AI · alternatives · 2026
24 alternatives to AI memory with biological decay (52% recall)
Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning. This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned. To solve the…
AI Memory with Biological Decay is a retrieval system that manages agent memory using the Ebbinghaus forgetting curve instead of static storage. Memories receive strength scores that increase with use and decrease over… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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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 →
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Agentmemory▲310Persistent memory for Claude Code, Codex & coding agents
May 2026 · agent-memory.dev · its alternatives →
- 3RG
Hey HN! I'm José, and I built Recall to solve a problem that was driving me crazy. The Problem: I use Claude for coding daily, but every conversation starts from scratch. I'd explain my architecture, coding standards, past decisions... then hit the context limit and lose everything. Next session? Start over. The Solution: Recall is an MCP (Model Context Protocol) server that gives Claude persistent memory using Redis + semantic search. Think of it as long-term memory that survives context limits and session restarts. How it works: - Claude stores important context as "memories" during…
2025 · npmjs.com · its alternatives →
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Persistent memory for Claude, ChatGPT & Cursor. Free.
May 2026 · github.com · its alternatives →
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Memori▲168Persistent memory from agent trace, not just conversation
May 2026 · memorilabs.ai · its alternatives →
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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 →
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Query Memory▲100One API for all documents your AI agents need
Mar 2026 · querymemory.com · its alternatives →
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Stop wasting tokens and re-explaining your project every session. Recall gives Claude Code durable memory — entirely offline. - raiyanyahya/recall
Jun 2026 · github.com · its alternatives →
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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 →
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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.
Aug 2026 · pinglin.tw · its alternatives →
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Lossless long-term memory for a personal AI: never summarize, keep every line, put a timestamp on everything. - aru-labs/lossless-memory
Sep 2026 · github.com · its alternatives →
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Hi HN, There’s been a lot of discussion lately around context graphs, decision traces, and how AI systems reason. One thing we kept running into: when AI agents make real decisions, the why behind those decisions often disappears. The context is scattered across prompts, tools, policies, and approvals. Logs show what happened, but not why it was allowed. TraceMem is an attempt to make decision context durable. It records the reasoning, authority, and context behind AI actions as a system of record, not as monitoring data, but as memory. Happy to share more details or answer questions. - Tommi
Jan 2026 · tracemem.com · its alternatives →
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Give AI agents long-term memory. Not vector search or RAG
Dec 2025 · github.com · its alternatives →
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Open Source Context Infrastructure for AI Agents
May 2026 · ravbyte-ai.github.io · its alternatives →
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Robot Memory - Persistent memory system for AI robots. MCP Server + hybrid search + spatial retrieval. - robotmem/robotmem
Mar 2026 · github.com · its alternatives →
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Long-term memory for AI coding agents (Claude Code, Codex, opencode) as a tree of plain files - gonzaroman/IHMT-MEMORY
11d ago · github.com · its alternatives →
- 21MC
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 · its alternatives →
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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 →
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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 →
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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 →
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