AI · alternatives · 2026
24 alternatives to MemoryOS
AI agent memory with knowledge graph and 78ms retrieval
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
- 1
DeltaMemory▲104Most AI agents forget everything between sessions. We built DeltaMemory to fix that. It's a cognitive memory layer that gives agents persistent recall, automatic fact extraction, and temporal reasoning — via a single SDK call. Key numbers: - 89% accuracy on LoCoMo long-term conversation benchmark - 50ms p50 retrieval latency - 97% cost reduction vs raw token re-processing Open source SDKs, works with any LLM stack. Currently in early access. Happy to answer questions about the architecture, benchmark methodology, or how we handle knowledge graphs.
Feb 2026 · deltamemory.com · its alternatives →
- 2
Memori▲168Persistent memory from agent trace, not just conversation
May 2026 · memorilabs.ai · its alternatives →
- 3
Agentmemory▲310Persistent memory for Claude Code, Codex & coding agents
May 2026 · agent-memory.dev · its alternatives →
- 4
Query Memory▲100One API for all documents your AI agents need
Mar 2026 · querymemory.com · its alternatives →
- 5
Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual…
2025 · github.com · its alternatives →
- 6
YourMemory▲93This is a project that I have been building for a while now, YourMemory is a solution to agentic memory which focuses on pruning of noise rather than hoarding of data. In the current state of agentic memory most of the context is stored in the form of a MD file or is derived through a RAG model where you store each and everything. Both of the solution leads to bloated context which does not optimize the usage of any tokens. In this system we only keep relevant data in our memory and prune all the unnecessary data. The relevance of a data is derived through multiple factors such as recall…
Apr 2026 · yourmemoryai.vercel.app · its alternatives →
- 7
MemoryOS▲3Hybrid AI Memory: Vector RAG meets Knowledge Graphs
Apr 2026 · github.com · its alternatives →
- 8
Eggshell▲74Local memory for AI agents to reuse work, spend fewer tokens
Sep 2026 · github.com · its alternatives →
- 9
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 →
- 10
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 →
- 11
Make0 Brain▲82A memory store that works across all of your AI agents.
13d ago · make0.ai · its alternatives →
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- 13
Hey HN! I'm Arindam, part of the team behind Memori (https://memori.gibsonai.com/). Memori adds a stateful memory engine to AI agents, enabling them to stay consistent, recall past work, and improve over time. With Memori, agents don’t lose track of multi-step workflows, repeat tool calls, or forget user preferences. Instead, they build up human-like memory that makes them more reliable and efficient across sessions. We’ve also put together demo apps (a personal diary assistant, a research agent, and a travel planner) so you can see memory in action. Current LLMs are stateless…
2025 · github.com · its alternatives →
- 14
Give AI agents long-term memory. Not vector search or RAG
Dec 2025 · github.com · its alternatives →
- 15
Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai · its alternatives →
- 16
MemoClaw▲6Hey HN, I built MemoClaw, a simple memory service for AI agents. Store and recall memories with semantic search. No registration, no API keys, your wallet address is your identity. How it works: - POST /v1/store → save a memory ($0.001) - POST /v1/recall → semantic search ($0.001) - Payment via x402 protocol (USDC on Base) Why I built this: Every agent framework has its own memory solution, but they're all local or require complex setup. I wanted something like S3 for memories – just call an API and pay for what you use. CLI: `npm install -g memoclaw` Docs:…
Feb 2026 · memoclaw.com · its alternatives →
- 17
Semantic search alone is not enough to capture all connected facts, they will capture the semantically most identical memory only. Tested on HotpotQA public dataset: Vector + BM25 + entity graph: BothFound@5 71.5% Vector + BM25 only: BothFound@5 59.5% Entity graph is the game changer to extract connected facts. More Benchmark result: LongMemEval-S: 84.8% recallAll@5 LoCoMo-10: 59% vs zep cloud 28% What is your approach for connected facts retrieval ?
Jul 2026 · yourmemoryai.xyz · its alternatives →
- 18
Tell MemorySync a fact, reload the page, then ask for it back in your own words. No signup. Every API call shown in cURL, Python and Node.js.
10d ago · memorysync.io · its alternatives →
- 19
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 →
- 20IM
Store memories, auto-extract entities and relationships, search semantically. MCP server + REST API + SDKs. Self-hostable, cloud option, MIT license.
May 2026 · agentrecall.cloud · its alternatives →
- 21
Memory + judgment for AI coding agents (local, MIT)
May 2026 · projectmem.dev · its alternatives →
- 22
ViBo▲1Memory for AI agents — never costs more than no memory
Aug 2026 · wwwvibo.com · its alternatives →
- 23
Tired of AI coding tools that forget everything between sessions? Every time I open a new chat with Claude or fire up Copilot, I'm back to square one explaining my codebase structure. So I built something to fix this. It's called In Memoria. Its an MCP server that gives AI tools persistent memory. Instead of starting fresh every conversation, the AI remembers your coding patterns, architectural decisions, and all the context you've built up. The setup is dead simple: `npx in-memoria server` then connect your AI tool. No accounts, no data leaves your machine. Under the hood it's TypeScript +…
2025 · github.com · its alternatives →
- 24
Hey HN! I built Memoripy, a memory layer for AI that adds short-term, long-term, and semantic memory capabilities to enhance LLM applications. It helps AI systems retain and prioritize past interactions, adapt over time, and respond with greater context and personalization. Memoripy uses semantic clustering to retrieve relevant memories, along with adaptive memory decay and reinforcement, so interactions stay fresh and context-aware. It’s designed for easy integration with OpenAI, Ollama, and other platforms—giving your AI applications dynamic memory management with minimal setup. Would love…
2024 · github.com · its alternatives →
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