YourMemory 2.0
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 ?
YourMemory 2.0 launched on July 2, 2026 with 15 votes, #1,090 of 24,623 launches that month and more than 95% of that year's launches.
What it does
Most AI memory tools (Mem0, Zep, vector DBs) are storage with search, they hoard everything and bloat into noise. YourMemory behaves like a brain: 🧠 It consolidates related facts into clean summaries, so memory gets sharper, not bigger (444 → 16). ♻️ It recovers your agent's context after every reset. 🔒 It proves every change with a tamper-evident audit trail. 👥 It shares role-based team memory pools. Everyone else's store gets worse as it grows. Ours gets better.
YourMemory gives your AI assistant persistent memory that survives across sessions. 89.4% Recall@5 on LongMemEval. Works with Claude, Cursor, Cline and any MCP client. Open source, self-hostable.
Persistent, intelligent memory for any AI agent — with biologically-inspired decay that forgets the right things at the right time. Open source. Runs entirely on your machine. Three steps. Your agent learns, remembers, and naturally forgets what it no longer needs. Evaluated on three public datasets. Every number independently reproducible — benchmark code lives in the repo. Two ways to find out — a 30-second check inside your AI, or a full breakdown from your own history. Paste this at the end of any session — Claude, ChatGPT, Gemini, Cursor, Copilot . It needs no knowledge of YourMemory; your AI measures and compresses the conversation itself. Export any AI chat and paste it below.…from yourmemoryai.xyz
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DeltaMemoryFeb 2026 · deltamemory.com · ▲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.
YourMemoryApr 2026 · yourmemoryai.vercel.app · ▲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…
Core – open source memory graph for LLMs – shareable, user owned2025 · github.com · ▲112I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
AgentmemoryMay 2026 · agent-memory.dev · ▲310Persistent memory for Claude Code, Codex & coding agents
I replaced vector databases with Git for AI memory (PoC)2025 · github.com · ▲198Hey 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…
CogniMemo ExtensionJan 2026 · chromewebstore.google.com · ▲106AI memory tool that lives where you work
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