Alternatives
Products that do what A lightweight, stateless database for agent memory does
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
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An OKF-backed Model Context Protocol (MCP) server delivering persistent long-term memory and SQLite FTS5 search for AI agents. - fellowgeek/mcp-memory
24d ago · github.com
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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
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An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience.
22d ago · pinglin.tw
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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
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So I've been building ClawMem, an open-source context engine that gives AI coding agents persistent memory across sessions. It works with Claude Code (hooks + MCP) and OpenClaw (ContextEngine plugin + REST API), and both can share the same SQLite vault, so your CLI agent and your voice/chat agent build on the same memory without syncing anything. The retrieval architecture is a Frankenstein, which is pretty much always my process. I pulled the best parts from recent projects and research and stitched them together: [QMD](https://github.com/tobi/qmd) for the…
Mar 2026 · github.com
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Hey HN, Matusa here! A friend and I have built Memora. Memora is a vector database with built-in multistage reranking, which can significantly improve search accuracy over semantic search. It also features a proprietary embedding model tailored for RAG use cases — where there's a structural mismatch between the content stored and the query used for searching (hence why HyDE works well). Memora started because we were working on a stealth AI startup where we used an agent that would query into a vector DB, but it would take multiple tries for the agent to find what it needed (20% of the time…
2023 · usememora.app
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May 2026 · github.com
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Gomaa — Autonomous Agent Memory OS. Persistent memory system for AI agents with Obsidian vault integration, hybrid RRF search, knowledge graphs, security gates, and MCP server. - M4F-S/gomaa
14d ago · github.com
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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
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Clawlet is a personal AI agent that ships as a single, self-contained binary. No runtime, no package manager, no external database. The main thing that sets it apart: built-in hybrid semantic memory search (vector similarity + full-text) using a bundled SQLite with vector extensions. The index is just a local .sqlite file — no separate vector DB to run. Drop the binary on any machine and memory search just works. GitHub: https://github.com/mosaxiv/clawlet
Feb 2026 · github.com
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hi HN, While building Wolbarg (an open-source shared memory SDK for AI agents), I assumed PostgreSQL would be the obvious choice for memory storage. After benchmarking SQLite under realistic agent workloads, I was surprised by the results. For local-first and single-node deployments, SQLite handled far more than I expected while keeping the architecture much simpler. I wrote up the benchmarks, methodology, trade-offs, and where I still think PostgreSQL is the better choice.
Jul 2026 · wolbarg.com
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It's an biological inspired decay system for our memories with extended support of temporal reasoning. Created a CLI command to infer knowledge from the context stored in memory system without any token utilization or llm call. It comes with a memory dashboard to monitor and manage your memories it can be extended as audit trail for agents as well !
May 2026
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Input a SMILES string (or pick one molecule from the examples) and it returns up to 100k molecules closest in 3-D shape or electrostatic similarity – from 10+ billion scale databases — typically in under 5-10 s. *Why it might interest HN* * Entire index lives on disk — no GPU at query-time, less than ~10 GB RAM total. * Built from scratch (no FAISS index / Milvus / Pinecone). * Index-build cost: one Nvidia T4 (~ 300USD) for one 5.5B database. * Open to anyone, predict ADMET, export results as CSV/SDF. Full write-up & benchmarks (DUD-E, LIT-PCBA, SVS) in the pre-print:…
2025 · cheese-new.deepmedchem.com
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Hi HN, I have been recently geeking on agentic memories, and I believe I finally came up with something that works. I spent the last couple of weeks building a memory[0] for Claude Code that dynamically evolve as you talk with it. I followed a new paradigm inspired from zettelkasten method. Whenever Claude discover something new in the codebase or during your conversation with it, it writes a note about it, link it with related memories, update the related memories accordingly, and store it in chromaDB (this part where it keeps self-evolving based on new inputs). Later when you ask it to…
Jan 2026 · github.com
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Store memories, auto-extract entities and relationships, search semantically. MCP server + REST API + SDKs. Self-hostable, cloud option, MIT license.
May 2026 · agentrecall.cloud
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