, no API keys, no cloud.

    Jun 2026 · pmbai.dev · its alternatives →

  8. 8

    Memory infrastructure for AI coding agents

    Feb 2026 · askaibase.com · its alternatives →

  9. 9

    An OKF-backed Model Context Protocol (MCP) server delivering persistent long-term memory and SQLite FTS5 search for AI agents. - fellowgeek/mcp-memory

    Aug 2026 · github.com · its alternatives →

  10. 10
    pumaDB▲159

    a small hosted memory layer for AI agents

    Jun 2026 · pumadb.ai · its alternatives →

  11. 11

    Markdown based AI agent memory with semantic search, hybrid retrieval, and offline-first sync between agents. - sqliteai/sqlite-memory

    Apr 2026 · github.com · its alternatives →

  12. 12
    Cortex▲70

    Run multiple claude-code agents from YAML config

    Jan 2026 · cortex-cli.vercel.app · its alternatives →

  13. 13
    SemaDB▲109

    No fuss vector database for AI

    2023 · semafind.com · its alternatives →

  14. 14

    An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience.

    Aug 2026 · pinglin.tw · its alternatives →

  15. 15

    The memory layer for AI agents

    Jul 2026 · kitforai.com · its alternatives →

  16. 16

    Eliminate bad data before it becomes the wrong answer. Next-generation knowledge and governance platform for accurate GenAI answers.

    2017 · shelf.io · its alternatives →

  17. 17

    I've been working on Polign and built a small prototype around something I've been thinking about with agent memory. I have built a lightweight/stateless vector db + BM25 search which works really well with typed facts and structured queries. It uses your own S3, or GCS bucket as primary storage, and restarting a node is fairly quick. Demo + writeup: https://polign.com/blog-edge-agent-memory Live search demo: https://demo.polign.com Docs: https://polign.com

    Aug 2026 · polign.com · its alternatives →

  18. 18

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

    Feb 2026 · memories.sh · its alternatives →

  19. 19

    Local-first AI memory layer for any LLM. Persistent knowledge graph, entity extraction, semantic retrieval. Works with Ollama, OpenAI, Anthropic, or any OpenAI-compatible backend. - zaydmulani09/mnemo

    Jun 2026 · github.com · its alternatives →

  20. 20

    Vector databases store memories. They don't manage them. After 10k memories, recall quality degrades because there's no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier. YantrikDB is a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores: consolidation collapses duplicate memories, contradiction detection flags incompatible facts, temporal decay with configurable half-life lets unimportant memories fade like human memory does. Single Rust binary. HTTP + binary wire protocol. 2-voter + 1-witness HA…

    Apr 2026 · github.com · its alternatives →

  21. 21

    Local persistent memory store for LLM applications including claude desktop, github copilot, codex, antigravity, etc. - CaviraOSS/LongMemory

    Dec 2025 · github.com · its alternatives →

  22. 22

    An embeddable cross-platform AI agent library written in C. Cloud and local LLMs, tool calling, long-term memory, voice, sessions, research mode, self-evolving loops. The SQLite of agent frameworks: small, portable, just works. - sqliteai/adam

    May 2026 · github.com · its alternatives →

  23. 23

    Hi HN — I’m Prateek Rao. My cofounders and I built Cortexa, which we describe as a Bloomberg terminal for agentic memory. A pattern I keep seeing: when agents misbehave, most teams iterate on prompts and then “fix” it by plugging in a memory layer (vector DB + RAG). That helps sometimes — but it doesn’t guarantee correctness. In practice it often introduces a new failure mode: the agent retrieves something dubious, writes it back to memory as if it’s truth, and that mistake becomes sticky. Over time you get memory pollution, circular hallucination loops, and debugging turns into log…

    Mar 2026 · cortexa.ink · its alternatives →

  24. 24

    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 · its alternatives →

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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →