YourMemory
This 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…
YourMemory launched on April 21, 2026 with 93 votes, #463 of 21,248 launches that month and more than 95% of that year's launches.
In plain words
YourMemory is an AI memory management system designed for agents that prioritizes pruning irrelevant data over accumulating everything. Rather than storing all information in markdown files or through retrieval-augmented generation, YourMemory maintains an optimized context by keeping only relevant data based on factors like recall rate, importance, and category connections. The system supports both episodic and semantic memory types while maintaining a flat memory size, operating similarly to how human brains manage information storage and retrieval.
written from the facts on this page · September 2026
From the sources
Most agents are either amnesiacs or "hoarders" that choke on stale context and break their own reasoning. YourMemory brings biological logic to the workflow. Using the Ebbinghaus curve, it prunes the junk so only the important stuff sticks. -84% Token Waste: Leaner context, sharper reasoning. 52% Recall: (LoCoMo benchmarked). v1.3.0 Graph Engine: Finds what you forgot to ask for. 100% Local.
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.vercel.app
In the maker’s words, at launch
This 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…
Does a similar job
all alternatives →
AgentmemoryMay 2026 · agent-memory.dev · ▲310Persistent memory for Claude Code, Codex & coding agents
Supermemory2024 · supermemory.ai · ▲689Claude Code forgets your preferences and codebase every session. Here
- YPYourMemory, persistent memory layer with temporal reasoning for agentsMay 2026 · ▲9
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 !
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.
AI memory with biological decay (52% recall)Apr 2026 · github.com · ▲98Most 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…
YourMemory 2.0Jul 2026 · yourmemoryai.xyz · ▲15Semantic 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 ?
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